From 53b704b666c37f73990f0a542243ed1c9bd60fdf Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Tue, 9 Aug 2022 17:17:02 +0800
Subject: [PATCH 1/6] Update MacOS_CI.yml

---
 .github/workflows/MacOS_CI.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index cdb3b4c7a..70db5de77 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -28,7 +28,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        # python -m pip install https://github.com/google/jax/archive/refs/tags/jax-v0.3.14.tar.gz
+        python -m pip install jax==0.3.14
+        python -m pip install jaxlib==0.3.14
         if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi
         python setup.py install
     - name: Lint with flake8

From 7787c57238f70d486dacdc956e70384dc1d4c58e Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Thu, 11 Aug 2022 10:33:08 +0800
Subject: [PATCH 2/6] fix: Tensor -> Array

---
 brainpy/algorithms/offline.py                 |   6 +-
 brainpy/analysis/highdim/slow_points.py       |  12 +-
 brainpy/dyn/channels/Ca.py                    | 184 +++++++-------
 brainpy/dyn/channels/IH.py                    |  28 +--
 brainpy/dyn/channels/K.py                     | 142 +++++------
 brainpy/dyn/channels/KCa.py                   |  14 +-
 brainpy/dyn/channels/Na.py                    |  58 ++---
 brainpy/dyn/channels/leaky.py                 |  10 +-
 brainpy/dyn/layers/linear.py                  |  14 +-
 brainpy/dyn/layers/reservoir.py               |  10 +-
 brainpy/dyn/layers/rnncells.py                |  28 +--
 brainpy/dyn/neurons/biological_models.py      | 124 +++++-----
 brainpy/dyn/neurons/fractional_models.py      |  46 ++--
 brainpy/dyn/neurons/input_groups.py           |   6 +-
 brainpy/dyn/neurons/noise_groups.py           |   8 +-
 brainpy/dyn/neurons/reduced_models.py         | 232 +++++++++---------
 brainpy/dyn/rates/populations.py              | 156 ++++++------
 brainpy/dyn/runners.py                        |  12 +-
 brainpy/dyn/synapses/abstract_models.py       |  46 ++--
 brainpy/dyn/synapses/biological_models.py     |  42 ++--
 brainpy/dyn/synapses/compat.py                |  66 ++---
 brainpy/dyn/synapses/delay_couplings.py       |  20 +-
 brainpy/dyn/synapses/gap_junction.py          |   6 +-
 brainpy/dyn/synapses/learning_rules.py        |  16 +-
 brainpy/dyn/synouts/conductances.py           |   4 +-
 brainpy/dyn/synouts/ions.py                   |  10 +-
 brainpy/dyn/synplast/short_term_plasticity.py |   8 +-
 brainpy/initialize/generic.py                 |   4 +-
 brainpy/integrators/fde/Caputo.py             |  10 +-
 brainpy/losses/comparison.py                  |  26 +-
 brainpy/math/delayvars.py                     |   2 +-
 brainpy/math/operators/pre2post.py            |  26 +-
 brainpy/math/operators/spikegrad.py           |  22 +-
 brainpy/tools/checking.py                     |   6 +-
 brainpy/train/back_propagation.py             |  12 +-
 brainpy/train/base.py                         |   8 +-
 brainpy/train/offline.py                      |  10 +-
 brainpy/train/online.py                       |  12 +-
 brainpy/types.py                              |   4 +-
 39 files changed, 725 insertions(+), 725 deletions(-)

diff --git a/brainpy/algorithms/offline.py b/brainpy/algorithms/offline.py
index 52027de08..3d0e61e62 100644
--- a/brainpy/algorithms/offline.py
+++ b/brainpy/algorithms/offline.py
@@ -7,7 +7,7 @@
 
 import brainpy.math as bm
 from brainpy.base import Base
-from brainpy.types import Tensor
+from brainpy.types import Array
 from .utils import (Sigmoid,
                     Regularization, L1Regularization, L1L2Regularization, L2Regularization,
                     polynomial_features, normalize)
@@ -60,7 +60,7 @@ def __call__(self, identifier, target, input, output):
     """
     return self.call(identifier, target, input, output)
 
-  def call(self, identifier, targets, inputs, outputs) -> Tensor:
+  def call(self, identifier, targets, inputs, outputs) -> Array:
     """The training procedure.
 
     Parameters
@@ -355,7 +355,7 @@ def __init__(
     self.gradient_descent = gradient_descent
     self.sigmoid = Sigmoid()
 
-  def call(self, identifier, targets, inputs, outputs=None) -> Tensor:
+  def call(self, identifier, targets, inputs, outputs=None) -> Array:
     # prepare data
     inputs = _check_data_2d_atls(bm.asarray(inputs))
     targets = _check_data_2d_atls(bm.asarray(targets))
diff --git a/brainpy/analysis/highdim/slow_points.py b/brainpy/analysis/highdim/slow_points.py
index 571ea4ae7..9b74be3bf 100644
--- a/brainpy/analysis/highdim/slow_points.py
+++ b/brainpy/analysis/highdim/slow_points.py
@@ -18,7 +18,7 @@
 from brainpy.dyn.runners import build_inputs, check_and_format_inputs
 from brainpy.errors import AnalyzerError, UnsupportedError
 from brainpy.tools.others.dicts import DotDict
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'SlowPointFinder',
@@ -295,7 +295,7 @@ def selected_ids(self, val):
 
   def find_fps_with_gd_method(
       self,
-      candidates: Union[Tensor, Dict[str, Tensor]],
+      candidates: Union[Array, Dict[str, Array]],
       tolerance: Union[float, Dict[str, float]] = 1e-5,
       num_batch: int = 100,
       num_opt: int = 10000,
@@ -305,7 +305,7 @@ def find_fps_with_gd_method(
 
     Parameters
     ----------
-    candidates : Tensor, dict
+    candidates : Array, dict
       The array with the shape of (batch size, state dim) of hidden states
       of RNN to start training for fixed points.
 
@@ -402,14 +402,14 @@ def batch_train(start_i, n_batch):
 
   def find_fps_with_opt_solver(
       self,
-      candidates: Union[Tensor, Dict[str, Tensor]],
+      candidates: Union[Array, Dict[str, Array]],
       opt_solver: str = 'BFGS'
   ):
     """Optimize fixed points with nonlinear optimization solvers.
 
     Parameters
     ----------
-    candidates: Tensor, dict
+    candidates: Array, dict
       The candidate (initial) fixed points.
     opt_solver: str
       The solver of the optimization.
@@ -536,7 +536,7 @@ def exclude_outliers(self, tolerance: float = 1e0):
 
   def compute_jacobians(
       self,
-      points: Union[Tensor, Dict[str, Tensor]],
+      points: Union[Array, Dict[str, Array]],
       stack_dict_var: bool = True,
       plot: bool = False,
       num_col: int = 4,
diff --git a/brainpy/dyn/channels/Ca.py b/brainpy/dyn/channels/Ca.py
index 9a8efd4fb..cb423200a 100644
--- a/brainpy/dyn/channels/Ca.py
+++ b/brainpy/dyn/channels/Ca.py
@@ -12,7 +12,7 @@
 from brainpy.initialize import OneInit, Initializer, parameter, variable
 from brainpy.integrators.joint_eq import JointEq
 from brainpy.integrators.ode import odeint
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 from brainpy.modes import Mode, BatchingMode, normal
 from .base import Calcium, CalciumChannel
 
@@ -46,8 +46,8 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = 120.,
-      C: Union[float, Tensor, Initializer, Callable] = 2.4e-4,
+      E: Union[float, Array, Initializer, Callable] = 120.,
+      C: Union[float, Array, Initializer, Callable] = 2.4e-4,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -82,11 +82,11 @@ class CalciumDyna(Calcium):
     The ion size.
   keep_size: bool
     Keep the geometry size.
-  C0: float, Tensor, Initializer, Callable
+  C0: float, Array, Initializer, Callable
     The Calcium concentration outside of membrane.
-  T: float, Tensor, Initializer, Callable
+  T: float, Array, Initializer, Callable
     The temperature.
-  C_initializer: Initializer, Callable, Tensor
+  C_initializer: Initializer, Callable, Array
     The initializer for Calcium concentration.
   method: str
     The numerical method.
@@ -100,9 +100,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      C0: Union[float, Tensor, Initializer, Callable] = 2.,
-      T: Union[float, Tensor, Initializer, Callable] = 36.,
-      C_initializer: Union[Initializer, Callable, Tensor] = OneInit(2.4e-4),
+      C0: Union[float, Array, Initializer, Callable] = 2.,
+      T: Union[float, Array, Initializer, Callable] = 36.,
+      C_initializer: Union[Initializer, Callable, Array] = OneInit(2.4e-4),
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -263,12 +263,12 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor, Initializer, Callable] = 36.,
-      d: Union[float, Tensor, Initializer, Callable] = 1.,
-      C_rest: Union[float, Tensor, Initializer, Callable] = 2.4e-4,
-      tau: Union[float, Tensor, Initializer, Callable] = 5.,
-      C0: Union[float, Tensor, Initializer, Callable] = 2.,
-      C_initializer: Union[Initializer, Callable, Tensor] = OneInit(2.4e-4),
+      T: Union[float, Array, Initializer, Callable] = 36.,
+      d: Union[float, Array, Initializer, Callable] = 1.,
+      C_rest: Union[float, Array, Initializer, Callable] = 2.4e-4,
+      tau: Union[float, Array, Initializer, Callable] = 5.,
+      C0: Union[float, Array, Initializer, Callable] = 2.,
+      C_initializer: Union[Initializer, Callable, Array] = OneInit(2.4e-4),
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -308,11 +308,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor, Initializer, Callable] = 36.,
-      alpha: Union[float, Tensor, Initializer, Callable] = 0.13,
-      beta: Union[float, Tensor, Initializer, Callable] = 0.075,
-      C0: Union[float, Tensor, Initializer, Callable] = 2.,
-      C_initializer: Union[Initializer, Callable, Tensor] = OneInit(2.4e-4),
+      T: Union[float, Array, Initializer, Callable] = 36.,
+      alpha: Union[float, Array, Initializer, Callable] = 0.13,
+      beta: Union[float, Array, Initializer, Callable] = 0.075,
+      C0: Union[float, Array, Initializer, Callable] = 2.,
+      C_initializer: Union[Initializer, Callable, Array] = OneInit(2.4e-4),
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -365,11 +365,11 @@ class ICa_p2q_ss(CalciumChannel):
     The numerical method
   name: str
     The name of the object.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximum conductance.
-  phi_p : float, Tensor, Callable, Initializer
+  phi_p : float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : float, Tensor, Callable, Initializer
+  phi_q : float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   """
@@ -378,9 +378,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 3.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 2.,
+      phi_p: Union[float, Array, Initializer, Callable] = 3.,
+      phi_q: Union[float, Array, Initializer, Callable] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 2.,
       method: str = 'exp_auto',
       mode: Mode = normal,
       name: str = None
@@ -458,11 +458,11 @@ class ICa_p2q_markov(CalciumChannel):
     The numerical method
   name: str
     The name of the object.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximum conductance.
-  phi_p : float, Tensor, Callable, Initializer
+  phi_p : float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : float, Tensor, Callable, Initializer
+  phi_q : float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   """
@@ -471,9 +471,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 3.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 2.,
+      phi_p: Union[float, Array, Initializer, Callable] = 3.,
+      phi_q: Union[float, Array, Initializer, Callable] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 2.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -573,9 +573,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = 10.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = 10.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -637,19 +637,19 @@ class ICaT_HM1992(ICa_p2q_ss):
 
   Parameters
   ----------
-  T : float, Tensor
+  T : float, Array
     The temperature.
-  T_base_p : float, Tensor
+  T_base_p : float, Array
     The base temperature factor of :math:`p` channel.
-  T_base_q : float, Tensor
+  T_base_q : float, Array
     The base temperature factor of :math:`q` channel.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximum conductance.
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -667,13 +667,13 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor] = 36.,
-      T_base_p: Union[float, Tensor] = 3.55,
-      T_base_q: Union[float, Tensor] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 2.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = -3.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = None,
-      phi_q: Union[float, Tensor, Initializer, Callable] = None,
+      T: Union[float, Array] = 36.,
+      T_base_p: Union[float, Array] = 3.55,
+      T_base_q: Union[float, Array] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 2.,
+      V_sh: Union[float, Array, Initializer, Callable] = -3.,
+      phi_p: Union[float, Array, Initializer, Callable] = None,
+      phi_q: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -734,19 +734,19 @@ class ICaT_HP1992(ICa_p2q_ss):
 
   Parameters
   ----------
-  T : float, Tensor
+  T : float, Array
     The temperature.
-  T_base_p : float, Tensor
+  T_base_p : float, Array
     The base temperature factor of :math:`p` channel.
-  T_base_q : float, Tensor
+  T_base_q : float, Array
     The base temperature factor of :math:`q` channel.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximum conductance.
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -765,13 +765,13 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor] = 36.,
-      T_base_p: Union[float, Tensor] = 5.,
-      T_base_q: Union[float, Tensor] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.75,
-      V_sh: Union[float, Tensor, Initializer, Callable] = -3.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = None,
-      phi_q: Union[float, Tensor, Initializer, Callable] = None,
+      T: Union[float, Array] = 36.,
+      T_base_p: Union[float, Array] = 5.,
+      T_base_q: Union[float, Array] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.75,
+      V_sh: Union[float, Array, Initializer, Callable] = -3.,
+      phi_p: Union[float, Array, Initializer, Callable] = None,
+      phi_q: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -835,15 +835,15 @@ class ICaHT_HM1992(ICa_p2q_ss):
 
   Parameters
   ----------
-  T : float, Tensor
+  T : float, Array
     The temperature.
-  T_base_p : float, Tensor
+  T_base_p : float, Array
     The base temperature factor of :math:`p` channel.
-  T_base_q : float, Tensor
+  T_base_q : float, Array
     The base temperature factor of :math:`q` channel.
-  g_max : float, Tensor, Initializer, Callable
+  g_max : float, Array, Initializer, Callable
     The maximum conductance.
-  V_sh : float, Tensor, Initializer, Callable
+  V_sh : float, Array, Initializer, Callable
     The membrane potential shift.
 
   References
@@ -860,11 +860,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor] = 36.,
-      T_base_p: Union[float, Tensor] = 3.55,
-      T_base_q: Union[float, Tensor] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 2.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 25.,
+      T: Union[float, Array] = 36.,
+      T_base_p: Union[float, Array] = 3.55,
+      T_base_q: Union[float, Array] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 2.,
+      V_sh: Union[float, Array, Initializer, Callable] = 25.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -935,20 +935,20 @@ class ICaHT_Re1993(ICa_p2q_markov):
     The numerical method
   name: str
     The name of the object.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximum conductance.
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  T : float, Tensor
+  T : float, Array
     The temperature.
-  T_base_p : float, Tensor
+  T_base_p : float, Array
     The base temperature factor of :math:`p` channel.
-  T_base_q : float, Tensor
+  T_base_q : float, Array
     The base temperature factor of :math:`q` channel.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
     If `None`, :math:`\phi_p = \mathrm{T_base_p}^{\frac{T-23}{10}}`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
     If `None`, :math:`\phi_q = \mathrm{T_base_q}^{\frac{T-23}{10}}`.
 
@@ -965,13 +965,13 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor] = 36.,
-      T_base_p: Union[float, Tensor] = 2.3,
-      T_base_q: Union[float, Tensor] = 2.3,
-      phi_p: Union[float, Tensor, Initializer, Callable] = None,
-      phi_q: Union[float, Tensor, Initializer, Callable] = None,
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
+      T: Union[float, Array] = 36.,
+      T_base_p: Union[float, Array] = 2.3,
+      T_base_q: Union[float, Array] = 2.3,
+      phi_p: Union[float, Array, Initializer, Callable] = None,
+      phi_q: Union[float, Array, Initializer, Callable] = None,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -1054,11 +1054,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[float, Tensor, Initializer, Callable] = 36.,
-      T_base_p: Union[float, Tensor, Initializer, Callable] = 3.55,
-      T_base_q: Union[float, Tensor, Initializer, Callable] = 3.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
+      T: Union[float, Array, Initializer, Callable] = 36.,
+      T_base_p: Union[float, Array, Initializer, Callable] = 3.55,
+      T_base_q: Union[float, Array, Initializer, Callable] = 3.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/channels/IH.py b/brainpy/dyn/channels/IH.py
index 4cb942416..2484d7d9e 100644
--- a/brainpy/dyn/channels/IH.py
+++ b/brainpy/dyn/channels/IH.py
@@ -10,7 +10,7 @@
 import brainpy.math as bm
 from brainpy.initialize import Initializer, parameter, variable
 from brainpy.integrators import odeint, JointEq
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 from brainpy.modes import Mode, BatchingMode, normal
 from .base import IhChannel, CalciumChannel, Calcium
 
@@ -58,9 +58,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      E: Union[float, Tensor, Initializer, Callable] = 43.,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      E: Union[float, Array, Initializer, Callable] = 43.,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -161,16 +161,16 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -40.,
-      k2: Union[float, Tensor, Initializer, Callable] = 4e-4,
-      k4: Union[float, Tensor, Initializer, Callable] = 1e-3,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.02,
-      g_inc: Union[float, Tensor, Initializer, Callable] = 2.,
-      Ca_half: Union[float, Tensor, Initializer, Callable] = 2e-3,
-      T: Union[float, Tensor] = 36.,
-      T_base: Union[float, Tensor] = 3.,
-      phi: Union[float, Tensor, Initializer, Callable] = None,
+      E: Union[float, Array, Initializer, Callable] = -40.,
+      k2: Union[float, Array, Initializer, Callable] = 4e-4,
+      k4: Union[float, Array, Initializer, Callable] = 1e-3,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
+      g_max: Union[float, Array, Initializer, Callable] = 0.02,
+      g_inc: Union[float, Array, Initializer, Callable] = 2.,
+      Ca_half: Union[float, Array, Initializer, Callable] = 2e-3,
+      T: Union[float, Array] = 36.,
+      T_base: Union[float, Array] = 3.,
+      phi: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/channels/K.py b/brainpy/dyn/channels/K.py
index c9ea80168..d1218e293 100644
--- a/brainpy/dyn/channels/K.py
+++ b/brainpy/dyn/channels/K.py
@@ -10,7 +10,7 @@
 import brainpy.math as bm
 from brainpy.initialize import Initializer, parameter, variable
 from brainpy.integrators import odeint, JointEq
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 from brainpy.modes import Mode, BatchingMode, normal
 from .base import PotassiumChannel
 
@@ -71,9 +71,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -167,12 +167,12 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = -50.,
-      T_base: Union[float, Tensor] = 3.,
-      T: Union[float, Tensor] = 36.,
-      phi: Optional[Union[float, Tensor, Initializer, Callable]] = None,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      V_sh: Union[float, Array, Initializer, Callable] = -50.,
+      T_base: Union[float, Array] = 3.,
+      T: Union[float, Array] = 36.,
+      phi: Optional[Union[float, Array, Initializer, Callable]] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -244,10 +244,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[int, float, Tensor, Initializer, Callable] = -60.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[int, float, Array, Initializer, Callable] = -60.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -315,10 +315,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[int, float, Tensor, Initializer, Callable] = -45.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[int, float, Array, Initializer, Callable] = -45.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -368,9 +368,9 @@ class IKA_p4q_ss(PotassiumChannel):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -387,10 +387,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -477,11 +477,11 @@ class IKA1_HM1992(IKA_p4q_ss):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -502,11 +502,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 30.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 30.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -572,11 +572,11 @@ class IKA2_HM1992(IKA_p4q_ss):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -597,11 +597,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 20.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 20.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -662,9 +662,9 @@ class IKK2_pq_ss(PotassiumChannel):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -682,10 +682,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -771,11 +771,11 @@ class IKK2A_HM1992(IKK2_pq_ss):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -793,11 +793,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -862,11 +862,11 @@ class IKK2B_HM1992(IKK2_pq_ss):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  phi_q : optional, float, Tensor, Callable, Initializer
+  phi_q : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`q`.
 
   References
@@ -884,11 +884,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -952,11 +952,11 @@ class IKNI_Ya1989(PotassiumChannel):
     The maximal conductance density (:math:`mS/cm^2`).
   E : float, JaxArray, ndarray, Initializer, Callable
     The reversal potential (mV).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The membrane potential shift.
-  phi_p : optional, float, Tensor, Callable, Initializer
+  phi_p : optional, float, Array, Callable, Initializer
     The temperature factor for channel :math:`p`.
-  tau_max: float, Tensor, Callable, Initializer
+  tau_max: float, Array, Callable, Initializer
     The :math:`tau_{\max}` parameter.
 
   References
@@ -969,12 +969,12 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -90.,
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.004,
-      phi_p: Union[float, Tensor, Initializer, Callable] = 1.,
-      phi_q: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau_max: Union[float, Tensor, Initializer, Callable] = 4e3,
-      V_sh: Union[float, Tensor, Initializer, Callable] = 0.,
+      E: Union[float, Array, Initializer, Callable] = -90.,
+      g_max: Union[float, Array, Initializer, Callable] = 0.004,
+      phi_p: Union[float, Array, Initializer, Callable] = 1.,
+      phi_q: Union[float, Array, Initializer, Callable] = 1.,
+      tau_max: Union[float, Array, Initializer, Callable] = 4e3,
+      V_sh: Union[float, Array, Initializer, Callable] = 0.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/channels/KCa.py b/brainpy/dyn/channels/KCa.py
index f413fef09..0baa9211f 100644
--- a/brainpy/dyn/channels/KCa.py
+++ b/brainpy/dyn/channels/KCa.py
@@ -11,7 +11,7 @@
 import brainpy.math as bm
 from brainpy.initialize import Initializer, parameter, variable
 from brainpy.integrators.ode import odeint
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 from brainpy.modes import Mode, BatchingMode, normal
 from .base import Calcium, CalciumChannel, PotassiumChannel
 
@@ -75,12 +75,12 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[float, Tensor, Initializer, Callable] = -95.,
-      n: Union[float, Tensor, Initializer, Callable] = 2,
-      g_max: Union[float, Tensor, Initializer, Callable] = 10.,
-      alpha: Union[float, Tensor, Initializer, Callable] = 48.,
-      beta: Union[float, Tensor, Initializer, Callable] = 0.09,
-      phi: Union[float, Tensor, Initializer, Callable] = 1.,
+      E: Union[float, Array, Initializer, Callable] = -95.,
+      n: Union[float, Array, Initializer, Callable] = 2,
+      g_max: Union[float, Array, Initializer, Callable] = 10.,
+      alpha: Union[float, Array, Initializer, Callable] = 48.,
+      beta: Union[float, Array, Initializer, Callable] = 0.09,
+      phi: Union[float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/channels/Na.py b/brainpy/dyn/channels/Na.py
index f225049f9..58ed5fe8f 100644
--- a/brainpy/dyn/channels/Na.py
+++ b/brainpy/dyn/channels/Na.py
@@ -10,7 +10,7 @@
 import brainpy.math as bm
 from brainpy.initialize import Initializer, parameter, variable
 from brainpy.integrators import odeint, JointEq
-from brainpy.types import Tensor, Shape
+from brainpy.types import Array, Shape
 from brainpy.modes import Mode, BatchingMode, normal
 from .base import SodiumChannel
 
@@ -39,11 +39,11 @@ class INa_p3q_markov(SodiumChannel):
 
   Parameters
   ----------
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximal conductance density (:math:`mS/cm^2`).
-  E : float, Tensor, Callable, Initializer
+  E : float, Array, Callable, Initializer
     The reversal potential (mV).
-  phi : float, Tensor, Callable, Initializer
+  phi : float, Array, Callable, Initializer
     The temperature-dependent factor.
   method: str
     The numerical method
@@ -56,9 +56,9 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[int, float, Tensor, Initializer, Callable] = 50.,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 90.,
-      phi: Union[int, float, Tensor, Initializer, Callable] = 1.,
+      E: Union[int, float, Array, Initializer, Callable] = 50.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 90.,
+      phi: Union[int, float, Array, Initializer, Callable] = 1.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -141,13 +141,13 @@ class INa_Ba2002(INa_p3q_markov):
 
   Parameters
   ----------
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximal conductance density (:math:`mS/cm^2`).
-  E : float, Tensor, Callable, Initializer
+  E : float, Array, Callable, Initializer
     The reversal potential (mV).
-  T : float, Tensor
+  T : float, Array
     The temperature (Celsius, :math:`^{\circ}C`).
-  V_sh : float, Tensor, Callable, Initializer
+  V_sh : float, Array, Callable, Initializer
     The shift of the membrane potential to spike.
 
   References
@@ -165,10 +165,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      T: Union[int, float, Tensor] = 36.,
-      E: Union[int, float, Tensor, Initializer, Callable] = 50.,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 90.,
-      V_sh: Union[int, float, Tensor, Initializer, Callable] = -50.,
+      T: Union[int, float, Array] = 36.,
+      E: Union[int, float, Array, Initializer, Callable] = 50.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 90.,
+      V_sh: Union[int, float, Array, Initializer, Callable] = -50.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -231,11 +231,11 @@ class INa_TM1991(INa_p3q_markov):
     The numerical method
   name: str
     The name of the object.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximal conductance density (:math:`mS/cm^2`).
-  E : float, Tensor, Callable, Initializer
+  E : float, Array, Callable, Initializer
     The reversal potential (mV).
-  V_sh: float, Tensor, Callable, Initializer
+  V_sh: float, Array, Callable, Initializer
     The membrane shift.
 
   References
@@ -252,10 +252,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[int, float, Tensor, Initializer, Callable] = 50.,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 120.,
-      phi: Union[int, float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[int, float, Tensor, Initializer, Callable] = -63.,
+      E: Union[int, float, Array, Initializer, Callable] = 50.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 120.,
+      phi: Union[int, float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[int, float, Array, Initializer, Callable] = -63.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -317,11 +317,11 @@ class INa_HH1952(INa_p3q_markov):
     The numerical method
   name: str
     The name of the object.
-  g_max : float, Tensor, Callable, Initializer
+  g_max : float, Array, Callable, Initializer
     The maximal conductance density (:math:`mS/cm^2`).
-  E : float, Tensor, Callable, Initializer
+  E : float, Array, Callable, Initializer
     The reversal potential (mV).
-  V_sh: float, Tensor, Callable, Initializer
+  V_sh: float, Array, Callable, Initializer
     The membrane shift.
 
   References
@@ -339,10 +339,10 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      E: Union[int, float, Tensor, Initializer, Callable] = 50.,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 120.,
-      phi: Union[int, float, Tensor, Initializer, Callable] = 1.,
-      V_sh: Union[int, float, Tensor, Initializer, Callable] = -45.,
+      E: Union[int, float, Array, Initializer, Callable] = 50.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 120.,
+      phi: Union[int, float, Array, Initializer, Callable] = 1.,
+      V_sh: Union[int, float, Array, Initializer, Callable] = -45.,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/channels/leaky.py b/brainpy/dyn/channels/leaky.py
index 2eb67cdff..b054127bc 100644
--- a/brainpy/dyn/channels/leaky.py
+++ b/brainpy/dyn/channels/leaky.py
@@ -8,7 +8,7 @@
 from typing import Union, Callable
 
 from brainpy.initialize import Initializer, parameter
-from brainpy.types import Tensor, Shape
+from brainpy.types import Array, Shape
 from brainpy.modes import Mode, BatchingMode, normal
 
 from .base import LeakyChannel
@@ -34,8 +34,8 @@ def __init__(
       self,
       size,
       keep_size: bool = False,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 0.1,
-      E: Union[int, float, Tensor, Initializer, Callable] = -70.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 0.1,
+      E: Union[int, float, Array, Initializer, Callable] = -70.,
       method: str = None,
       name: str = None,
       mode: Mode = normal,
@@ -75,8 +75,8 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      g_max: Union[int, float, Tensor, Initializer, Callable] = 0.005,
-      E: Union[int, float, Tensor, Initializer, Callable] = -90.,
+      g_max: Union[int, float, Array, Initializer, Callable] = 0.005,
+      E: Union[int, float, Array, Initializer, Callable] = -90.,
       method: str = None,
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/layers/linear.py b/brainpy/dyn/layers/linear.py
index d695da81d..4365cb1fd 100644
--- a/brainpy/dyn/layers/linear.py
+++ b/brainpy/dyn/layers/linear.py
@@ -11,7 +11,7 @@
 from brainpy.initialize import XavierNormal, ZeroInit, Initializer, parameter
 from brainpy.modes import Mode, TrainingMode, training
 from brainpy.tools.checking import check_initializer
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'Dense',
@@ -45,8 +45,8 @@ def __init__(
       self,
       num_in: int,
       num_out: int,
-      W_initializer: Union[Initializer, Callable, Tensor] = XavierNormal(),
-      b_initializer: Optional[Union[Initializer, Callable, Tensor]] = ZeroInit(),
+      W_initializer: Union[Initializer, Callable, Array] = XavierNormal(),
+      b_initializer: Optional[Union[Initializer, Callable, Array]] = ZeroInit(),
       mode: Mode = training,
       name: str = None,
   ):
@@ -108,8 +108,8 @@ def online_init(self):
     self.online_fit_by.initialize(feature_in=num_input, feature_out=self.num_out, identifier=self.name)
 
   def online_fit(self,
-                 target: Tensor,
-                 fit_record: Dict[str, Tensor]):
+                 target: Array,
+                 fit_record: Dict[str, Array]):
     if not isinstance(target, (bm.ndarray, jnp.ndarray)):
       raise MathError(f'"target" must be a tensor, but got {type(target)}')
     x = fit_record['input']
@@ -150,8 +150,8 @@ def offline_init(self):
     self.offline_fit_by.initialize(feature_in=num_input, feature_out=self.num_out, identifier=self.name)
 
   def offline_fit(self,
-                  target: Tensor,
-                  fit_record: Dict[str, Tensor]):
+                  target: Array,
+                  fit_record: Dict[str, Array]):
     """The offline training interface for the Dense node."""
     # data checking
     if not isinstance(target, (bm.ndarray, jnp.ndarray)):
diff --git a/brainpy/dyn/layers/reservoir.py b/brainpy/dyn/layers/reservoir.py
index ff6d17625..32234f9a1 100644
--- a/brainpy/dyn/layers/reservoir.py
+++ b/brainpy/dyn/layers/reservoir.py
@@ -8,7 +8,7 @@
 from brainpy.modes import Mode, TrainingMode, batching
 from brainpy.tools.checking import check_float, check_initializer, check_string
 from brainpy.tools.others import to_size
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'Reservoir',
@@ -29,7 +29,7 @@ class Reservoir(DynamicalSystem):
     The initialization method for the feedforward connections.
   Wrec_initializer: Initializer
     The initialization method for the recurrent connections.
-  b_initializer: optional, Tensor, Initializer
+  b_initializer: optional, Array, Initializer
     The initialization method for the bias.
   leaky_rate: float
     A float between 0 and 1.
@@ -92,9 +92,9 @@ def __init__(
       leaky_rate: float = 0.3,
       activation: Union[str, Callable] = 'tanh',
       activation_type: str = 'internal',
-      Win_initializer: Union[Initializer, Callable, Tensor] = Normal(scale=0.1),
-      Wrec_initializer: Union[Initializer, Callable, Tensor] = Normal(scale=0.1),
-      b_initializer: Optional[Union[Initializer, Callable, Tensor]] = ZeroInit(),
+      Win_initializer: Union[Initializer, Callable, Array] = Normal(scale=0.1),
+      Wrec_initializer: Union[Initializer, Callable, Array] = Normal(scale=0.1),
+      b_initializer: Optional[Union[Initializer, Callable, Array]] = ZeroInit(),
       in_connectivity: float = 0.1,
       rec_connectivity: float = 0.1,
       comp_type='dense',
diff --git a/brainpy/dyn/layers/rnncells.py b/brainpy/dyn/layers/rnncells.py
index 572c9bdca..d53113822 100644
--- a/brainpy/dyn/layers/rnncells.py
+++ b/brainpy/dyn/layers/rnncells.py
@@ -14,7 +14,7 @@
 from brainpy.modes import Mode, TrainingMode, training
 from brainpy.tools.checking import (check_integer,
                                     check_initializer)
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'VanillaRNN',
@@ -26,7 +26,7 @@
 class RecurrentCell(DynamicalSystem):
   def __init__(self,
                num_out: int,
-               state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
+               state_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
                mode: Mode = training,
                train_state: bool = False,
                name: str = None):
@@ -77,10 +77,10 @@ def __init__(
       self,
       num_in: int,
       num_out: int,
-      state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
-      Wi_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(),
-      Wh_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(),
-      b_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
+      state_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
+      Wi_initializer: Union[Array, Callable, Initializer] = XavierNormal(),
+      Wh_initializer: Union[Array, Callable, Initializer] = XavierNormal(),
+      b_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
       activation: str = 'relu',
       mode: Mode = training,
       train_state: bool = False,
@@ -188,10 +188,10 @@ def __init__(
       self,
       num_in: int,
       num_out: int,
-      Wi_initializer: Union[Tensor, Callable, Initializer] = Orthogonal(),
-      Wh_initializer: Union[Tensor, Callable, Initializer] = Orthogonal(),
-      b_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
-      state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
+      Wi_initializer: Union[Array, Callable, Initializer] = Orthogonal(),
+      Wh_initializer: Union[Array, Callable, Initializer] = Orthogonal(),
+      b_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
+      state_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
       activation: str = 'tanh',
       mode: Mode = training,
       train_state: bool = False,
@@ -322,10 +322,10 @@ def __init__(
       self,
       num_in: int,
       num_out: int,
-      Wi_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(),
-      Wh_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(),
-      b_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
-      state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
+      Wi_initializer: Union[Array, Callable, Initializer] = XavierNormal(),
+      Wh_initializer: Union[Array, Callable, Initializer] = XavierNormal(),
+      b_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
+      state_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
       activation: str = 'tanh',
       mode: Mode = training,
       train_state: bool = False,
diff --git a/brainpy/dyn/neurons/biological_models.py b/brainpy/dyn/neurons/biological_models.py
index 23c737c49..566e5024d 100644
--- a/brainpy/dyn/neurons/biological_models.py
+++ b/brainpy/dyn/neurons/biological_models.py
@@ -10,7 +10,7 @@
 from brainpy.integrators.sde import sdeint
 from brainpy.modes import Mode, BatchingMode, TrainingMode, NormalMode, normal, check
 from brainpy.tools.checking import check_initializer
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 
 __all__ = [
   'HH',
@@ -195,19 +195,19 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      ENa: Union[float, Tensor, Initializer, Callable] = 50.,
-      gNa: Union[float, Tensor, Initializer, Callable] = 120.,
-      EK: Union[float, Tensor, Initializer, Callable] = -77.,
-      gK: Union[float, Tensor, Initializer, Callable] = 36.,
-      EL: Union[float, Tensor, Initializer, Callable] = -54.387,
-      gL: Union[float, Tensor, Initializer, Callable] = 0.03,
-      V_th: Union[float, Tensor, Initializer, Callable] = 20.,
-      C: Union[float, Tensor, Initializer, Callable] = 1.0,
-      V_initializer: Union[Initializer, Callable, Tensor] = Uniform(-70, -60.),
-      m_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.5),
-      h_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.6),
-      n_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.32),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      ENa: Union[float, Array, Initializer, Callable] = 50.,
+      gNa: Union[float, Array, Initializer, Callable] = 120.,
+      EK: Union[float, Array, Initializer, Callable] = -77.,
+      gK: Union[float, Array, Initializer, Callable] = 36.,
+      EL: Union[float, Array, Initializer, Callable] = -54.387,
+      gL: Union[float, Array, Initializer, Callable] = 0.03,
+      V_th: Union[float, Array, Initializer, Callable] = 20.,
+      C: Union[float, Array, Initializer, Callable] = 1.0,
+      V_initializer: Union[Initializer, Callable, Array] = Uniform(-70, -60.),
+      m_initializer: Union[Initializer, Callable, Array] = OneInit(0.5),
+      h_initializer: Union[Initializer, Callable, Array] = OneInit(0.6),
+      n_initializer: Union[Initializer, Callable, Array] = OneInit(0.32),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
 
@@ -385,22 +385,22 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      V_Ca: Union[float, Tensor, Initializer, Callable] = 130.,
-      g_Ca: Union[float, Tensor, Initializer, Callable] = 4.4,
-      V_K: Union[float, Tensor, Initializer, Callable] = -84.,
-      g_K: Union[float, Tensor, Initializer, Callable] = 8.,
-      V_leak: Union[float, Tensor, Initializer, Callable] = -60.,
-      g_leak: Union[float, Tensor, Initializer, Callable] = 2.,
-      C: Union[float, Tensor, Initializer, Callable] = 20.,
-      V1: Union[float, Tensor, Initializer, Callable] = -1.2,
-      V2: Union[float, Tensor, Initializer, Callable] = 18.,
-      V3: Union[float, Tensor, Initializer, Callable] = 2.,
-      V4: Union[float, Tensor, Initializer, Callable] = 30.,
-      phi: Union[float, Tensor, Initializer, Callable] = 0.04,
-      V_th: Union[float, Tensor, Initializer, Callable] = 10.,
-      W_initializer: Union[Callable, Initializer, Tensor] = OneInit(0.02),
-      V_initializer: Union[Callable, Initializer, Tensor] = Uniform(-70., -60.),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_Ca: Union[float, Array, Initializer, Callable] = 130.,
+      g_Ca: Union[float, Array, Initializer, Callable] = 4.4,
+      V_K: Union[float, Array, Initializer, Callable] = -84.,
+      g_K: Union[float, Array, Initializer, Callable] = 8.,
+      V_leak: Union[float, Array, Initializer, Callable] = -60.,
+      g_leak: Union[float, Array, Initializer, Callable] = 2.,
+      C: Union[float, Array, Initializer, Callable] = 20.,
+      V1: Union[float, Array, Initializer, Callable] = -1.2,
+      V2: Union[float, Array, Initializer, Callable] = 18.,
+      V3: Union[float, Array, Initializer, Callable] = 2.,
+      V4: Union[float, Array, Initializer, Callable] = 30.,
+      phi: Union[float, Array, Initializer, Callable] = 0.04,
+      V_th: Union[float, Array, Initializer, Callable] = 10.,
+      W_initializer: Union[Callable, Initializer, Array] = OneInit(0.02),
+      V_initializer: Union[Callable, Initializer, Array] = Uniform(-70., -60.),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
 
@@ -638,29 +638,29 @@ def __init__(
       size: Shape,
       keep_size: bool = False,
       # maximum conductance
-      gNa: Union[float, Tensor, Initializer, Callable] = 30.,
-      gK: Union[float, Tensor, Initializer, Callable] = 15.,
-      gCa: Union[float, Tensor, Initializer, Callable] = 10.,
-      gAHP: Union[float, Tensor, Initializer, Callable] = 0.8,
-      gC: Union[float, Tensor, Initializer, Callable] = 15.,
-      gL: Union[float, Tensor, Initializer, Callable] = 0.1,
+      gNa: Union[float, Array, Initializer, Callable] = 30.,
+      gK: Union[float, Array, Initializer, Callable] = 15.,
+      gCa: Union[float, Array, Initializer, Callable] = 10.,
+      gAHP: Union[float, Array, Initializer, Callable] = 0.8,
+      gC: Union[float, Array, Initializer, Callable] = 15.,
+      gL: Union[float, Array, Initializer, Callable] = 0.1,
       # reversal potential
-      ENa: Union[float, Tensor, Initializer, Callable] = 60.,
-      EK: Union[float, Tensor, Initializer, Callable] = -75.,
-      ECa: Union[float, Tensor, Initializer, Callable] = 80.,
-      EL: Union[float, Tensor, Initializer, Callable] = -60.,
+      ENa: Union[float, Array, Initializer, Callable] = 60.,
+      EK: Union[float, Array, Initializer, Callable] = -75.,
+      ECa: Union[float, Array, Initializer, Callable] = 80.,
+      EL: Union[float, Array, Initializer, Callable] = -60.,
       # other parameters
-      gc: Union[float, Tensor, Initializer, Callable] = 2.1,
-      V_th: Union[float, Tensor, Initializer, Callable] = 20.,
-      Cm: Union[float, Tensor, Initializer, Callable] = 3.0,
-      p: Union[float, Tensor, Initializer, Callable] = 0.5,
-      A: Union[float, Tensor, Initializer, Callable] = 1.,
+      gc: Union[float, Array, Initializer, Callable] = 2.1,
+      V_th: Union[float, Array, Initializer, Callable] = 20.,
+      Cm: Union[float, Array, Initializer, Callable] = 3.0,
+      p: Union[float, Array, Initializer, Callable] = 0.5,
+      A: Union[float, Array, Initializer, Callable] = 1.,
       # initializers
-      Vs_initializer: Union[Initializer, Callable, Tensor] = OneInit(-64.6),
-      Vd_initializer: Union[Initializer, Callable, Tensor] = OneInit(-64.5),
-      Ca_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.2),
+      Vs_initializer: Union[Initializer, Callable, Array] = OneInit(-64.6),
+      Vd_initializer: Union[Initializer, Callable, Array] = OneInit(-64.5),
+      Ca_initializer: Union[Initializer, Callable, Array] = OneInit(0.2),
       # others
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
@@ -960,19 +960,19 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      ENa: Union[float, Tensor, Initializer, Callable] = 55.,
-      gNa: Union[float, Tensor, Initializer, Callable] = 35.,
-      EK: Union[float, Tensor, Initializer, Callable] = -90.,
-      gK: Union[float, Tensor, Initializer, Callable] = 9.,
-      EL: Union[float, Tensor, Initializer, Callable] = -65,
-      gL: Union[float, Tensor, Initializer, Callable] = 0.1,
-      V_th: Union[float, Tensor, Initializer, Callable] = 20.,
-      phi: Union[float, Tensor, Initializer, Callable] = 5.0,
-      C: Union[float, Tensor, Initializer, Callable] = 1.0,
-      V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-65.),
-      h_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.6),
-      n_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.32),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      ENa: Union[float, Array, Initializer, Callable] = 55.,
+      gNa: Union[float, Array, Initializer, Callable] = 35.,
+      EK: Union[float, Array, Initializer, Callable] = -90.,
+      gK: Union[float, Array, Initializer, Callable] = 9.,
+      EL: Union[float, Array, Initializer, Callable] = -65,
+      gL: Union[float, Array, Initializer, Callable] = 0.1,
+      V_th: Union[float, Array, Initializer, Callable] = 20.,
+      phi: Union[float, Array, Initializer, Callable] = 5.0,
+      C: Union[float, Array, Initializer, Callable] = 1.0,
+      V_initializer: Union[Initializer, Callable, Array] = OneInit(-65.),
+      h_initializer: Union[Initializer, Callable, Array] = OneInit(0.6),
+      n_initializer: Union[Initializer, Callable, Array] = OneInit(0.32),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,
diff --git a/brainpy/dyn/neurons/fractional_models.py b/brainpy/dyn/neurons/fractional_models.py
index 9a643b563..7f8c548a6 100644
--- a/brainpy/dyn/neurons/fractional_models.py
+++ b/brainpy/dyn/neurons/fractional_models.py
@@ -10,7 +10,7 @@
 from brainpy.integrators.joint_eq import JointEq
 from brainpy.tools.checking import check_float, check_integer
 from brainpy.tools.checking import check_initializer
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 
 __all__ = [
   'FractionalNeuron',
@@ -83,16 +83,16 @@ def __init__(
       size: Shape,
       alpha: Union[float, Sequence[float]],
       num_memory: int = 1000,
-      a: Union[float, Tensor, Initializer, Callable] = 0.7,
-      b: Union[float, Tensor, Initializer, Callable] = 0.8,
-      c: Union[float, Tensor, Initializer, Callable] = -0.775,
-      d: Union[float, Tensor, Initializer, Callable] = 1.,
-      delta: Union[float, Tensor, Initializer, Callable] = 0.08,
-      mu: Union[float, Tensor, Initializer, Callable] = 0.0001,
-      Vth: Union[float, Tensor, Initializer, Callable] = 1.8,
-      V_initializer: Union[Initializer, Callable, Tensor] = OneInit(2.5),
-      w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      y_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
+      a: Union[float, Array, Initializer, Callable] = 0.7,
+      b: Union[float, Array, Initializer, Callable] = 0.8,
+      c: Union[float, Array, Initializer, Callable] = -0.775,
+      d: Union[float, Array, Initializer, Callable] = 1.,
+      delta: Union[float, Array, Initializer, Callable] = 0.08,
+      mu: Union[float, Array, Initializer, Callable] = 0.0001,
+      Vth: Union[float, Array, Initializer, Callable] = 1.8,
+      V_initializer: Union[Initializer, Callable, Array] = OneInit(2.5),
+      w_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      y_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
       name: str = None,
       keep_size: bool = False,
   ):
@@ -227,18 +227,18 @@ def __init__(
       size: Shape,
       alpha: Union[float, Sequence[float]],
       num_memory: int,
-      a: Union[float, Tensor, Initializer, Callable] = 0.02,
-      b: Union[float, Tensor, Initializer, Callable] = 0.20,
-      c: Union[float, Tensor, Initializer, Callable] = -65.,
-      d: Union[float, Tensor, Initializer, Callable] = 8.,
-      f: Union[float, Tensor, Initializer, Callable] = 0.04,
-      g: Union[float, Tensor, Initializer, Callable] = 5.,
-      h: Union[float, Tensor, Initializer, Callable] = 140.,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_th: Union[float, Tensor, Initializer, Callable] = 30.,
-      V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-65.),
-      u_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.20 * -65.),
+      a: Union[float, Array, Initializer, Callable] = 0.02,
+      b: Union[float, Array, Initializer, Callable] = 0.20,
+      c: Union[float, Array, Initializer, Callable] = -65.,
+      d: Union[float, Array, Initializer, Callable] = 8.,
+      f: Union[float, Array, Initializer, Callable] = 0.04,
+      g: Union[float, Array, Initializer, Callable] = 5.,
+      h: Union[float, Array, Initializer, Callable] = 140.,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 1.,
+      V_th: Union[float, Array, Initializer, Callable] = 30.,
+      V_initializer: Union[Initializer, Callable, Array] = OneInit(-65.),
+      u_initializer: Union[Initializer, Callable, Array] = OneInit(0.20 * -65.),
       keep_size: bool = False,
       name: str = None
   ):
diff --git a/brainpy/dyn/neurons/input_groups.py b/brainpy/dyn/neurons/input_groups.py
index dbc35bcbe..413ac0597 100644
--- a/brainpy/dyn/neurons/input_groups.py
+++ b/brainpy/dyn/neurons/input_groups.py
@@ -9,7 +9,7 @@
 from brainpy.errors import ModelBuildError
 from brainpy.initialize import Initializer, parameter, variable
 from brainpy.modes import Mode, BatchingMode, normal
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 
 __all__ = [
   'InputGroup',
@@ -112,8 +112,8 @@ class SpikeTimeGroup(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      times: Union[Sequence, Tensor],
-      indices: Union[Sequence, Tensor],
+      times: Union[Sequence, Array],
+      indices: Union[Sequence, Array],
       need_sort: bool = True,
       keep_size: bool = False,
       mode: Mode = normal,
diff --git a/brainpy/dyn/neurons/noise_groups.py b/brainpy/dyn/neurons/noise_groups.py
index c14be24c1..0e6de7aeb 100644
--- a/brainpy/dyn/neurons/noise_groups.py
+++ b/brainpy/dyn/neurons/noise_groups.py
@@ -7,7 +7,7 @@
 from brainpy.initialize import Initializer
 from brainpy.integrators.sde import sdeint
 from brainpy.modes import Mode, normal
-from brainpy.types import Tensor, Shape
+from brainpy.types import Array, Shape
 
 __all__ = [
   'OUProcess',
@@ -46,9 +46,9 @@ class OUProcess(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      mean: Union[float, Tensor, Initializer, Callable] = 0.,
-      sigma: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
+      mean: Union[float, Array, Initializer, Callable] = 0.,
+      sigma: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
       method: str = 'exp_euler',
       keep_size: bool = False,
       mode: Mode = normal,
diff --git a/brainpy/dyn/neurons/reduced_models.py b/brainpy/dyn/neurons/reduced_models.py
index d590bddb3..1cbec2913 100644
--- a/brainpy/dyn/neurons/reduced_models.py
+++ b/brainpy/dyn/neurons/reduced_models.py
@@ -11,7 +11,7 @@
 from brainpy.integrators import sdeint, odeint, JointEq
 from brainpy.modes import Mode, NormalMode, BatchingMode, TrainingMode, normal, check
 from brainpy.tools.checking import check_initializer, check_callable
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 
 __all__ = [
   'LeakyIntegrator',
@@ -72,11 +72,11 @@ def __init__(
       keep_size: bool = False,
 
       # neuron parameters
-      V_rest: Union[float, Tensor, Initializer, Callable] = 0.,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = 0.,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
 
       # other parameter
       name: str = None,
@@ -184,14 +184,14 @@ def __init__(
       keep_size: bool = False,
 
       # other parameter
-      V_rest: Union[float, Tensor, Initializer, Callable] = 0.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -5.,
-      V_th: Union[float, Tensor, Initializer, Callable] = 20.,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau_ref: Union[float, Tensor, Initializer, Callable] = None,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = 0.,
+      V_reset: Union[float, Array, Initializer, Callable] = -5.,
+      V_th: Union[float, Array, Initializer, Callable] = 20.,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      tau_ref: Union[float, Array, Initializer, Callable] = None,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
 
@@ -397,16 +397,16 @@ class ExpIF(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -65.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -68.,
-      V_th: Union[float, Tensor, Initializer, Callable] = -30.,
-      V_T: Union[float, Tensor, Initializer, Callable] = -59.9,
-      delta_T: Union[float, Tensor, Initializer, Callable] = 3.48,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau_ref: Union[float, Tensor, Initializer, Callable] = None,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -65.,
+      V_reset: Union[float, Array, Initializer, Callable] = -68.,
+      V_th: Union[float, Array, Initializer, Callable] = -30.,
+      V_T: Union[float, Array, Initializer, Callable] = -59.9,
+      delta_T: Union[float, Array, Initializer, Callable] = 3.48,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      tau_ref: Union[float, Array, Initializer, Callable] = None,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       keep_size: bool = False,
       mode: Mode = normal,
       method: str = 'exp_auto',
@@ -564,19 +564,19 @@ class AdExIF(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -65.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -68.,
-      V_th: Union[float, Tensor, Initializer, Callable] = -30.,
-      V_T: Union[float, Tensor, Initializer, Callable] = -59.9,
-      delta_T: Union[float, Tensor, Initializer, Callable] = 3.48,
-      a: Union[float, Tensor, Initializer, Callable] = 1.,
-      b: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau_w: Union[float, Tensor, Initializer, Callable] = 30.,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -65.,
+      V_reset: Union[float, Array, Initializer, Callable] = -68.,
+      V_th: Union[float, Array, Initializer, Callable] = -30.,
+      V_T: Union[float, Array, Initializer, Callable] = -59.9,
+      delta_T: Union[float, Array, Initializer, Callable] = 3.48,
+      a: Union[float, Array, Initializer, Callable] = 1.,
+      b: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      tau_w: Union[float, Array, Initializer, Callable] = 30.,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      w_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       keep_size: bool = False,
       mode: Mode = normal,
@@ -723,16 +723,16 @@ class QuaIF(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -65.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -68.,
-      V_th: Union[float, Tensor, Initializer, Callable] = -30.,
-      V_c: Union[float, Tensor, Initializer, Callable] = -50.0,
-      c: Union[float, Tensor, Initializer, Callable] = .07,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau_ref: Union[float, Tensor, Initializer, Callable] = None,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -65.,
+      V_reset: Union[float, Array, Initializer, Callable] = -68.,
+      V_th: Union[float, Array, Initializer, Callable] = -30.,
+      V_c: Union[float, Array, Initializer, Callable] = -50.0,
+      c: Union[float, Array, Initializer, Callable] = .07,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      tau_ref: Union[float, Array, Initializer, Callable] = None,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       keep_size: bool = False,
       mode: Mode = normal,
       method: str = 'exp_auto',
@@ -891,18 +891,18 @@ class AdQuaIF(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -65.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -68.,
-      V_th: Union[float, Tensor, Initializer, Callable] = -30.,
-      V_c: Union[float, Tensor, Initializer, Callable] = -50.0,
-      a: Union[float, Tensor, Initializer, Callable] = 1.,
-      b: Union[float, Tensor, Initializer, Callable] = .1,
-      c: Union[float, Tensor, Initializer, Callable] = .07,
-      tau: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau_w: Union[float, Tensor, Initializer, Callable] = 10.,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -65.,
+      V_reset: Union[float, Array, Initializer, Callable] = -68.,
+      V_th: Union[float, Array, Initializer, Callable] = -30.,
+      V_c: Union[float, Array, Initializer, Callable] = -50.0,
+      a: Union[float, Array, Initializer, Callable] = 1.,
+      b: Union[float, Array, Initializer, Callable] = .1,
+      c: Union[float, Array, Initializer, Callable] = .07,
+      tau: Union[float, Array, Initializer, Callable] = 10.,
+      tau_w: Union[float, Array, Initializer, Callable] = 10.,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      w_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       keep_size: bool = False,
       mode: Mode = normal,
@@ -1064,25 +1064,25 @@ class GIF(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -70.,
-      V_reset: Union[float, Tensor, Initializer, Callable] = -70.,
-      V_th_inf: Union[float, Tensor, Initializer, Callable] = -50.,
-      V_th_reset: Union[float, Tensor, Initializer, Callable] = -60.,
-      R: Union[float, Tensor, Initializer, Callable] = 20.,
-      tau: Union[float, Tensor, Initializer, Callable] = 20.,
-      a: Union[float, Tensor, Initializer, Callable] = 0.,
-      b: Union[float, Tensor, Initializer, Callable] = 0.01,
-      k1: Union[float, Tensor, Initializer, Callable] = 0.2,
-      k2: Union[float, Tensor, Initializer, Callable] = 0.02,
-      R1: Union[float, Tensor, Initializer, Callable] = 0.,
-      R2: Union[float, Tensor, Initializer, Callable] = 1.,
-      A1: Union[float, Tensor, Initializer, Callable] = 0.,
-      A2: Union[float, Tensor, Initializer, Callable] = 0.,
-      V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-70.),
-      I1_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      I2_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      Vth_initializer: Union[Initializer, Callable, Tensor] = OneInit(-50.),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -70.,
+      V_reset: Union[float, Array, Initializer, Callable] = -70.,
+      V_th_inf: Union[float, Array, Initializer, Callable] = -50.,
+      V_th_reset: Union[float, Array, Initializer, Callable] = -60.,
+      R: Union[float, Array, Initializer, Callable] = 20.,
+      tau: Union[float, Array, Initializer, Callable] = 20.,
+      a: Union[float, Array, Initializer, Callable] = 0.,
+      b: Union[float, Array, Initializer, Callable] = 0.01,
+      k1: Union[float, Array, Initializer, Callable] = 0.2,
+      k2: Union[float, Array, Initializer, Callable] = 0.02,
+      R1: Union[float, Array, Initializer, Callable] = 0.,
+      R2: Union[float, Array, Initializer, Callable] = 1.,
+      A1: Union[float, Array, Initializer, Callable] = 0.,
+      A2: Union[float, Array, Initializer, Callable] = 0.,
+      V_initializer: Union[Initializer, Callable, Array] = OneInit(-70.),
+      I1_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      I2_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      Vth_initializer: Union[Initializer, Callable, Array] = OneInit(-50.),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       keep_size: bool = False,
       name: str = None,
@@ -1235,18 +1235,18 @@ def __init__(
       keep_size: bool = False,
 
       # model parameters
-      V_rest: Union[float, Tensor, Initializer, Callable] = -70.,
-      V_th: Union[float, Tensor, Initializer, Callable] = -60.,
-      R: Union[float, Tensor, Initializer, Callable] = 1.,
-      beta: Union[float, Tensor, Initializer, Callable] = 1.6,
-      tau: Union[float, Tensor, Initializer, Callable] = 20.,
-      tau_a: Union[float, Tensor, Initializer, Callable] = 2000.,
-      tau_ref: Union[float, Tensor, Initializer, Callable] = None,
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      V_rest: Union[float, Array, Initializer, Callable] = -70.,
+      V_th: Union[float, Array, Initializer, Callable] = -60.,
+      R: Union[float, Array, Initializer, Callable] = 1.,
+      beta: Union[float, Array, Initializer, Callable] = 1.6,
+      tau: Union[float, Array, Initializer, Callable] = 20.,
+      tau_a: Union[float, Array, Initializer, Callable] = 2000.,
+      tau_ref: Union[float, Array, Initializer, Callable] = None,
+      noise: Union[float, Array, Initializer, Callable] = None,
 
       # initializers
-      V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-70.),
-      a_initializer: Union[Initializer, Callable, Tensor] = OneInit(-50.),
+      V_initializer: Union[Initializer, Callable, Array] = OneInit(-70.),
+      a_initializer: Union[Initializer, Callable, Array] = OneInit(-50.),
 
       # parameter for training
       spike_fun: Callable = bm.spike_with_linear_grad,
@@ -1433,15 +1433,15 @@ class Izhikevich(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      a: Union[float, Tensor, Initializer, Callable] = 0.02,
-      b: Union[float, Tensor, Initializer, Callable] = 0.20,
-      c: Union[float, Tensor, Initializer, Callable] = -65.,
-      d: Union[float, Tensor, Initializer, Callable] = 8.,
-      V_th: Union[float, Tensor, Initializer, Callable] = 30.,
-      tau_ref: Union[float, Tensor, Initializer, Callable] = None,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      u_initializer: Union[Initializer, Callable, Tensor] = OneInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      a: Union[float, Array, Initializer, Callable] = 0.02,
+      b: Union[float, Array, Initializer, Callable] = 0.20,
+      c: Union[float, Array, Initializer, Callable] = -65.,
+      d: Union[float, Array, Initializer, Callable] = 8.,
+      V_th: Union[float, Array, Initializer, Callable] = 30.,
+      tau_ref: Union[float, Array, Initializer, Callable] = None,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      u_initializer: Union[Initializer, Callable, Array] = OneInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       mode: Mode = normal,
       spike_fun: Callable = bm.spike_with_sigmoid_grad,
@@ -1658,18 +1658,18 @@ class HindmarshRose(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      a: Union[float, Tensor, Initializer, Callable] = 1.,
-      b: Union[float, Tensor, Initializer, Callable] = 3.,
-      c: Union[float, Tensor, Initializer, Callable] = 1.,
-      d: Union[float, Tensor, Initializer, Callable] = 5.,
-      r: Union[float, Tensor, Initializer, Callable] = 0.01,
-      s: Union[float, Tensor, Initializer, Callable] = 4.,
-      V_rest: Union[float, Tensor, Initializer, Callable] = -1.6,
-      V_th: Union[float, Tensor, Initializer, Callable] = 1.0,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      y_initializer: Union[Initializer, Callable, Tensor] = OneInit(-10.),
-      z_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      a: Union[float, Array, Initializer, Callable] = 1.,
+      b: Union[float, Array, Initializer, Callable] = 3.,
+      c: Union[float, Array, Initializer, Callable] = 1.,
+      d: Union[float, Array, Initializer, Callable] = 5.,
+      r: Union[float, Array, Initializer, Callable] = 0.01,
+      s: Union[float, Array, Initializer, Callable] = 4.,
+      V_rest: Union[float, Array, Initializer, Callable] = -1.6,
+      V_th: Union[float, Array, Initializer, Callable] = 1.0,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      y_initializer: Union[Initializer, Callable, Array] = OneInit(-10.),
+      z_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       keep_size: bool = False,
       name: str = None,
@@ -1842,13 +1842,13 @@ class FHN(NeuGroup):
   def __init__(
       self,
       size: Shape,
-      a: Union[float, Tensor, Initializer, Callable] = 0.7,
-      b: Union[float, Tensor, Initializer, Callable] = 0.8,
-      tau: Union[float, Tensor, Initializer, Callable] = 12.5,
-      Vth: Union[float, Tensor, Initializer, Callable] = 1.8,
-      V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      a: Union[float, Array, Initializer, Callable] = 0.7,
+      b: Union[float, Array, Initializer, Callable] = 0.8,
+      tau: Union[float, Array, Initializer, Callable] = 12.5,
+      Vth: Union[float, Array, Initializer, Callable] = 1.8,
+      V_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      w_initializer: Union[Initializer, Callable, Array] = ZeroInit(),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       keep_size: bool = False,
       name: str = None,
diff --git a/brainpy/dyn/rates/populations.py b/brainpy/dyn/rates/populations.py
index e3ac7f474..0c3d90739 100644
--- a/brainpy/dyn/rates/populations.py
+++ b/brainpy/dyn/rates/populations.py
@@ -11,7 +11,7 @@
 from brainpy.modes import Mode, normal
 from brainpy.tools.checking import check_float, check_initializer
 from brainpy.tools.errors import check_error_in_jit
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 
 __all__ = [
   'RateModel',
@@ -69,24 +69,24 @@ def __init__(
       keep_size: bool = False,
 
       # fhn parameters
-      alpha: Union[float, Tensor, Initializer, Callable] = 3.0,
-      beta: Union[float, Tensor, Initializer, Callable] = 4.0,
-      gamma: Union[float, Tensor, Initializer, Callable] = -1.5,
-      delta: Union[float, Tensor, Initializer, Callable] = 0.0,
-      epsilon: Union[float, Tensor, Initializer, Callable] = 0.5,
-      tau: Union[float, Tensor, Initializer, Callable] = 20.0,
+      alpha: Union[float, Array, Initializer, Callable] = 3.0,
+      beta: Union[float, Array, Initializer, Callable] = 4.0,
+      gamma: Union[float, Array, Initializer, Callable] = -1.5,
+      delta: Union[float, Array, Initializer, Callable] = 0.0,
+      epsilon: Union[float, Array, Initializer, Callable] = 0.5,
+      tau: Union[float, Array, Initializer, Callable] = 20.0,
 
       # noise parameters
-      x_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
-      y_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
+      x_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
+      y_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
 
       # other parameters
-      x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
-      y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
+      x_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
+      y_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
       method: str = 'exp_auto',
       name: str = None,
 
@@ -251,24 +251,24 @@ def __init__(
       keep_size: bool = False,
 
       # model parameters
-      a: Union[float, Tensor, Initializer, Callable] = 0.7,
-      b: Union[float, Tensor, Initializer, Callable] = 0.8,
-      delay: Union[float, Tensor, Initializer, Callable] = 10.,
-      tau: Union[float, Tensor, Initializer, Callable] = 12.5,
-      mu: Union[float, Tensor, Initializer, Callable] = 1.6886,
-      v0: Union[float, Tensor, Initializer, Callable] = -1,
+      a: Union[float, Array, Initializer, Callable] = 0.7,
+      b: Union[float, Array, Initializer, Callable] = 0.8,
+      delay: Union[float, Array, Initializer, Callable] = 10.,
+      tau: Union[float, Array, Initializer, Callable] = 12.5,
+      mu: Union[float, Array, Initializer, Callable] = 1.6886,
+      v0: Union[float, Array, Initializer, Callable] = -1,
 
       # noise parameters
-      x_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
-      y_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
+      x_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
+      y_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
 
       # other parameters
-      x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
-      y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
+      x_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
+      y_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
       method: str = 'exp_auto',
       name: str = None,
       dt: float = None,
@@ -451,22 +451,22 @@ def __init__(
       keep_size: bool = False,
 
       # model parameters
-      tau: Union[float, Tensor, Initializer, Callable] = 1.,
-      eta: Union[float, Tensor, Initializer, Callable] = -5.0,
-      delta: Union[float, Tensor, Initializer, Callable] = 1.0,
-      J: Union[float, Tensor, Initializer, Callable] = 15.,
+      tau: Union[float, Array, Initializer, Callable] = 1.,
+      eta: Union[float, Array, Initializer, Callable] = -5.0,
+      delta: Union[float, Array, Initializer, Callable] = 1.0,
+      J: Union[float, Array, Initializer, Callable] = 15.,
 
       # noise parameters
-      x_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
-      y_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
+      x_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
+      y_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
 
       # other parameters
-      x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
-      y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05),
+      x_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
+      y_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.05),
       method: str = 'exp_auto',
       name: str = None,
 
@@ -594,20 +594,20 @@ def __init__(
       keep_size: bool = False,
 
       # model parameters
-      a: Union[float, Tensor, Initializer, Callable] = 0.25,
-      w: Union[float, Tensor, Initializer, Callable] = 0.2,
+      a: Union[float, Array, Initializer, Callable] = 0.25,
+      w: Union[float, Array, Initializer, Callable] = 0.2,
 
       # noise parameters
-      x_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
-      y_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
+      x_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
+      y_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
 
       # other parameters
-      x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.5),
-      y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.5),
+      x_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.5),
+      y_initializer: Union[Initializer, Callable, Array] = Uniform(0, 0.5),
       method: str = 'exp_auto',
       name: str = None,
 
@@ -732,35 +732,35 @@ def __init__(
       keep_size: bool = False,
 
       # Excitatory parameters
-      E_tau: Union[float, Tensor, Initializer, Callable] = 1.,  # excitatory time constant
-      E_a: Union[float, Tensor, Initializer, Callable] = 1.2,  # excitatory gain
-      E_theta: Union[float, Tensor, Initializer, Callable] = 2.8,  # excitatory firing threshold
+      E_tau: Union[float, Array, Initializer, Callable] = 1.,  # excitatory time constant
+      E_a: Union[float, Array, Initializer, Callable] = 1.2,  # excitatory gain
+      E_theta: Union[float, Array, Initializer, Callable] = 2.8,  # excitatory firing threshold
 
       # Inhibitory parameters
-      I_tau: Union[float, Tensor, Initializer, Callable] = 1.,  # inhibitory time constant
-      I_a: Union[float, Tensor, Initializer, Callable] = 1.,  # inhibitory gain
-      I_theta: Union[float, Tensor, Initializer, Callable] = 4.0,  # inhibitory firing threshold
+      I_tau: Union[float, Array, Initializer, Callable] = 1.,  # inhibitory time constant
+      I_a: Union[float, Array, Initializer, Callable] = 1.,  # inhibitory gain
+      I_theta: Union[float, Array, Initializer, Callable] = 4.0,  # inhibitory firing threshold
 
       # connection parameters
-      wEE: Union[float, Tensor, Initializer, Callable] = 12.,  # local E-E coupling
-      wIE: Union[float, Tensor, Initializer, Callable] = 4.,  # local E-I coupling
-      wEI: Union[float, Tensor, Initializer, Callable] = 13.,  # local I-E coupling
-      wII: Union[float, Tensor, Initializer, Callable] = 11.,  # local I-I coupling
+      wEE: Union[float, Array, Initializer, Callable] = 12.,  # local E-E coupling
+      wIE: Union[float, Array, Initializer, Callable] = 4.,  # local E-I coupling
+      wEI: Union[float, Array, Initializer, Callable] = 13.,  # local I-E coupling
+      wII: Union[float, Array, Initializer, Callable] = 11.,  # local I-I coupling
 
       # Refractory parameter
-      r: Union[float, Tensor, Initializer, Callable] = 1.,
+      r: Union[float, Array, Initializer, Callable] = 1.,
 
       # noise parameters
-      x_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      x_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
-      y_ou_mean: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_sigma: Union[float, Tensor, Initializer, Callable] = 0.0,
-      y_ou_tau: Union[float, Tensor, Initializer, Callable] = 5.0,
+      x_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      x_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
+      y_ou_mean: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_sigma: Union[float, Array, Initializer, Callable] = 0.0,
+      y_ou_tau: Union[float, Array, Initializer, Callable] = 5.0,
 
       # state initializer
-      x_initializer: Union[Initializer, Callable, Tensor] = Uniform(max_val=0.05),
-      y_initializer: Union[Initializer, Callable, Tensor] = Uniform(max_val=0.05),
+      x_initializer: Union[Initializer, Callable, Array] = Uniform(max_val=0.05),
+      y_initializer: Union[Initializer, Callable, Array] = Uniform(max_val=0.05),
 
       # other parameters
       method: str = 'exp_euler_auto',
@@ -910,14 +910,14 @@ class ThresholdLinearModel(RateModel):
   def __init__(
       self,
       size: Shape,
-      tau_e: Union[float, Callable, Initializer, Tensor] = 2e-2,
-      tau_i: Union[float, Callable, Initializer, Tensor] = 1e-2,
-      beta_e: Union[float, Callable, Initializer, Tensor] = .066,
-      beta_i: Union[float, Callable, Initializer, Tensor] = .351,
-      noise_e: Union[float, Callable, Initializer, Tensor] = 0.,
-      noise_i: Union[float, Callable, Initializer, Tensor] = 0.,
-      e_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
-      i_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(),
+      tau_e: Union[float, Callable, Initializer, Array] = 2e-2,
+      tau_i: Union[float, Callable, Initializer, Array] = 1e-2,
+      beta_e: Union[float, Callable, Initializer, Array] = .066,
+      beta_i: Union[float, Callable, Initializer, Array] = .351,
+      noise_e: Union[float, Callable, Initializer, Array] = 0.,
+      noise_i: Union[float, Callable, Initializer, Array] = 0.,
+      e_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
+      i_initializer: Union[Array, Callable, Initializer] = ZeroInit(),
       seed: int = None,
       keep_size: bool = False,
       name: str = None,
diff --git a/brainpy/dyn/runners.py b/brainpy/dyn/runners.py
index 4dff85db7..0c1be7df4 100644
--- a/brainpy/dyn/runners.py
+++ b/brainpy/dyn/runners.py
@@ -17,7 +17,7 @@
 from brainpy.running.runner import Runner
 from brainpy.tools.checking import check_float, serialize_kwargs
 from brainpy.tools.others.dicts import DotDict
-from brainpy.types import Tensor, Output, Monitor
+from brainpy.types import Array, Output, Monitor
 
 __all__ = [
   'DSRunner',
@@ -350,7 +350,7 @@ def reset_state(self):
   def predict(
       self,
       duration: Union[float, int] = None,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]] = None,
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]] = None,
       inputs_are_batching: bool = False,
       reset_state: bool = False,
       shared_args: Dict = None,
@@ -368,7 +368,7 @@ def predict(
     ----------
     duration: int, float
       The simulation time length.
-    inputs: Tensor, dict of Tensor, sequence of Tensor
+    inputs: Array, dict of Array, sequence of Array
       The input data. If ``inputs_are_batching=True``, ``inputs`` must be a
       PyTree of data with two dimensions: `(num_sample, num_time, ...)`.
       Otherwise, the ``inputs`` should be a PyTree of data with one dimension:
@@ -387,7 +387,7 @@ def predict(
 
     Returns
     -------
-    output: Tensor, dict, sequence
+    output: Array, dict, sequence
       The model output.
     """
 
@@ -475,7 +475,7 @@ def run(self, *args, **kwargs) -> Output:
     ----------
     duration: int, float
       The simulation time length.
-    inputs: Tensor, dict of Tensor, sequence of Tensor
+    inputs: Array, dict of Array, sequence of Array
       The input data. If ``inputs_are_batching=True``, ``inputs`` must be a
       PyTree of data with two dimensions: `(num_sample, num_time, ...)`.
       Otherwise, the ``inputs`` should be a PyTree of data with one dimension:
@@ -494,7 +494,7 @@ def run(self, *args, **kwargs) -> Output:
 
     Returns
     -------
-    output: Tensor, dict, sequence
+    output: Array, dict, sequence
       The model output.
     """
     return self.predict(*args, **kwargs)
diff --git a/brainpy/dyn/synapses/abstract_models.py b/brainpy/dyn/synapses/abstract_models.py
index 60ee6fc57..711d05be9 100644
--- a/brainpy/dyn/synapses/abstract_models.py
+++ b/brainpy/dyn/synapses/abstract_models.py
@@ -11,7 +11,7 @@
 from brainpy.initialize import Initializer, variable
 from brainpy.integrators import odeint, JointEq
 from brainpy.modes import Mode, BatchingMode, normal
-from brainpy.types import Tensor
+from brainpy.types import Array
 from ..synouts import CUBA, MgBlock
 
 __all__ = [
@@ -87,12 +87,12 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = CUBA(target_var='V'),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'sparse',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[float, Tensor, Initializer, Callable] = None,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[float, Array, Initializer, Callable] = None,
       post_ref_key: str = None,
 
       # other parameters
@@ -264,13 +264,13 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = CUBA(),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'sparse',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau: Union[float, Tensor] = 8.0,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau: Union[float, Array] = 8.0,
       method: str = 'exp_auto',
 
       # other parameters
@@ -447,14 +447,14 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       stp: Optional[SynSTP] = None,
       output: SynOut = CUBA(),
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau_decay: Union[float, Tensor] = 10.0,
-      tau_rise: Union[float, Tensor] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      tau_decay: Union[float, Array] = 10.0,
+      tau_rise: Union[float, Array] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
 
       # other parameters
@@ -627,13 +627,13 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = CUBA(),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau_decay: Union[float, Tensor] = 10.0,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau_decay: Union[float, Array] = 10.0,
       method: str = 'exp_auto',
 
       # other parameters
@@ -788,15 +788,15 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = MgBlock(E=0., alpha=0.062, beta=3.57, cc_Mg=1.2),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.15,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau_decay: Union[float, Tensor] = 100.,
-      a: Union[float, Tensor] = 0.5,
-      tau_rise: Union[float, Tensor] = 2.,
+      g_max: Union[float, Array, Initializer, Callable] = 0.15,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau_decay: Union[float, Array] = 100.,
+      a: Union[float, Array] = 0.5,
+      tau_rise: Union[float, Array] = 2.,
       method: str = 'exp_auto',
 
       # other parameters
diff --git a/brainpy/dyn/synapses/biological_models.py b/brainpy/dyn/synapses/biological_models.py
index 3ebfcb2f7..a6db1fb7a 100644
--- a/brainpy/dyn/synapses/biological_models.py
+++ b/brainpy/dyn/synapses/biological_models.py
@@ -12,7 +12,7 @@
 from brainpy.dyn.synouts import COBA, MgBlock
 from brainpy.initialize import Initializer, variable
 from brainpy.integrators import odeint, JointEq
-from brainpy.types import Tensor
+from brainpy.types import Array
 from brainpy.modes import Mode, BatchingMode, TrainingMode, normal, batching, training
 
 __all__ = [
@@ -139,12 +139,12 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = COBA(E=0.),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.42,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
+      g_max: Union[float, Array, Initializer, Callable] = 0.42,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
       alpha: float = 0.98,
       beta: float = 0.18,
       T: float = 0.5,
@@ -313,16 +313,16 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = COBA(E=-80.),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.04,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      alpha: Union[float, Tensor] = 0.53,
-      beta: Union[float, Tensor] = 0.18,
-      T: Union[float, Tensor] = 1.,
-      T_duration: Union[float, Tensor] = 1.,
+      g_max: Union[float, Array, Initializer, Callable] = 0.04,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      alpha: Union[float, Array] = 0.53,
+      beta: Union[float, Array] = 0.18,
+      T: Union[float, Array] = 1.,
+      T_duration: Union[float, Array] = 1.,
       method: str = 'exp_auto',
 
       # other parameters
@@ -331,7 +331,7 @@ def __init__(
       stop_spike_gradient: bool = False,
 
       # deprecated
-      E: Union[float, Tensor] = None,
+      E: Union[float, Array] = None,
   ):
     super(GABAa, self).__init__(pre=pre,
                                 post=post,
@@ -476,18 +476,18 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       output: SynOut = MgBlock(E=0.),
       stp: Optional[SynSTP] = None,
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 0.15,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      alpha1: Union[float, Tensor] = 2.,
-      beta1: Union[float, Tensor] = 0.01,
-      alpha2: Union[float, Tensor] = 1.,
-      beta2: Union[float, Tensor] = 0.5,
-      T_0: Union[float, Tensor] = 1.,
-      T_dur: Union[float, Tensor] = 0.5,
+      g_max: Union[float, Array, Initializer, Callable] = 0.15,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      alpha1: Union[float, Array] = 2.,
+      beta1: Union[float, Array] = 0.01,
+      alpha2: Union[float, Array] = 1.,
+      beta2: Union[float, Array] = 0.5,
+      T_0: Union[float, Array] = 1.,
+      T_dur: Union[float, Array] = 0.5,
       method: str = 'exp_auto',
 
       # other parameters
diff --git a/brainpy/dyn/synapses/compat.py b/brainpy/dyn/synapses/compat.py
index 12c7d88dd..38898d16c 100644
--- a/brainpy/dyn/synapses/compat.py
+++ b/brainpy/dyn/synapses/compat.py
@@ -5,7 +5,7 @@
 from brainpy.connect import TwoEndConnector
 from brainpy.dyn.base import NeuGroup
 from brainpy.initialize import Initializer
-from brainpy.types import Tensor
+from brainpy.types import Array
 from .abstract_models import Delta, Exponential, DualExponential, NMDA
 from ..synouts import COBA, CUBA
 
@@ -33,10 +33,10 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'sparse',
-      weights: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[float, Tensor, Initializer, Callable] = None,
+      weights: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[float, Array, Initializer, Callable] = None,
       post_input_key: str = 'V',
       post_has_ref: bool = False,
       name: str = None,
@@ -66,11 +66,11 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'sparse',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau: Union[float, Tensor] = 8.0,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau: Union[float, Array] = 8.0,
       name: str = None,
       method: str = 'exp_auto',
   ):
@@ -98,15 +98,15 @@ def __init__(
       pre: NeuGroup,
       post: NeuGroup,
       # connection
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'sparse',
       # connection strength
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
       # synapse parameter
-      tau: Union[float, Tensor] = 8.0,
-      E: Union[float, Tensor] = 0.,
+      tau: Union[float, Array] = 8.0,
+      E: Union[float, Array] = 0.,
       # synapse delay
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
       # others
       method: str = 'exp_auto',
       name: str = None
@@ -135,12 +135,12 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      tau_decay: Union[float, Tensor] = 10.0,
-      tau_rise: Union[float, Tensor] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      tau_decay: Union[float, Array] = 10.0,
+      tau_rise: Union[float, Array] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None
   ):
@@ -170,13 +170,13 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau_decay: Union[float, Tensor] = 10.0,
-      tau_rise: Union[float, Tensor] = 1.,
-      E: Union[float, Tensor] = 0.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau_decay: Union[float, Array] = 10.0,
+      tau_rise: Union[float, Array] = 1.,
+      E: Union[float, Array] = 0.,
       method: str = 'exp_auto',
       name: str = None
   ):
@@ -205,11 +205,11 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau_decay: Union[float, Tensor] = 10.0,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau_decay: Union[float, Array] = 10.0,
       method: str = 'exp_auto',
       name: str = None
   ):
@@ -237,12 +237,12 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       conn_type: str = 'dense',
-      g_max: Union[float, Tensor, Callable, Initializer] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
-      tau_decay: Union[float, Tensor] = 10.0,
-      E: Union[float, Tensor] = 0.,
+      g_max: Union[float, Array, Callable, Initializer] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
+      tau_decay: Union[float, Array] = 10.0,
+      E: Union[float, Array] = 0.,
       method: str = 'exp_auto',
       name: str = None
   ):
diff --git a/brainpy/dyn/synapses/delay_couplings.py b/brainpy/dyn/synapses/delay_couplings.py
index 76144c418..84f1db6ef 100644
--- a/brainpy/dyn/synapses/delay_couplings.py
+++ b/brainpy/dyn/synapses/delay_couplings.py
@@ -12,7 +12,7 @@
 from brainpy.dyn.neurons.input_groups import InputGroup, OutputGroup
 from brainpy.modes import Mode, TrainingMode, normal
 from brainpy.tools.checking import check_sequence
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'DelayCoupling',
@@ -44,10 +44,10 @@ def __init__(
       self,
       delay_var: bm.Variable,
       var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
-      conn_mat: Tensor,
+      conn_mat: Array,
       required_shape: Tuple[int, ...],
-      delay_steps: Optional[Union[int, Tensor, Initializer, Callable]] = None,
-      initial_delay_data: Union[Initializer, Callable, Tensor, float, int, bool] = None,
+      delay_steps: Optional[Union[int, Array, Initializer, Callable]] = None,
+      initial_delay_data: Union[Initializer, Callable, Array, float, int, bool] = None,
       name: str = None,
       mode: Mode = normal,
   ):
@@ -160,9 +160,9 @@ def __init__(
       coupling_var1: bm.Variable,
       coupling_var2: bm.Variable,
       var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
-      conn_mat: Tensor,
-      delay_steps: Optional[Union[int, Tensor, Initializer, Callable]] = None,
-      initial_delay_data: Union[Initializer, Callable, Tensor, float, int, bool] = None,
+      conn_mat: Array,
+      delay_steps: Optional[Union[int, Array, Initializer, Callable]] = None,
+      initial_delay_data: Union[Initializer, Callable, Array, float, int, bool] = None,
       name: str = None,
       mode: Mode = normal,
   ):
@@ -252,9 +252,9 @@ def __init__(
       self,
       coupling_var: bm.Variable,
       var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
-      conn_mat: Tensor,
-      delay_steps: Optional[Union[int, Tensor, Initializer, Callable]] = None,
-      initial_delay_data: Union[Initializer, Callable, Tensor, float, int, bool] = None,
+      conn_mat: Array,
+      delay_steps: Optional[Union[int, Array, Initializer, Callable]] = None,
+      initial_delay_data: Union[Initializer, Callable, Array, float, int, bool] = None,
       name: str = None,
       mode: Mode = normal,
   ):
diff --git a/brainpy/dyn/synapses/gap_junction.py b/brainpy/dyn/synapses/gap_junction.py
index 46e304078..1b4027042 100644
--- a/brainpy/dyn/synapses/gap_junction.py
+++ b/brainpy/dyn/synapses/gap_junction.py
@@ -6,7 +6,7 @@
 from brainpy.connect import TwoEndConnector
 from brainpy.dyn.base import NeuGroup, SynOut, SynSTP, TwoEndConn
 from brainpy.initialize import Initializer, parameter
-from brainpy.types import Tensor
+from brainpy.types import Array
 from ..synouts import CUBA
 
 __all__ = [
@@ -19,9 +19,9 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
       comp_method: str = 'dense',
-      g_max: Union[float, Tensor, Initializer, Callable] = 1.,
+      g_max: Union[float, Array, Initializer, Callable] = 1.,
       name: str = None,
   ):
     super(GapJunction, self).__init__(pre=pre,
diff --git a/brainpy/dyn/synapses/learning_rules.py b/brainpy/dyn/synapses/learning_rules.py
index aeae458d6..fb6a26147 100644
--- a/brainpy/dyn/synapses/learning_rules.py
+++ b/brainpy/dyn/synapses/learning_rules.py
@@ -7,7 +7,7 @@
 from brainpy.dyn.base import NeuGroup, TwoEndConn
 from brainpy.initialize import Initializer, delay as init_delay
 from brainpy.integrators import odeint, JointEq
-from brainpy.types import Tensor, Parameter
+from brainpy.types import Array, Parameter
 
 __all__ = [
   'STP'
@@ -176,13 +176,13 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]],
-      U: Union[float, Tensor] = 0.15,
-      tau_f: Union[float, Tensor] = 1500.,
-      tau_d: Union[float, Tensor] = 200.,
-      tau: Union[float, Tensor] = 8.,
-      A: Union[float, Tensor] = 1.,
-      delay_step: Union[int, Tensor, Initializer, Callable] = None,
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]],
+      U: Union[float, Array] = 0.15,
+      tau_f: Union[float, Array] = 1500.,
+      tau_d: Union[float, Array] = 200.,
+      tau: Union[float, Array] = 8.,
+      A: Union[float, Array] = 1.,
+      delay_step: Union[int, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None
   ):
diff --git a/brainpy/dyn/synouts/conductances.py b/brainpy/dyn/synouts/conductances.py
index 04644f451..6d187ac52 100644
--- a/brainpy/dyn/synouts/conductances.py
+++ b/brainpy/dyn/synouts/conductances.py
@@ -5,7 +5,7 @@
 from brainpy.math import Variable
 from brainpy.dyn.base import SynOut
 from brainpy.initialize import parameter, Initializer
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'COBA',
@@ -68,7 +68,7 @@ class COBA(SynOut):
 
   def __init__(
       self,
-      E: Union[float, Tensor, Callable, Initializer] = 0.,
+      E: Union[float, Array, Callable, Initializer] = 0.,
       target_var: Optional[Union[str, Variable]] = 'input',
       membrane_var: Union[str, Variable] = 'V',
       name: str = None,
diff --git a/brainpy/dyn/synouts/ions.py b/brainpy/dyn/synouts/ions.py
index 4c73c8efe..f781e3464 100644
--- a/brainpy/dyn/synouts/ions.py
+++ b/brainpy/dyn/synouts/ions.py
@@ -5,7 +5,7 @@
 import brainpy.math as bm
 from brainpy.dyn.base import SynOut
 from brainpy.initialize import parameter, Initializer
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'MgBlock',
@@ -45,10 +45,10 @@ class MgBlock(SynOut):
 
   def __init__(
       self,
-      E: Union[float, Tensor, Callable, Initializer] = 0.,
-      cc_Mg: Union[float, Tensor, Callable, Initializer] = 1.2,
-      alpha: Union[float, Tensor, Callable, Initializer] = 0.062,
-      beta: Union[float, Tensor, Callable, Initializer] = 3.57,
+      E: Union[float, Array, Callable, Initializer] = 0.,
+      cc_Mg: Union[float, Array, Callable, Initializer] = 1.2,
+      alpha: Union[float, Array, Callable, Initializer] = 0.062,
+      beta: Union[float, Array, Callable, Initializer] = 3.57,
       target_var: Optional[Union[str, bm.Variable]] = 'input',
       membrane_var: Union[str, bm.Variable] = 'V',
       name: str = None,
diff --git a/brainpy/dyn/synplast/short_term_plasticity.py b/brainpy/dyn/synplast/short_term_plasticity.py
index 1c9da7b26..2c89466ef 100644
--- a/brainpy/dyn/synplast/short_term_plasticity.py
+++ b/brainpy/dyn/synplast/short_term_plasticity.py
@@ -6,7 +6,7 @@
 from brainpy.dyn.base import SynSTP
 from brainpy.integrators import odeint, JointEq
 from brainpy.tools.checking import check_float
-from brainpy.types import Tensor
+from brainpy.types import Array
 from brainpy.initialize import variable
 
 __all__ = [
@@ -133,9 +133,9 @@ class STP(SynSTP):
 
   def __init__(
       self,
-      U: Union[float, Tensor] = 0.15,
-      tau_f: Union[float, Tensor] = 1500.,
-      tau_d: Union[float, Tensor] = 200.,
+      U: Union[float, Array] = 0.15,
+      tau_f: Union[float, Array] = 1500.,
+      tau_d: Union[float, Array] = 200.,
       method: str = 'exp_auto',
       name: str = None
   ):
diff --git a/brainpy/initialize/generic.py b/brainpy/initialize/generic.py
index b818ccd1d..4dd58f844 100644
--- a/brainpy/initialize/generic.py
+++ b/brainpy/initialize/generic.py
@@ -7,7 +7,7 @@
 
 import brainpy.math as bm
 from brainpy.tools.others import to_size
-from brainpy.types import Shape, Tensor
+from brainpy.types import Shape, Array
 from brainpy.modes import Mode, NormalMode, BatchingMode
 from .base import Initializer
 
@@ -82,7 +82,7 @@ def init_param(
 
 
 def variable(
-    data: Union[Callable, Tensor],
+    data: Union[Callable, Array],
     batch_size_or_mode: Optional[Union[int, bool, Mode]] = None,
     var_shape: Shape = None,
     batch_axis: int = 0,
diff --git a/brainpy/integrators/fde/Caputo.py b/brainpy/integrators/fde/Caputo.py
index fd36e69a4..18f68e9c1 100644
--- a/brainpy/integrators/fde/Caputo.py
+++ b/brainpy/integrators/fde/Caputo.py
@@ -17,7 +17,7 @@
 from brainpy.tools.errors import check_error_in_jit
 from .base import FDEIntegrator
 from .generic import register_fde_integrator, get_supported_methods
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'CaputoEuler',
@@ -115,9 +115,9 @@ class CaputoEuler(FDEIntegrator):
   def __init__(
       self,
       f: Callable,
-      alpha: Union[float, Sequence[float], Tensor],
+      alpha: Union[float, Sequence[float], Array],
       num_memory: int,
-      inits: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inits: Union[Array, Sequence[Array], Dict[str, Array]],
       dt: float = None,
       name: str = None,
       state_delays: Dict[str, Union[bm.LengthDelay, bm.TimeDelay]] = None,
@@ -308,9 +308,9 @@ class CaputoL1Schema(FDEIntegrator):
   def __init__(
       self,
       f: Callable,
-      alpha: Union[float, Sequence[float], Tensor],
+      alpha: Union[float, Sequence[float], Array],
       num_memory: int,
-      inits: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inits: Union[Array, Sequence[Array], Dict[str, Array]],
       dt: float = None,
       name: str = None,
       state_delays: Dict[str, Union[bm.LengthDelay, bm.TimeDelay]] = None,
diff --git a/brainpy/losses/comparison.py b/brainpy/losses/comparison.py
index 0626e7102..f485513a6 100644
--- a/brainpy/losses/comparison.py
+++ b/brainpy/losses/comparison.py
@@ -16,7 +16,7 @@
 from jax.lax import scan
 
 import brainpy.math as bm
-from brainpy.types import Tensor
+from brainpy.types import Array
 from .utils import _return, _multi_return, _is_leaf
 
 __all__ = [
@@ -43,7 +43,7 @@ def cross_entropy_loss(predicts, targets, weight=None, reduction='mean'):
   r"""This criterion combines ``LogSoftmax`` and `NLLLoss`` in one single class.
 
   It is useful when training a classification problem with `C` classes.
-  If provided, the optional argument :attr:`weight` should be a 1D `Tensor`
+  If provided, the optional argument :attr:`weight` should be a 1D `Array`
   assigning weight to each of the classes. This is particularly useful when
   you have an unbalanced training set.
 
@@ -72,7 +72,7 @@ def cross_entropy_loss(predicts, targets, weight=None, reduction='mean'):
 
   Parameters
   ----------
-  predicts : Tensor
+  predicts : Array
     :math:`(N, C)` where `C = number of classes`, or
     :math:`(d_1, d_2, ..., d_K, N, C)` with :math:`K \geq 1`
     in the case of `K`-dimensional loss.
@@ -421,13 +421,13 @@ def loss(pred, tar):
 
 
 def ctc_loss_with_forward_probs(
-    logits: Tensor,
-    logit_paddings: Tensor,
-    labels: Tensor,
-    label_paddings: Tensor,
+    logits: Array,
+    logit_paddings: Array,
+    labels: Array,
+    label_paddings: Array,
     blank_id: int = 0,
     log_epsilon: float = -1e5
-) -> Tuple[Tensor, Tensor, Tensor]:
+) -> Tuple[Array, Array, Array]:
   r"""Computes CTC loss and CTC forward-probabilities.
   The CTC loss is a loss function based on log-likelihoods of the model that
   introduces a special blank symbol :math:`\phi` to represent variable-length
@@ -545,12 +545,12 @@ def loop_body(prev, x):
   return per_seq_loss, logalpha_phi, logalpha_emit
 
 
-def ctc_loss(logits: Tensor,
-             logit_paddings: Tensor,
-             labels: Tensor,
-             label_paddings: Tensor,
+def ctc_loss(logits: Array,
+             logit_paddings: Array,
+             labels: Array,
+             label_paddings: Array,
              blank_id: int = 0,
-             log_epsilon: float = -1e5) -> Tensor:
+             log_epsilon: float = -1e5) -> Array:
   """Computes CTC loss.
   See docstring for ``ctc_loss_with_forward_probs`` for details.
   Args:
diff --git a/brainpy/math/delayvars.py b/brainpy/math/delayvars.py
index 5fdeb3c18..0926cf97e 100644
--- a/brainpy/math/delayvars.py
+++ b/brainpy/math/delayvars.py
@@ -271,7 +271,7 @@ class LengthDelay(AbstractDelay):
     The initial delay data.
   delay_len: int
     The maximum delay length.
-  initial_delay_data: Tensor
+  initial_delay_data: Array
     The delay data.
   name: str
     The delay object name.
diff --git a/brainpy/math/operators/pre2post.py b/brainpy/math/operators/pre2post.py
index be6b8a40c..9f45d998c 100644
--- a/brainpy/math/operators/pre2post.py
+++ b/brainpy/math/operators/pre2post.py
@@ -10,7 +10,7 @@
 from brainpy.errors import MathError
 from brainpy.math.jaxarray import JaxArray
 from brainpy.math.numpy_ops import as_device_array
-from brainpy.types import Tensor
+from brainpy.types import Array
 from .pre2syn import pre2syn
 from .syn2post import syn2post_mean
 from .utils import _check_brainpylib
@@ -42,10 +42,10 @@ def _raise_pre_ids_is_none(pre_ids):
                     f'(brainpy.math.ndim(pre_values) != 0).')
 
 
-def pre2post_event_sum(events: Tensor,
-                       pre2post: Tuple[Tensor, Tensor],
+def pre2post_event_sum(events: Array,
+                       pre2post: Tuple[Array, Array],
                        post_num: int,
-                       values: Union[float, Tensor] = 1.):
+                       values: Union[float, Array] = 1.):
   """The pre-to-post synaptic computation with event-driven summation.
 
   When ``values`` is a scalar, this function is equivalent to
@@ -77,13 +77,13 @@ def pre2post_event_sum(events: Tensor,
 
   Parameters
   ----------
-  events: Tensor
+  events: Array
     The events, must be bool.
-  pre2post: tuple of Tensor, tuple of Tensor
+  pre2post: tuple of Array, tuple of Array
     A tuple contains the connection information of pre-to-post.
   post_num: int
     The number of post-synaptic group.
-  values: float, Tensor
+  values: float, Array
     The value to make summation.
 
   Returns
@@ -100,10 +100,10 @@ def pre2post_event_sum(events: Tensor,
   return brainpylib.event_sum(events, (indices, idnptr), post_num, values)
 
 
-def pre2post_event_sum2(events: Tensor,
-                        pre2post: Tuple[Tensor, Tensor],
+def pre2post_event_sum2(events: Array,
+                        pre2post: Tuple[Array, Array],
                         post_num: int,
-                        values: Union[float, Tensor] = 1.):
+                        values: Union[float, Array] = 1.):
   """The pre-to-post synaptic computation with event-driven summation.
 
   When ``values`` is a scalar, this function is equivalent to
@@ -135,13 +135,13 @@ def pre2post_event_sum2(events: Tensor,
 
   Parameters
   ----------
-  events: Tensor
+  events: Array
     The events, must be bool.
-  pre2post: tuple of Tensor, tuple of Tensor
+  pre2post: tuple of Array, tuple of Array
     A tuple contains the connection information of pre-to-post.
   post_num: int
     The number of post-synaptic group.
-  values: float, Tensor
+  values: float, Array
     The value to make summation.
 
   Returns
diff --git a/brainpy/math/operators/spikegrad.py b/brainpy/math/operators/spikegrad.py
index a39b1f57d..809821183 100644
--- a/brainpy/math/operators/spikegrad.py
+++ b/brainpy/math/operators/spikegrad.py
@@ -5,7 +5,7 @@
 
 from brainpy.math import numpy_ops as bm
 from brainpy.math.jaxarray import JaxArray
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 from brainpy.math.setting import dftype
 
@@ -26,12 +26,12 @@ def _consistent_type(target, compare):
 
 
 @custom_gradient
-def spike_with_sigmoid_grad(x: Tensor, scale: float = None):
+def spike_with_sigmoid_grad(x: Array, scale: float = None):
   """Spike function with the sigmoid surrogate gradient.
 
   Parameters
   ----------
-  x: Tensor
+  x: Array
     The input data.
   scale: float
     The scaling factor.
@@ -52,14 +52,14 @@ def grad(dE_dz):
 
 
 @custom_gradient
-def spike2_with_sigmoid_grad(x_new: Tensor, x_old: Tensor, scale: float = None):
+def spike2_with_sigmoid_grad(x_new: Array, x_old: Array, scale: float = None):
   """Spike function with the sigmoid surrogate gradient.
 
   Parameters
   ----------
-  x_new: Tensor
+  x_new: Array
     The input data.
-  x_old: Tensor
+  x_old: Array
     The input data.
   scale: optional, float
     The scaling factor.
@@ -85,12 +85,12 @@ def grad(dE_dz):
 
 
 @custom_gradient
-def spike_with_linear_grad(x: Tensor, scale: float = None):
+def spike_with_linear_grad(x: Array, scale: float = None):
   """Spike function with the relu surrogate gradient.
 
   Parameters
   ----------
-  x: Tensor
+  x: Array
     The input data.
   scale: float
     The scaling factor.
@@ -110,14 +110,14 @@ def grad(dE_dz):
 
 
 @custom_gradient
-def spike2_with_linear_grad(x_new: Tensor, x_old: Tensor, scale: float = 10.):
+def spike2_with_linear_grad(x_new: Array, x_old: Array, scale: float = 10.):
   """Spike function with the linear surrogate gradient.
 
   Parameters
   ----------
-  x_new: Tensor
+  x_new: Array
     The input data.
-  x_old: Tensor
+  x_old: Array
     The input data.
   scale: float
     The scaling factor.
diff --git a/brainpy/tools/checking.py b/brainpy/tools/checking.py
index a29001c2e..a18f9d06b 100644
--- a/brainpy/tools/checking.py
+++ b/brainpy/tools/checking.py
@@ -8,7 +8,7 @@
 
 import brainpy.connect as conn
 import brainpy.initialize as init
-from brainpy.types import Tensor
+from brainpy.types import Array
 
 __all__ = [
   'check_shape_consistency',
@@ -184,7 +184,7 @@ def check_callable(fun: Callable,
   return fun
 
 
-def check_initializer(initializer: Union[Callable, init.Initializer, Tensor],
+def check_initializer(initializer: Union[Callable, init.Initializer, Array],
                       name: str = None,
                       allow_none: bool = False):
   """Check the initializer.
@@ -208,7 +208,7 @@ def check_initializer(initializer: Union[Callable, init.Initializer, Tensor],
                      f'tensor or callable function. While we got {type(initializer)}')
 
 
-def check_connector(connector: Union[Callable, conn.Connector, Tensor],
+def check_connector(connector: Union[Callable, conn.Connector, Array],
                     name: str = None, allow_none=False):
   """Check the connector.
   """
diff --git a/brainpy/train/back_propagation.py b/brainpy/train/back_propagation.py
index c4e2704f8..2657619d7 100644
--- a/brainpy/train/back_propagation.py
+++ b/brainpy/train/back_propagation.py
@@ -14,7 +14,7 @@
 from brainpy.errors import UnsupportedError
 from brainpy.tools.checking import serialize_kwargs
 from brainpy.tools.others import DotDict
-from brainpy.types import Tensor, Output
+from brainpy.types import Array, Output
 from ..running import constants as c
 from .base import DSTrainer
 
@@ -117,7 +117,7 @@ def train_loss_aux(self):
 
   def predict(
       self,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]],
       reset_state: bool = True,
       shared_args: Dict = None,
       eval_time: bool = False
@@ -130,7 +130,7 @@ def predict(
 
     Parameters
     ----------
-    inputs: Tensor, sequence, dict
+    inputs: Array, sequence, dict
       The feedforward input data. It must be a 3-dimensional data
       which has the shape of `(num_sample, num_time, num_feature)`.
     shared_args: dict
@@ -387,7 +387,7 @@ class BPFF(BPTT):
 
   def predict(
       self,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]],
       reset_state: bool = True,
       shared_args: Dict = None,
       eval_time: bool = False
@@ -400,7 +400,7 @@ def predict(
 
     Parameters
     ----------
-    inputs: Tensor, dict
+    inputs: Array, dict
       The feedforward input data. It must be a 3-dimensional data
       which has the shape of `(num_sample, num_time, num_feature)`.
     reset_state: bool
@@ -412,7 +412,7 @@ def predict(
 
     Returns
     -------
-    output: Tensor, dict
+    output: Array, dict
       The model output.
     """
     # format input data
diff --git a/brainpy/train/base.py b/brainpy/train/base.py
index b1d50eb2c..288aaf159 100644
--- a/brainpy/train/base.py
+++ b/brainpy/train/base.py
@@ -8,7 +8,7 @@
 from brainpy.dyn.base import DynamicalSystem
 from brainpy.dyn.runners import DSRunner
 from brainpy.tools.checking import check_dict_data
-from brainpy.types import Tensor, Output
+from brainpy.types import Array, Output
 from ..running import constants as c
 
 __all__ = [
@@ -38,7 +38,7 @@ def __init__(
 
   def predict(
       self,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]],
       reset_state: bool = False,
       shared_args: Dict = None,
       eval_time: bool = False
@@ -50,7 +50,7 @@ def predict(
 
     Parameters
     ----------
-    inputs: Tensor, sequence of Tensor, dict of Tensor
+    inputs: Array, sequence of Array, dict of Array
       The input values.
     reset_state: bool
       Reset the target state before running.
@@ -61,7 +61,7 @@ def predict(
 
     Returns
     -------
-    output: Tensor, sequence of Tensor, dict of Tensor
+    output: Array, sequence of Array, dict of Array
       The running output.
     """
     return super(DSTrainer, self).predict(duration=None,
diff --git a/brainpy/train/offline.py b/brainpy/train/offline.py
index 4ad1b4aa4..f3e3e592d 100644
--- a/brainpy/train/offline.py
+++ b/brainpy/train/offline.py
@@ -13,7 +13,7 @@
 from brainpy.errors import NoImplementationError
 from brainpy.modes import TrainingMode
 from brainpy.tools.checking import serialize_kwargs
-from brainpy.types import Tensor, Output
+from brainpy.types import Array, Output
 from .base import DSTrainer
 
 __all__ = [
@@ -100,7 +100,7 @@ def __repr__(self):
 
   def predict(
       self,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]],
       reset_state: bool = False,
       shared_args: Dict = None,
       eval_time: bool = False
@@ -112,7 +112,7 @@ def predict(
 
     Parameters
     ----------
-    inputs: Tensor, sequence of Tensor, dict of Tensor
+    inputs: Array, sequence of Array, dict of Array
       The input values.
     reset_state: bool
       Reset the target state before running.
@@ -123,7 +123,7 @@ def predict(
 
     Returns
     -------
-    output: Tensor, sequence of Tensor, dict of Tensor
+    output: Array, sequence of Array, dict of Array
       The running output.
     """
     outs = super(OfflineTrainer, self).predict(inputs=inputs,
@@ -221,7 +221,7 @@ def f_train(self, shared_args: Dict = None) -> Callable:
   def _make_fit_func(self, shared_args):
     shared_args = dict() if shared_args is None else shared_args
 
-    def train_func(monitor_data: Dict[str, Tensor], target_data: Dict[str, Tensor]):
+    def train_func(monitor_data: Dict[str, Array], target_data: Dict[str, Array]):
       for node in self.train_nodes:
         fit_record = monitor_data[f'{node.name}-fit_record']
         targets = target_data[node.name]
diff --git a/brainpy/train/online.py b/brainpy/train/online.py
index 1835d9c84..78e05cd36 100644
--- a/brainpy/train/online.py
+++ b/brainpy/train/online.py
@@ -15,7 +15,7 @@
 from brainpy.modes import TrainingMode
 from brainpy.tools.checking import serialize_kwargs
 from brainpy.tools.others.dicts import DotDict
-from brainpy.types import Tensor, Output
+from brainpy.types import Array, Output
 from .base import DSTrainer
 
 __all__ = [
@@ -101,7 +101,7 @@ def __repr__(self):
 
   def predict(
       self,
-      inputs: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      inputs: Union[Array, Sequence[Array], Dict[str, Array]],
       reset_state: bool = False,
       shared_args: Dict = None,
       eval_time: bool = False
@@ -113,7 +113,7 @@ def predict(
 
     Parameters
     ----------
-    inputs: Tensor, sequence of Tensor, dict of Tensor
+    inputs: Array, sequence of Array, dict of Array
       The input values.
     reset_state: bool
       Reset the target state before running.
@@ -124,7 +124,7 @@ def predict(
 
     Returns
     -------
-    output: Tensor, sequence of Tensor, dict of Tensor
+    output: Array, sequence of Array, dict of Array
       The running output.
     """
     outs = super(OnlineTrainer, self).predict(inputs=inputs,
@@ -196,7 +196,7 @@ def fit(
   def _fit(
       self,
       xs: Tuple,
-      ys: Union[Tensor, Sequence[Tensor], Dict[str, Tensor]],
+      ys: Union[Array, Sequence[Array], Dict[str, Array]],
       shared_args: Dict = None,
   ):
     """Predict the output according to the inputs.
@@ -205,7 +205,7 @@ def _fit(
     ----------
     xs: tuple
       Each tensor should have the shape of `(num_time, num_batch, num_feature)`.
-    ys: Tensor, sequence of Tensor, dict of Tensor
+    ys: Array, sequence of Array, dict of Array
       Each tensor should have the shape of `(num_time, num_batch, num_feature)`.
     shared_args: optional, dict
       The shared keyword arguments.
diff --git a/brainpy/types.py b/brainpy/types.py
index 01141d681..75594d09c 100644
--- a/brainpy/types.py
+++ b/brainpy/types.py
@@ -7,7 +7,7 @@
 
 
 __all__ = [
-  'Tensor', 'Parameter',
+  'Array', 'Parameter',
 
   'Shape',
 
@@ -15,7 +15,7 @@
 ]
 
 Parameter = TypeVar('Parameter', float, int, jnp.ndarray, 'JaxArray', 'Variable') # noqa
-Tensor = TypeVar('Tensor', 'JaxArray', 'Variable', 'TrainVar', jnp.ndarray, np.ndarray) # noqa
+Array = TypeVar('Array', 'JaxArray', 'Variable', 'TrainVar', jnp.ndarray, np.ndarray) # noqa
 
 Shape = TypeVar('Shape', int, Tuple[int, ...])
 

From bbb4fd05fe2bc76ea417c39b10f13fa9539d8aa8 Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Thu, 11 Aug 2022 10:33:30 +0800
Subject: [PATCH 3/6] fix bugs

---
 brainpy/dyn/base.py | 46 ++++++++++++++++++++++-----------------------
 1 file changed, 23 insertions(+), 23 deletions(-)

diff --git a/brainpy/dyn/base.py b/brainpy/dyn/base.py
index 0b88fbfa1..b70f1af52 100644
--- a/brainpy/dyn/base.py
+++ b/brainpy/dyn/base.py
@@ -18,7 +18,7 @@
 from brainpy.integrators import odeint, sdeint
 from brainpy.modes import Mode, TrainingMode, BatchingMode, normal, training
 from brainpy.tools.others import to_size, size2num
-from brainpy.types import Tensor, Shape
+from brainpy.types import Array, Shape
 
 __all__ = [
   # general class
@@ -70,17 +70,17 @@ def __init__(
       name: str = None,
       mode: Optional[Mode] = None,
   ):
-    super(DynamicalSystem, self).__init__(name=name)
-
-    # local delay variables
-    self.local_delay_vars: Dict[str, bm.LengthDelay] = Collector()
-
     # mode setting
     if mode is None: mode = normal
     if not isinstance(mode, Mode):
       raise ValueError(f'Should be instance of {Mode.__name__}, but we got {type(Mode)}: {Mode}')
     self._mode = mode
 
+    super(DynamicalSystem, self).__init__(name=name)
+
+    # local delay variables
+    self.local_delay_vars: Dict[str, bm.LengthDelay] = Collector()
+
     # fitting parameters
     self.online_fit_by = None
     self.offline_fit_by = None
@@ -106,9 +106,9 @@ def __call__(self, *args, **kwargs):
   def register_delay(
       self,
       identifier: str,
-      delay_step: Optional[Union[int, Tensor, Callable, Initializer]],
+      delay_step: Optional[Union[int, Array, Callable, Initializer]],
       delay_target: bm.Variable,
-      initial_delay_data: Union[Initializer, Callable, Tensor, float, int, bool] = None,
+      initial_delay_data: Union[Initializer, Callable, Array, float, int, bool] = None,
   ):
     """Register delay variable.
 
@@ -317,14 +317,14 @@ def offline_init(self):
 
   @tools.not_customized
   def online_fit(self,
-                 target: Tensor,
-                 fit_record: Dict[str, Tensor]):
+                 target: Array,
+                 fit_record: Dict[str, Array]):
     raise NoImplementationError('Subclass must implement online_fit() function when using OnlineTrainer.')
 
   @tools.not_customized
   def offline_fit(self,
-                  target: Tensor,
-                  fit_record: Dict[str, Tensor]):
+                  target: Array,
+                  fit_record: Dict[str, Array]):
     raise NoImplementationError('Subclass must implement offline_fit() function when using OfflineTrainer.')
 
   def clear_input(self):
@@ -482,7 +482,7 @@ def f(x):
     entries = '\n'.join(f'  [{i}] {f(x)}' for i, x in enumerate(self))
     return f'{self.__class__.__name__}(\n{entries}\n)'
 
-  def update(self, sha: dict, x: Any) -> Tensor:
+  def update(self, sha: dict, x: Any) -> Array:
     """Update function of a sequential model.
 
     Parameters
@@ -494,7 +494,7 @@ def update(self, sha: dict, x: Any) -> Tensor:
 
     Returns
     -------
-    y: Tensor
+    y: Array
       The output tensor.
     """
     for node in self.implicit_nodes.values():
@@ -686,7 +686,7 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]] = None,
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]] = None,
       name: str = None,
       mode: Mode = normal,
   ):
@@ -904,7 +904,7 @@ def __init__(
       self,
       pre: NeuGroup,
       post: NeuGroup,
-      conn: Union[TwoEndConnector, Tensor, Dict[str, Tensor]] = None,
+      conn: Union[TwoEndConnector, Array, Dict[str, Array]] = None,
       output: SynOut = NullSynOut(),
       stp: SynSTP = NullSynSTP(),
       ltp: SynLTP = NullSynLTP(),
@@ -946,10 +946,10 @@ def __init__(
 
   def init_weights(
       self,
-      weight: Union[float, Tensor, Initializer, Callable],
+      weight: Union[float, Array, Initializer, Callable],
       comp_method: str,
       sparse_data: str = 'csr'
-  ) -> Union[float, Tensor]:
+  ) -> Union[float, Array]:
     if comp_method not in ['sparse', 'dense']:
       raise ValueError(f'"comp_method" must be in "sparse" and "dense", but we got {comp_method}')
     if sparse_data not in ['csr', 'ij']:
@@ -1061,11 +1061,11 @@ def __init__(
       self,
       size: Shape,
       keep_size: bool = False,
-      C: Union[float, Tensor, Initializer, Callable] = 1.,
-      A: Union[float, Tensor, Initializer, Callable] = 1e-3,
-      V_th: Union[float, Tensor, Initializer, Callable] = 0.,
-      V_initializer: Union[Initializer, Callable, Tensor] = Uniform(-70, -60.),
-      noise: Union[float, Tensor, Initializer, Callable] = None,
+      C: Union[float, Array, Initializer, Callable] = 1.,
+      A: Union[float, Array, Initializer, Callable] = 1e-3,
+      V_th: Union[float, Array, Initializer, Callable] = 0.,
+      V_initializer: Union[Initializer, Callable, Array] = Uniform(-70, -60.),
+      noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
       mode: Mode = normal,

From 355abf3cca73f4febad87130e2414e97e8c27b6e Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Thu, 11 Aug 2022 21:09:31 +0800
Subject: [PATCH 4/6] Update runners.py

---
 brainpy/dyn/runners.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/brainpy/dyn/runners.py b/brainpy/dyn/runners.py
index 0c1be7df4..ecdd9ec53 100644
--- a/brainpy/dyn/runners.py
+++ b/brainpy/dyn/runners.py
@@ -490,7 +490,7 @@ def run(self, *args, **kwargs) -> Output:
     progress_bar: bool
       Whether report the progress of the simulation using progress bar.
     eval_time: bool
-      Whether ro evaluate the running time.
+      Whether to evaluate the running time.
 
     Returns
     -------

From 255b4915a34025a1bea3c8b0fede8dfd3da4e881 Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Thu, 11 Aug 2022 21:09:46 +0800
Subject: [PATCH 5/6] docs update

---
 docs/_static/dyn_models.svg                   |    2 +-
 .../tutorial_building/dynamical_systems.ipynb |  809 ++++++++
 docs/tutorial_building/index.rst              |    5 +
 docs/tutorial_building/network_models.ipynb   |  481 +++++
 docs/tutorial_building/neuron_models.ipynb    |  558 ++++++
 .../overview_of_dynamic_model.ipynb           |  898 +++++++++
 docs/tutorial_building/synapse_models.ipynb   | 1663 +++++++++++++++++
 .../{tensors.ipynb => array.ipynb}            |   72 +-
 ...ables.ipynb => arrays_and_variables.ipynb} |   46 +-
 docs/tutorial_math/control_flows.ipynb        |    6 +-
 docs/tutorial_math/index.rst                  |    2 +-
 docs/tutorial_math/jit_compilation.ipynb      |    4 +-
 docs/tutorial_math/overview.ipynb             |    4 +-
 .../dynamical_systems.ipynb                   |  808 --------
 docs/tutorial_simulation/index.rst            |    7 +-
 docs/tutorial_simulation/network_models.ipynb |  533 ------
 docs/tutorial_simulation/neuron_models.ipynb  |  565 ------
 .../overview_of_dynamic_model.ipynb           |  900 ---------
 .../parallel_computing.ipynb                  |  463 +++++
 .../simulation_dsrunner.ipynb                 |  804 ++++++++
 docs/tutorial_simulation/synapse_models.ipynb | 1642 ----------------
 docs/tutorial_toolbox/inputs.ipynb            |   57 -
 22 files changed, 5751 insertions(+), 4578 deletions(-)
 create mode 100644 docs/tutorial_building/dynamical_systems.ipynb
 create mode 100644 docs/tutorial_building/network_models.ipynb
 create mode 100644 docs/tutorial_building/neuron_models.ipynb
 create mode 100644 docs/tutorial_building/overview_of_dynamic_model.ipynb
 create mode 100644 docs/tutorial_building/synapse_models.ipynb
 rename docs/tutorial_math/{tensors.ipynb => array.ipynb} (93%)
 rename docs/tutorial_math/{tensors_and_variables.ipynb => arrays_and_variables.ipynb} (83%)
 delete mode 100644 docs/tutorial_simulation/dynamical_systems.ipynb
 delete mode 100644 docs/tutorial_simulation/network_models.ipynb
 delete mode 100644 docs/tutorial_simulation/neuron_models.ipynb
 delete mode 100644 docs/tutorial_simulation/overview_of_dynamic_model.ipynb
 create mode 100644 docs/tutorial_simulation/parallel_computing.ipynb
 create mode 100644 docs/tutorial_simulation/simulation_dsrunner.ipynb
 delete mode 100644 docs/tutorial_simulation/synapse_models.ipynb

diff --git a/docs/_static/dyn_models.svg b/docs/_static/dyn_models.svg
index d6708a670..78df73923 100644
--- a/docs/_static/dyn_models.svg
+++ b/docs/_static/dyn_models.svg
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
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diff --git a/docs/tutorial_building/dynamical_systems.ipynb b/docs/tutorial_building/dynamical_systems.ipynb
new file mode 100644
index 000000000..c358a648b
--- /dev/null
+++ b/docs/tutorial_building/dynamical_systems.ipynb
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Building General Dynamical Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](mailto:adaduo@outlook.com)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The previous sections have shown how to build neuron models, synapse models, and network models. In fact, these brain objects all inherit the base class [brainpy.dyn.DynamicalSystem](../apis/auto/dyn/generated/brainpy.dyn.base.DynamicalSystem.rst), ``brainpy.dyn.DynamicalSystem`` is the universal language to define dynamical models in BrainPy.\n",
+    "\n",
+    "To begin with, let's make a rief summary of previous dynamic models and give the definition of a dynamical system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-25T03:02:48.939126Z",
+     "start_time": "2021-03-25T03:02:47.073698Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "\n",
+    "bm.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## What is a dynamical system?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Looking back to the neuron and synapse models defined in the previous sections, they share a common feature that **they all contain some variables that change over time**. Because of these variables, the models become 'dynamic' and behave differently at different times.\n",
+    "\n",
+    "Actually, a *dynamical system* is defined as a system with time-dependent states. These time-dependent states are displayed as variables in the previous models.\n",
+    "\n",
+    "Mathematically, the change of a state $X$ can be expressed as\n",
+    "\n",
+    "$$\n",
+    "\\dot{X} = f(X, t)\n",
+    "$$\n",
+    "\n",
+    "where $X$ is the state of the system, $t$ is the time, and $f$ is a function describing the time dependence of the state. \n",
+    "\n",
+    "Alternatively, the evolution of the system over time can be given by\n",
+    "\n",
+    "$$\n",
+    "X(t+dt) = F\\left(X(t), t, dt\\right)\n",
+    "$$\n",
+    "\n",
+    "where $dt$ is the time step and $F$ is the evolution rule to update the system's state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Customizing your dynamical systems\n",
+    "\n",
+    "According to the mathematical expression of a dynamical system, any subclass of ``brainpy.dyn.DynamicalSystem`` must implement an updating rule in the ``update(self, tdi)`` function.\n",
+    "\n",
+    "To define a dynamical system, the following requirements should be satisfied:\n",
+    "- Inherit from `brainpy.dyn.DynamicalSystem`.\n",
+    "- Implement the `update(self, tdi)` function.\n",
+    "- When defining variables, they should be declared as `brainpy.math.Variable`.\n",
+    "- When updating the variables, it should be realized by [in-place operations](./tutorial_basics/arrays_and_variables.ipynb).\n",
+    "\n",
+    "Below is a simple example of a dynamical system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class FitzHughNagumoModel(bp.dyn.DynamicalSystem):\n",
+    "    def __init__(self, a=0.8, b=0.7, tau=12.5, **kwargs):\n",
+    "        super(FitzHughNagumoModel, self).__init__(**kwargs)\n",
+    "        \n",
+    "        # parameters\n",
+    "        self.a = a\n",
+    "        self.b = b\n",
+    "        self.tau = tau\n",
+    "        \n",
+    "        # variables should be packed by brainpy.math.Variable\n",
+    "        self.v = bm.Variable([0.])\n",
+    "        self.w = bm.Variable([0.])\n",
+    "        self.I = bm.Variable([0.])\n",
+    "        \n",
+    "    def update(self, tdi):\n",
+    "        t, dt = tdi.t, tdi.dt\n",
+    "        # t : the current time, the system keyword\n",
+    "        # dt : the time step, the system keyword\n",
+    "        \n",
+    "        # in-place update\n",
+    "        self.w += (self.v + self.a - self.b * self.w) / self.tau * dt\n",
+    "        self.v += (self.v - self.v ** 3 / 3 - self.w + self.I) * dt\n",
+    "        self.I[:] = 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Here, we have defined a dynamical system called [FitzHugh–Nagumo neuron model](https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model), whose dynamics is given by: \n",
+    "\n",
+    "$$\n",
+    "{\\dot {v}}=v-{\\frac {v^{3}}{3}}-w+I, \\\\\n",
+    "\\tau {\\dot {w}}=v+a-bw.\n",
+    "$$\n",
+    "\n",
+    "By using the [Euler method](../apis/integrators/generated/brainpy.integrators.ode.explicit_rk.Euler.rst), this system can be updated by the following rule:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "v(t+dt) &= v(t) + [v(t)-{v(t)^{3}/3}-w(t)+RI] * dt, \\\\\n",
+    "w(t + dt) &= w(t) + [v(t) + a - b w(t)] * dt.\n",
+    "\\end{aligned}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Advantages of using `brainpy.dyn.DynamicalSystem`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "There are several advantages of defining a dynamical system as `brainpy.dyn.DynamicalSystem`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 1. A systematic naming system. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "First, every instance of ``DynamicalSystem`` has its unique name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "fhn = FitzHughNagumoModel()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'FitzHughNagumoModel0'"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fhn.name  # name for \"fhn\" instance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Every instance has its unique name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FitzHughNagumoModel1\n",
+      "FitzHughNagumoModel2\n",
+      "FitzHughNagumoModel3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for _ in range(3):\n",
+    "    print(FitzHughNagumoModel().name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Users can also specify the name of a dynamic system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'X'"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fhn2 = FitzHughNagumoModel(name='X')\n",
+    "\n",
+    "fhn2.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In BrainPy, each object should have a unique name. However, we detect that FitzHughNagumoModel(name=None, mode=NormalMode) has a used name \"X\". \n",
+      "If you try to run multiple trials, you may need \n",
+      "\n",
+      ">>> brainpy.base.clear_name_cache() \n",
+      "\n",
+      "to clear all cached names. \n"
+     ]
+    }
+   ],
+   "source": [
+    "# same name will cause error\n",
+    "\n",
+    "try:\n",
+    "    FitzHughNagumoModel(name='X')\n",
+    "except bp.errors.UniqueNameError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Second, variables, children nodes, etc. inside an instance can be easily accessed by their *absolute* or *relative* path. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'X.I': Variable([0.], dtype=float32),\n 'X.v': Variable([0.], dtype=float32),\n 'X.w': Variable([0.], dtype=float32)}"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# All variables can be acessed by \n",
+    "# 1). the absolute path\n",
+    "\n",
+    "fhn2.vars()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'I': Variable([0.], dtype=float32),\n 'v': Variable([0.], dtype=float32),\n 'w': Variable([0.], dtype=float32)}"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2). or, the relative path\n",
+    "\n",
+    "fhn2.vars(method='relative')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 2. Convenient operations for simulation and analysis.\n",
+    "Brainpy provides different runners for dynamics simulation and analyzers for dynamics analysis, both of which require the dynamic model to be `Brainpy.dyn.DynamicalSystem`. For example, dynamic models can be packed by a runner for simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "9c2d6439164042f5ac02af067f566785"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5))\n",
+    "runner(duration=100)\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Please see [Runners](../tutorial_toolbox/runners.ipynb) to know more about the operations in runners."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 3. Efficient computation.\n",
+    "\n",
+    "``brainpy.dyn.DynamicalSystem`` is a subclass of [brainpy.Base](../apis/generated/brainpy.base.Base.rst), and therefore, any instance of ``brainpy.dyn.DynamicalSystem`` can be complied [just-in-time](../tutorial_basics/jit_compilation.ipynb) into efficient machine codes targeting on CPUs, GPUs, and TPUs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "7b92727e32354fa6ab3120707cccf1c3"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5), jit=True)\n",
+    "runner(duration=100)\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 4. Support composable programming.\n",
+    "Instances of ``brainpy.dyn.DynamicalSystem`` can be combined at will. The combined system is also a `brainpy.dyn.DynamicalSystem` and enjoys all the properties, methods, and interfaces provided by `brainpy.dyn.DynamicalSystem`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "For example, if the instances are wrapped into a container, i.e. `brainpy.dyn.Network`, variables and nodes can also be accessed by their absolute or relative path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fhn_net = bp.dyn.Network(f1=fhn, f2=fhn2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'FitzHughNagumoModel0.I': Variable([0.], dtype=float32),\n 'FitzHughNagumoModel0.v': Variable([0.], dtype=float32),\n 'FitzHughNagumoModel0.w': Variable([0.], dtype=float32),\n 'X.I': Variable([0.], dtype=float32),\n 'X.v': Variable([1.492591], dtype=float32),\n 'X.w': Variable([1.9365357], dtype=float32)}"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# absolute access of variables\n",
+    "\n",
+    "fhn_net.vars()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'f1.I': Variable([0.], dtype=float32),\n 'f1.v': Variable([0.], dtype=float32),\n 'f1.w': Variable([0.], dtype=float32),\n 'f2.I': Variable([0.], dtype=float32),\n 'f2.v': Variable([1.492591], dtype=float32),\n 'f2.w': Variable([1.9365357], dtype=float32)}"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# relative access of variables\n",
+    "\n",
+    "fhn_net.vars(method='relative')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'Network0': Network(f1=FitzHughNagumoModel(name=FitzHughNagumoModel0, mode=NormalMode), f2=FitzHughNagumoModel(name=X, mode=NormalMode)),\n 'FitzHughNagumoModel0': FitzHughNagumoModel(name=FitzHughNagumoModel0, mode=NormalMode),\n 'X': FitzHughNagumoModel(name=X, mode=NormalMode)}"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# absolute access of nodes\n",
+    "\n",
+    "fhn_net.nodes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'': Network(f1=FitzHughNagumoModel(name=FitzHughNagumoModel0, mode=NormalMode), f2=FitzHughNagumoModel(name=X, mode=NormalMode)),\n 'f1': FitzHughNagumoModel(name=FitzHughNagumoModel0, mode=NormalMode),\n 'f2': FitzHughNagumoModel(name=X, mode=NormalMode)}"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# relative access of nodes\n",
+    "\n",
+    "fhn_net.nodes(method='relative')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3a1922e96b82426f839dd3c925418c82"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(fhn_net,\n",
+    "                         monitors=['f1.v', 'X.v'], \n",
+    "                         inputs=[('f1.I', 1.5),   # relative access to variable \"I\" in 'fhn1'\n",
+    "                                 ('X.I', 1.0),])  # absolute access to variable \"I\" in 'fhn2'\n",
+    "runner(duration=100)\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon['f1.v'], legend='fhn1.v', show=False)\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon['X.v'], legend='fhn2.v', show=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "411px",
+    "width": "316px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "243.068px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/tutorial_building/index.rst b/docs/tutorial_building/index.rst
index ce8fad09f..ed5c7016b 100644
--- a/docs/tutorial_building/index.rst
+++ b/docs/tutorial_building/index.rst
@@ -4,3 +4,8 @@ Model Building
 .. toctree::
    :maxdepth: 1
 
+   overview_of_dynamic_model
+   neuron_models
+   synapse_models
+   network_models
+   dynamical_systems
\ No newline at end of file
diff --git a/docs/tutorial_building/network_models.ipynb b/docs/tutorial_building/network_models.ipynb
new file mode 100644
index 000000000..be6de2b1b
--- /dev/null
+++ b/docs/tutorial_building/network_models.ipynb
@@ -0,0 +1,481 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a449066c",
+   "metadata": {},
+   "source": [
+    "# Building Network Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f27e704",
+   "metadata": {},
+   "source": [
+    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](https://github.com/chaoming0625)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1daa966d",
+   "metadata": {},
+   "source": [
+    "In previous sections, it has been illustrated how to define neuron models by `brainpy.dyn.NeuGroup` and synapse models by `brainpy.dyn.TwoEndConn`. This section will introduce `brainpy.dyn.Network`, which is the base class used to build network models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa2b708a",
+   "metadata": {},
+   "source": [
+    "In essence, [brainpy.dyn.Network](../apis/auto/building/generated/brainpy.dyn.Network.rst) is a container, whose function is to compose the individual elements. It is a subclass of a more general class: [brainpy.dyn.Container](../apis/auto/building/generated/brainpy.dyn.Container.rst). \n",
+    "\n",
+    "In below, we take an excitation-inhibition (E-I) balanced network model as an example to illustrate how to compose the [LIF neurons](./neuron_models.ipynb) and [Exponential synapses](./synapse_models.ipynb) defined in previous tutorials to build a network. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "49c0646a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "\n",
+    "bp.math.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e363c68a",
+   "metadata": {},
+   "source": [
+    "## Excitation-Inhibition (E-I) Balanced Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34345d13",
+   "metadata": {},
+   "source": [
+    "The E-I balanced network was first proposed to explain the irregular firing patterns of cortical neurons and comfirmed by experimental data. The network [1] we are going to implement consists of excitatory (E) neurons and inhibitory (I) neurons, the ratio of which is about 4 : 1. The biggest difference between excitatory and inhibitory neurons is the reversal potential - the reversal potential of inhibitory neurons is much lower than that of excitatory neurons. Besides, the membrane time constant of inhibitory neurons is longer than that of excitatory neurons, which indicates that inhibitory neurons have slower dynamics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eccd498d",
+   "metadata": {},
+   "source": [
+    "[1] Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007), Simulation of networks of spiking neurons: a review of tools and strategies., J. Comput. Neurosci., 23, 3, 349–98."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3be5a19",
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "# BrianPy has some built-in conanical neuron and synapse models\n",
+    "\n",
+    "LIF = bp.neurons.LIF\n",
+    "Exponential = bp.synapses.Exponential"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aae1bdd0",
+   "metadata": {},
+   "source": [
+    "## Two ways to define network models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c63a6d",
+   "metadata": {},
+   "source": [
+    "There are several ways to define a Network model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abcd15a8",
+   "metadata": {},
+   "source": [
+    "### 1. Defining a network as a class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9230ab4a",
+   "metadata": {},
+   "source": [
+    "The first way to define a network model is like follows. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2213320",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EINet(bp.dyn.Network):\n",
+    "  def __init__(self, num_exc, num_inh, method='exp_auto', **kwargs):\n",
+    "    super(EINet, self).__init__(**kwargs)\n",
+    "\n",
+    "    # neurons\n",
+    "    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
+    "    E = LIF(num_exc, **pars, method=method)\n",
+    "    I = LIF(num_inh, **pars, method=method)\n",
+    "    E.V.value = bp.math.random.randn(num_exc) * 2 - 55.\n",
+    "    I.V.value = bp.math.random.randn(num_inh) * 2 - 55.\n",
+    "\n",
+    "    # synapses\n",
+    "    w_e = 0.6  # excitatory synaptic weight\n",
+    "    w_i = 6.7  # inhibitory synaptic weight\n",
+    "    E_pars = dict(output=bp.synouts.COBA(E=0.), g_max=w_e, tau=5.)\n",
+    "    I_pars = dict(output=bp.synouts.COBA(E=-80.), g_max=w_i, tau=10.)\n",
+    "    \n",
+    "    # Neurons connect to each other randomly with a connection probability of 2%\n",
+    "    self.E2E = Exponential(E, E, bp.conn.FixedProb(prob=0.02), **E_pars, method=method)\n",
+    "    self.E2I = Exponential(E, I, bp.conn.FixedProb(prob=0.02), **E_pars, method=method)\n",
+    "    self.I2E = Exponential(I, E, bp.conn.FixedProb(prob=0.02), **I_pars, method=method)\n",
+    "    self.I2I = Exponential(I, I, bp.conn.FixedProb(prob=0.02), **I_pars, method=method)\n",
+    "\n",
+    "    self.E = E\n",
+    "    self.I = I"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99233e81",
+   "metadata": {},
+   "source": [
+    "In an instance of ``brainpy.dyn.Network``,  all ``self.`` accessed elements can be gathered by the ``.nodes()`` function automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c1d98910",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'EINet0': EINet(),\n 'Exponential0': Exponential(name=Exponential0, mode=NormalMode),\n 'Exponential1': Exponential(name=Exponential1, mode=NormalMode),\n 'Exponential2': Exponential(name=Exponential2, mode=NormalMode),\n 'Exponential3': Exponential(name=Exponential3, mode=NormalMode),\n 'LIF0': LIF(name=LIF0, mode=NormalMode),\n 'LIF1': LIF(name=LIF1, mode=NormalMode),\n 'COBA2': COBA,\n 'NullSynSTP1': NullSynSTP,\n 'NullSynLTP0': NullSynLTP,\n 'COBA4': COBA,\n 'NullSynSTP2': NullSynSTP,\n 'NullSynLTP1': NullSynLTP,\n 'COBA3': COBA,\n 'NullSynSTP3': NullSynSTP,\n 'NullSynLTP2': NullSynLTP,\n 'COBA5': COBA,\n 'NullSynSTP4': NullSynSTP,\n 'NullSynLTP3': NullSynLTP}"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EINet(8, 2).nodes(level=-1).subset(bp.dyn.DynamicalSystem)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b6ce36",
+   "metadata": {},
+   "source": [
+    "Note in the above ``EINet``, we do not define the ``update()`` function. This is because any subclass of ``brainpy.dyn.Network`` has a default update function, in which it automatically gathers the elements defined in this network and sequentially runs the update function of each element."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "550ac98b",
+   "metadata": {},
+   "source": [
+    "Let's try to simulate our defined `EINet` model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a74c5b2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2f27dcd6a3a440a3b6da61a2999ea260"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Used time None s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "net = EINet(3200, 800, method='exp_auto')  # \"method\": the numerical integrator method\n",
+    "\n",
+    "runner = bp.dyn.DSRunner(net,\n",
+    "                         monitors=['E.spike', 'I.spike'],\n",
+    "                         inputs=[('E.input', 20.), ('I.input', 20.)])\n",
+    "t = runner.run(100.)\n",
+    "print(f'Used time {t} s')\n",
+    "\n",
+    "# visualization\n",
+    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'],\n",
+    "                         title='Spikes of Excitatory Neurons', show=True)\n",
+    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['I.spike'],\n",
+    "                         title='Spikes of Inhibitory Neurons', show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92b7a472",
+   "metadata": {},
+   "source": [
+    "### 2. Instantiating a network directly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4e5848b",
+   "metadata": {},
+   "source": [
+    "Another way to instantiate a network model is directly pass the elements into the constructor of ``brainpy.Network``. It receives ``*args`` and ``**kwargs`` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "14e659ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# neurons\n",
+    "pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
+    "E = LIF(3200, **pars)\n",
+    "I = LIF(800, **pars)\n",
+    "E.V.value = bp.math.random.randn(E.num) * 2 - 55.\n",
+    "I.V.value = bp.math.random.randn(I.num) * 2 - 55.\n",
+    "\n",
+    "# synapses\n",
+    "E_pars = dict(output=bp.synouts.COBA(E=0.), g_max=0.6, tau=5.)\n",
+    "I_pars = dict(output=bp.synouts.COBA(E=-80.), g_max=6.7, tau=10.)\n",
+    "E2E = Exponential(E, E, bp.conn.FixedProb(prob=0.02), **E_pars)\n",
+    "E2I = Exponential(E, I, bp.conn.FixedProb(prob=0.02), **E_pars)\n",
+    "I2E = Exponential(I, E, bp.conn.FixedProb(prob=0.02), **I_pars)\n",
+    "I2I = Exponential(I, I, bp.conn.FixedProb(prob=0.02), **I_pars)\n",
+    "\n",
+    "\n",
+    "# Network\n",
+    "net2 = bp.dyn.Network(E2E, E2I, I2E, I2I, exc_group=E, inh_group=I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84449872",
+   "metadata": {},
+   "source": [
+    "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in tutorial of [Runners](../tutorial_toolbox/runners.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "36f54a4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "LIF(name=LIF4, mode=NormalMode)"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net2.exc_group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad57ec70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "LIF(name=LIF5, mode=NormalMode)"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net2.inh_group"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa372446",
+   "metadata": {},
+   "source": [
+    "After construction, the simulation goes the same way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "29ebd650",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "68c84417212541578975034aa74e81be"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Used time None s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(net2,\n",
+    "                         monitors=['exc_group.spike', 'inh_group.spike'],\n",
+    "                         inputs=[('exc_group.input', 20.), ('inh_group.input', 20.)])\n",
+    "t = runner.run(100.)\n",
+    "print(f'Used time {t} s')\n",
+    "\n",
+    "# visualization\n",
+    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['exc_group.spike'],\n",
+    "                         title='Spikes of Excitatory Neurons', show=True)\n",
+    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['inh_group.spike'],\n",
+    "                         title='Spikes of Inhibitory Neurons', show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee0ef0f9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in the toolboxes, where the use of [runners](../tutorial_toolbox/runners.ipynb), [monitors](../tutorial_toolbox/monitors.ipynb), and [inputs](../tutorial_toolbox/inputs.ipynb) will be expatiated."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/tutorial_building/neuron_models.ipynb b/docs/tutorial_building/neuron_models.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "118e3b1d",
+   "metadata": {},
+   "source": [
+    "# Building Neuron Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c68cbca",
+   "metadata": {},
+   "source": [
+    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](https://github.com/chaoming0625)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f783d7fb",
+   "metadata": {},
+   "source": [
+    "The previous section shows all available models users can utilize by simply instantiating the abstract model. In following sections we will dive into details to illustrate how to build a neuron model with ``brainpy.dyn.NeuGroup``. Neurons are the most basic components in neural dynamics simulation. In BrainPy, `brainpy.dyn.NeuGroup` is used for neuron modeling. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aac4b858",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "\n",
+    "bm.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d38f2b7",
+   "metadata": {},
+   "source": [
+    "## ``brainpy.dyn.NeuGroup``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6444c5ce",
+   "metadata": {},
+   "source": [
+    "Generally, any neuron model can evolve continuously or discontinuously. \n",
+    "Discontinuous evolution may be triggered by events, such as the reset of membrane potential. \n",
+    "Moreover, it is common in a neural system that a dynamical system has different states, such as the excitable or refractory\n",
+    "state in a [leaky integrate-and-fire (LIF) model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html). \n",
+    "In this section, we will use two examples to illustrate how to capture these complexity in neuron modeling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9520e950",
+   "metadata": {},
+   "source": [
+    "Defining a neuron model in BrainPy is simple. You just need to inherit from ``brainpy.dyn.NeuGroup``, and satisfy the following two requirements:\n",
+    "\n",
+    "- Providing the `size` of the neural group in the constructor when initialize a new neural group class. `size` can be a integer referring to the number of neurons or a tuple/list of integers referring to the geometry of the neural group in different dimensions. Acoording to the provided group ``size``, NeuroGroup will automatically calculate the total number ``num`` of neurons in this group.\n",
+    "\n",
+    "- Creating an `update(_t, dt)` function. Update function provides the rule how the neuron states are evolved from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2993080",
+   "metadata": {},
+   "source": [
+    "In the following part, a [Hodgkin-Huxley](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.HH.html) (HH) model is used as an example for illustration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3095ec6f",
+   "metadata": {},
+   "source": [
+    "## [Hodgkin–Huxley Model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.HH.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5170763",
+   "metadata": {},
+   "source": [
+    "The Hodgkin-Huxley (HH) model is a continuous-time dynamical system. It is one of the most successful mathematical models of a complex biological process that has ever been formulated. Changes of the membrane potential influence the conductances of different channels, elaborately modeling the neural activities in biological systems. Mathematically, the model is given by:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "    C_m \\frac {dV} {dt} &= -(\\bar{g}_{Na} m^3 h (V -E_{Na})\n",
+    "    + \\bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t) \\quad\\quad(1) \\\\\n",
+    "    \\frac {dx} {dt} &= \\alpha_x (1-x)  - \\beta_x, \\quad x\\in {\\rm{\\{m, h, n\\}}} \\quad\\quad(2) \\\\\n",
+    "    &\\alpha_m(V) = \\frac {0.1(V+40)}{1-\\exp(\\frac{-(V + 40)} {10})} \\quad\\quad(3) \\\\\n",
+    "    &\\beta_m(V) = 4.0 \\exp(\\frac{-(V + 65)} {18}) \\quad\\quad(4) \\\\\n",
+    "    &\\alpha_h(V) = 0.07 \\exp(\\frac{-(V+65)}{20}) \\quad\\quad(5) \\\\\n",
+    "    &\\beta_h(V) = \\frac 1 {1 + \\exp(\\frac{-(V + 35)} {10})} \\quad\\quad(6) \\\\\n",
+    "    &\\alpha_n(V) = \\frac {0.01(V+55)}{1-\\exp(-(V+55)/10)} \\quad\\quad(7) \\\\\n",
+    "    &\\beta_n(V) = 0.125 \\exp(\\frac{-(V + 65)} {80}) \\quad\\quad(8) \\\\\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "where $V$ is the membrane potential, $C_m$ is the membrane capacitance per unit area, $E_K$ and $E_{Na}$ are the potassium and sodium reversal potentials, respectively, $E_l$ is the leak reversal potential, $\\bar{g}_K$ and $\\bar{g}_{Na}$ are the potassium and sodium conductances per unit area, respectively, and $\\bar{g}_l$ is the leak conductance per unit area. Because the potassium and sodium channels are voltage-sensitive, according to the biological experiments, $m$, $n$ and $h$ are used to simulate the activation of the channels. Speficially, $n$ measures the activatio of potassium channels, and $m$  and $h$ measures the activation and inactivation of sodium channels, respectively. $\\alpha_{x}$ and $\\beta_{x}$ are rate constants for the ion channel x and depend exclusively on the membrane potential."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84f438ae",
+   "metadata": {},
+   "source": [
+    "To implement the HH model, variables should be specified. According to the above equations, the following five state variables change with respect to time:\n",
+    "- `V`: the membrane potential\n",
+    "- `m`: the activation of sodium channels\n",
+    "- `h`: the inactivation of sodium channels\n",
+    "- `n`: the activation of potassium channels\n",
+    "- `input`: the external/synaptic input\n",
+    "\n",
+    "Besides, the spiking state and the last spiking time can also be recorded for statistic analysis:\n",
+    "- ``spike``: whether a spike is produced\n",
+    "- ``t_last_spike``: the last spiking time\n",
+    "\n",
+    "Based on these state variables, the HH model can be implemented as below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3ea88e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class HH(bp.dyn.NeuGroup):\n",
+    "  def __init__(self, size, ENa=50., gNa=120., EK=-77., gK=36., EL=-54.387, gL=0.03,\n",
+    "               V_th=20., C=1.0, **kwargs):\n",
+    "    # providing the group \"size\" information\n",
+    "    super(HH, self).__init__(size=size, **kwargs)\n",
+    "\n",
+    "    # initialize parameters\n",
+    "    self.ENa = ENa\n",
+    "    self.EK = EK\n",
+    "    self.EL = EL\n",
+    "    self.gNa = gNa\n",
+    "    self.gK = gK\n",
+    "    self.gL = gL\n",
+    "    self.C = C\n",
+    "    self.V_th = V_th\n",
+    "\n",
+    "    # initialize variables\n",
+    "    self.V = bm.Variable(bm.random.randn(self.num) - 70.)\n",
+    "    self.m = bm.Variable(0.5 * bm.ones(self.num))\n",
+    "    self.h = bm.Variable(0.6 * bm.ones(self.num))\n",
+    "    self.n = bm.Variable(0.32 * bm.ones(self.num))\n",
+    "    self.input = bm.Variable(bm.zeros(self.num))\n",
+    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
+    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
+    "\n",
+    "    # integral functions\n",
+    "    self.int_V = bp.odeint(f=self.dV, method='exp_auto')\n",
+    "    self.int_m = bp.odeint(f=self.dm, method='exp_auto')\n",
+    "    self.int_h = bp.odeint(f=self.dh, method='exp_auto')\n",
+    "    self.int_n = bp.odeint(f=self.dn, method='exp_auto')\n",
+    "\n",
+    "  def dV(self, V, t, m, h, n, Iext):\n",
+    "    I_Na = (self.gNa * m ** 3.0 * h) * (V - self.ENa)\n",
+    "    I_K = (self.gK * n ** 4.0) * (V - self.EK)\n",
+    "    I_leak = self.gL * (V - self.EL)\n",
+    "    dVdt = (- I_Na - I_K - I_leak + Iext) / self.C\n",
+    "    return dVdt\n",
+    "\n",
+    "  def dm(self, m, t, V):\n",
+    "    alpha = 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))\n",
+    "    beta = 4.0 * bm.exp(-(V + 65) / 18)\n",
+    "    dmdt = alpha * (1 - m) - beta * m\n",
+    "    return dmdt\n",
+    "  \n",
+    "  def dh(self, h, t, V):\n",
+    "    alpha = 0.07 * bm.exp(-(V + 65) / 20.)\n",
+    "    beta = 1 / (1 + bm.exp(-(V + 35) / 10))\n",
+    "    dhdt = alpha * (1 - h) - beta * h\n",
+    "    return dhdt\n",
+    "\n",
+    "  def dn(self, n, t, V):\n",
+    "    alpha = 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))\n",
+    "    beta = 0.125 * bm.exp(-(V + 65) / 80)\n",
+    "    dndt = alpha * (1 - n) - beta * n\n",
+    "    return dndt\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    # compute V, m, h, n\n",
+    "    V = self.int_V(self.V, _t, self.m, self.h, self.n, self.input, dt=_dt)\n",
+    "    self.h.value = self.int_h(self.h, _t, self.V, dt=_dt)\n",
+    "    self.m.value = self.int_m(self.m, _t, self.V, dt=_dt)\n",
+    "    self.n.value = self.int_n(self.n, _t, self.V, dt=_dt)\n",
+    "\n",
+    "    # update the spiking state and the last spiking time\n",
+    "    self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)\n",
+    "    self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike)\n",
+    "\n",
+    "    # update V\n",
+    "    self.V.value = V\n",
+    "\n",
+    "    # reset the external input\n",
+    "    self.input[:] = 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d523fb3",
+   "metadata": {},
+   "source": [
+    "When defining the HH model, equation (1) is accomplished by [brainpy.odeint](../apis/integrators/generated/brainpy.integrators.odeint.rst) as an [ODEIntegrator](../apis/integrators/generated/brainpy.integrators.ODEIntegrator.rst). The details are contained in the [Numerical Solvers for ODEs](../tutorial_intg/ode_numerical_solvers.ipynb) tutorial.\n",
+    "\n",
+    "The variables, which will be updated during dynamics simulation, should be packed as `brainpy.math.Variable` and thus can be processed by JIT compliers to accelerate simulation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "215292d2",
+   "metadata": {},
+   "source": [
+    "In the following part, a [leaky integrate-and-fire](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html) (LIF) model is introduced as another example for illustration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d7d580",
+   "metadata": {},
+   "source": [
+    "## [Leaky Integrate-and-Fire Model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f45c7805",
+   "metadata": {},
+   "source": [
+    "The LIF model is the classical neuron model which contains a continuous process and a discontinous spike reset operation. \n",
+    "Formally, it is given by:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\tau_m \\frac{dV}{dt} = - (V(t) - V_{rest}) + I(t)  \\quad\\quad (1) \\\\\n",
+    "\\text{if} \\, V(t) \\gt V_{th}, V(t) =V_{rest} \\,\n",
+    "\\text{after} \\, \\tau_{ref} \\,  \\text{ms}        \\quad\\quad (2)\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "where $V$ is the membrane potential, $V_{rest}$ is the rest membrane potential, $V_{th}$ is the spike threshold, $\\tau_m$ is the time constant, $\\tau_{ref}$ is the refractory time period, and $I$ is the time-variant synaptic inputs. \n",
+    "\n",
+    "The above two equations model the continuous change and the spiking of neurons, respectively. Moreover, it has multiple states: ``subthreshold`` state, and  ``spiking`` or ``refractory`` state.  The membrane potential $V$ is integrated according to equation (1) when it is below $V_{th}$. Once $V$ reaches the threshold $V_{th}$, according to equation (2), $V$ is reaet to $V_{rest}$, and the neuron enters the refractory period where the membrane potential $V$ will remain constant in the following $\\tau_{ref}$ ms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f3f7d32",
+   "metadata": {},
+   "source": [
+    "The neuronal variables, like the membrane potential and external input, can be captured by the following two variables:\n",
+    "\n",
+    "- ``V``: the membrane potential\n",
+    "- ``input``: the external/synaptic input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76fa0aa2",
+   "metadata": {},
+   "source": [
+    "In order to define the different states of a LIF neuron, we define additional variables:\n",
+    "\n",
+    "- ``spike``: whether a spike is produced\n",
+    "- ``refractory``: whether the neuron is in the refractory period\n",
+    "- ``t_last_spike``: the last spiking time\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50fbecbe",
+   "metadata": {},
+   "source": [
+    "Based on these state variables, the LIF model can be implemented as below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4961244a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LIF(bp.dyn.NeuGroup):\n",
+    "  def __init__(self, size, V_rest=0., V_reset=-5., V_th=20., R=1., tau=10., t_ref=5., **kwargs):\n",
+    "    super(LIF, self).__init__(size=size, **kwargs)\n",
+    "\n",
+    "    # initialize parameters\n",
+    "    self.V_rest = V_rest\n",
+    "    self.V_reset = V_reset\n",
+    "    self.V_th = V_th\n",
+    "    self.R = R\n",
+    "    self.tau = tau\n",
+    "    self.t_ref = t_ref\n",
+    "\n",
+    "    # initialize variables\n",
+    "    self.V = bm.Variable(bm.random.randn(self.num) + V_reset)\n",
+    "    self.input = bm.Variable(bm.zeros(self.num))\n",
+    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
+    "    self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
+    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
+    "\n",
+    "    # integral function\n",
+    "    self.integral = bp.odeint(f=self.derivative, method='exp_auto')\n",
+    "\n",
+    "  def derivative(self, V, t, Iext):\n",
+    "    dvdt = (-V + self.V_rest + self.R * Iext) / self.tau\n",
+    "    return dvdt\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    # Whether the neurons are in the refractory period\n",
+    "    refractory = (_t - self.t_last_spike) <= self.t_ref\n",
+    "    \n",
+    "    # compute the membrane potential\n",
+    "    V = self.integral(self.V, _t, self.input, dt=_dt)\n",
+    "    \n",
+    "    # computed membrane potential is valid only when the neuron is not in the refractory period \n",
+    "    V = bm.where(refractory, self.V, V)\n",
+    "    \n",
+    "    # update the spiking state\n",
+    "    spike = self.V_th <= V\n",
+    "    self.spike.value = spike\n",
+    "    \n",
+    "    # update the last spiking time\n",
+    "    self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike)\n",
+    "    \n",
+    "    # update the membrane potential and reset spiked neurons\n",
+    "    self.V.value = bm.where(spike, self.V_reset, V)\n",
+    "    \n",
+    "    # update the refractory state\n",
+    "    self.refractory.value = bm.logical_or(refractory, spike)\n",
+    "    \n",
+    "    # reset the external input\n",
+    "    self.input[:] = 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b54438c",
+   "metadata": {},
+   "source": [
+    "In above, the discontinous resetting is implemented with ``brainpy.math.where`` operation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b80959f",
+   "metadata": {},
+   "source": [
+    "## Instantiation and running"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05818ebb",
+   "metadata": {},
+   "source": [
+    "Here, let's try to instantiate a ``HH`` neuron group:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7afcd4ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neu = HH(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6be8d3d",
+   "metadata": {},
+   "source": [
+    "in which a neural group containing 10 HH neurons is generated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9d2604b",
+   "metadata": {},
+   "source": [
+    "The details of the model simulation will be expanded in the [Runners](../tutorial_toolbox/runners.ipynb) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.DSRunner` and `brainpy.ReportRunner`. The variables to be monitored and the input crrents to be applied in the simulation can be provided when initializing the runner. The details are accessible in [Monitors](../tutorial_toolbox/monitors.ipynb) and [Inputs](../tutorial_toolbox/inputs.ipynb). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9a291f2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "runner = bp.dyn.DSRunner(\n",
+    "    neu, \n",
+    "    monitors=['V'], \n",
+    "    inputs=('input', 22.)  # constant external inputs of 22 mA to all neurons\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00385de1",
+   "metadata": {},
+   "source": [
+    "Then the simulation can be performed with a given time period, and the simulation result can be visualized:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f102b056",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/2000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a77fa36ee2864feeb46c661fe7c207da"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner.run(200)  # the running time is 200 ms\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93208ac2",
+   "metadata": {},
+   "source": [
+    "A LIF neural group can be instantiated and applied in simulation in a similar way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "929d85e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/2000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c90d792872134110823b060707c5e714"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "group = LIF(10)\n",
+    "\n",
+    "runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 22.), jit=True)\n",
+    "runner.run(200)\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/tutorial_building/overview_of_dynamic_model.ipynb b/docs/tutorial_building/overview_of_dynamic_model.ipynb
new file mode 100644
index 000000000..5375fb15f
--- /dev/null
+++ b/docs/tutorial_building/overview_of_dynamic_model.ipynb
@@ -0,0 +1,898 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Dynamical System Specification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    " @[Tianqiu Zhang](mailto:tianqiuakita@gmail.com) @[Chaoming Wang](mailto:adaduo@outlook.com)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "BrainPy enables modularity programming and easy model debugging. To build a complex brain dynamics model, you just need to group its building blocks. In this section, we are going to talk about what building blocks we have provided, and how to use these building blocks.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "\n",
+    "bm.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Models in ``brainpy.dyn``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "``brainpy.dyn`` has provided many convenient channels, neurons, synapse, and other models for users. The following figure is a glimpse of the provided models.\n",
+    "\n",
+    "<img src=\"../_static/dyn_models.svg\" width=\"750 px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "New models will be continuously updated in the page of [API documentation](../apis/dyn.rst)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Initializing a neuron model"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "All neuron models implemented in brainpy are subclasses of ``brainpy.dyn.NeuGroup``. The initialization of a neuron model just needs to provide the geometry size of neurons in a population group."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "hh = bp.neurons.HH(size=1)  # only 1 neuron\n",
+    "\n",
+    "hh = bp.neurons.HH(size=10)  # 10 neurons in a group\n",
+    "\n",
+    "hh = bp.neurons.HH(size=(10, 10))  # a grid of (10, 10) neurons in a group\n",
+    "\n",
+    "hh = bp.neurons.HH(size=(5, 4, 2))  # a column of (5, 4, 2) neurons in a group"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Generally speaking, there are two types of arguments can be set by users:\n",
+    "\n",
+    "- **parameters**: the model parameters, like `gNa` refers to the maximum conductance of sodium channel in the ``brainpy.dyn.HH`` model.\n",
+    "- **variables**: the model variables, like `V` refers to the membrane potential of a neuron model.\n",
+    "\n",
+    "In default, model *parameters* are homogeneous, which are just scalar values."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "120.0"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hh = bp.neurons.HH(5)  # there are five neurons in this group\n",
+    "\n",
+    "hh.gNa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "However, neuron models support heterogeneous parameters when performing computations in a neuron group. One can initialize *heterogeneous parameters* by several ways.\n",
+    "\n",
+    "**1\\. Array**\n",
+    "\n",
+    "Users can directly provide an array as the parameter."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([122.192924, 125.95139 , 114.511345, 122.27126 , 114.39388 ],            dtype=float32)"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hh = bp.neurons.HH(5, gNa=bm.random.uniform(110, 130, size=5))\n",
+    "\n",
+    "hh.gNa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "**2\\. Initializer**\n",
+    "\n",
+    "BrainPy provides wonderful supports on [initializations](../tutorial_toolbox/synaptic_weights.ipynb). One can provide an initializer to the parameter to instruct the model initialize heterogeneous parameters."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([50., 50., 50., 50., 50.], dtype=float32)"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hh = bp.neurons.HH(5, ENa=bp.init.OneInit(50.))\n",
+    "\n",
+    "hh.ENa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "**3\\. Callable function**\n",
+    "\n",
+    "You can also directly provide a callable function which receive a ``shape`` argument."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([59.987877, 56.06326 , 43.771053, 53.228992, 49.78434 ], dtype=float32)"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hh = bp.neurons.HH(5, ENa=lambda shape: bm.random.uniform(40, 60, shape))\n",
+    "\n",
+    "hh.ENa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Here, let's see how the heterogeneous parameters influence our model simulation."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "# we create 3 neurons in a group. Each neuron has a unique \"gNa\"\n",
+    "\n",
+    "model = bp.neurons.HH(3, gNa=bp.init.Uniform(min_val=100, max_val=140))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "977ec6f4ed03435fbedf30137144633e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(model, monitors=['V'], inputs=['input', 5.])\n",
+    "runner.run(100.)\n",
+    "\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, plot_ids=[0, 1, 2], show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Similarly, the setting of the initial values of a variable can also be realized through the above three ways: *Array*, *Initializer*, and *Callable function*. For example,"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "hh = bp.neurons.HH(\n",
+    "   3,\n",
+    "   V_initializer=bp.init.Uniform(-80., -60.),  # Initializer\n",
+    "   m_initializer=lambda shape: bm.random.random(shape),  # function\n",
+    "   h_initializer=bm.random.random(3),  # Array\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V:  Variable([-78.496025, -77.036995, -72.28617 ], dtype=float32)\n",
+      "m:  Variable([0.40498435, 0.000857  , 0.40790236], dtype=float32)\n",
+      "h:  Variable([0.5012727 , 0.90631044, 0.96595407], dtype=float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('V: ', hh.V)\n",
+    "print('m: ', hh.m)\n",
+    "print('h: ', hh.h)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Initializing a synapse model"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Initializing a synapse model needs to provide its pre-synaptic group (``pre``), post-synaptic group (``post``) and the connection method between them (``conn``). The below is an example to create an [Exponential synapse model](../apis/auto/dyn/generated/brainpy.dyn.synapses.ExpCUBA.rst):"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "neu = bp.neurons.LIF(10)\n",
+    "\n",
+    "# here we create a synaptic projection within a population\n",
+    "syn = bp.synapses.compat.ExpCUBA(pre=neu, post=neu, conn=bp.conn.All2All())"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "BrainPy's build-in synapse models support **heterogeneous** synaptic weights and delay steps by using *Array*, *Initializer* and *Callable function*. For example,"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [],
+   "source": [
+    "syn = bp.synapses.compat.ExpCUBA(neu, neu, bp.conn.FixedProb(prob=0.1),\n",
+    "                     g_max=bp.init.Uniform(min_val=0.1, max_val=1.),\n",
+    "                     delay_step=lambda shape: bm.random.randint(10, 30, shape))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([0.5460913 , 0.98663217, 0.8724222 , 0.62892395, 0.18731643,\n          0.400298  , 0.96323854, 0.54389703, 0.7557717 , 0.42726317,\n          0.5927771 ], dtype=float32)"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "syn.g_max"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([22, 14, 14, 28, 18, 10, 11, 19, 15, 11], dtype=int32)"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "syn.delay_step"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "However, in BrainPy, the built-in synapse models only support homogenous synaptic parameters, like the time constant $\\tau$. Users can [customize their synaptic models](./synapse_models.ipynb) when they want heterogeneous synatic parameters."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Similar, the synaptic variables can be initialized heterogeneously by using *Array*, *Initializer*, and *Callable functions*."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Change model parameters during simulation"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In BrainPy, all the dynamically changed variables (no matter it is changed inside or outside of a jitted function) should be marked as ``brainpy.math.Variable``. BrainPy's built-in models also support modifying model parameters during simulation."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For example, if you want to fix the `gNa` in the first 100 ms simulation, and then try to decrease its value in the following simulations. In this case, we can provide the `gNa` as an instance of ``brainpy.math.Variable`` when initializing the model."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [],
+   "source": [
+    "hh = bp.neurons.HH(5, gNa=bm.Variable(bm.asarray([120.])))\n",
+    "\n",
+    "runner = bp.dyn.DSRunner(hh, monitors=['V'], inputs=['input', 5.])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "f37ef7da7f754e5b8c2be267033617cd"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the first running\n",
+    "runner.run(100.)\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "56dd4ba8545246368bcea8b0f67113f0"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# change the gNa first\n",
+    "hh.gNa[:] = 100.\n",
+    "\n",
+    "# the second running\n",
+    "runner.run(100.)\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Examples of using built-in models"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Here we show users how to simulate a famous neuron models: [The Morris-Lecar neuron model](../apis/auto/dyn/generated/brainpy.dyn.neurons.MorrisLecar.rst), which is a two-dimensional \"reduced\" excitation model applicable to systems having two non-inactivating voltage-sensitive conductances."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [],
+   "source": [
+    "group = bp.neurons.MorrisLecar(1)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Then users can utilize various tools provided by BrainPy to easily simulate the Morris-Lecar neuron model. Here we are not going to dive into details so please read the corresponding tutorials if you want to learn more."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "8e6e6709e4bf4709a88c9d792a326528"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x432 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(group, monitors=['V', 'W'], inputs=('input', 100.))\n",
+    "runner.run(1000)\n",
+    "\n",
+    "fig, gs = bp.visualize.get_figure(2, 1, 3, 8)\n",
+    "fig.add_subplot(gs[0, 0])\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.W, ylabel='W')\n",
+    "fig.add_subplot(gs[1, 0])\n",
+    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next we will also give users an intuitive understanding about building a network composed of different neurons and synapses model. Users can simply initialize these models as below and pass into ``brainpy.dyn.Network``."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "neu1 = bp.neurons.HH(1)\n",
+    "neu2 = bp.neurons.HH(1)\n",
+    "syn1 = bp.synapses.AMPA(neu1, neu2, bp.connect.All2All())\n",
+    "net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "By selecting proper runner, users can simulate the network efficiently and plot the simulation results."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1500 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "52c4e1eb9b9245d4a85cd7e271ad5fec"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x432 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 5.)], monitors=['pre.V', 'post.V', 'syn.g'])\n",
+    "runner.run(150.)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, gs = bp.visualize.get_figure(2, 1, 3, 8)\n",
+    "fig.add_subplot(gs[0, 0])\n",
+    "plt.plot(runner.mon.ts, runner.mon['pre.V'], label='pre-V')\n",
+    "plt.plot(runner.mon.ts, runner.mon['post.V'], label='post-V')\n",
+    "plt.legend()\n",
+    "\n",
+    "fig.add_subplot(gs[1, 0])\n",
+    "plt.plot(runner.mon.ts, runner.mon['syn.g'], label='g')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/tutorial_building/synapse_models.ipynb b/docs/tutorial_building/synapse_models.ipynb
new file mode 100644
index 000000000..95a185c55
--- /dev/null
+++ b/docs/tutorial_building/synapse_models.ipynb
@@ -0,0 +1,1663 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "096f2ee4",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Building Synapse Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c1ae039",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "@[Chaoming Wang](https://github.com/chaoming0625) @[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bed1c4f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Synaptic computation is the core of brain dynamics programming. This is beacuse in a real project most of the simulation time spends on the computation of synapses. In order to achieve efficient synaptic computation, BrainPy provides many useful supports. Here, we are going to explore the details of these supports. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1e518e11",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "\n",
+    "# bm.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f111708e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Synapse Models in Math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c5bbda2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Before we talk about the implementation of synapses in BrainPy, it's better to understand the targets (synapse models) we are going to implement. For different illustration purposes, we are going to implement two synapse models: [exponential synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.DualExpCOBA.html) and [AMPA synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.AMPA.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee864f9e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 1. The exponential synapse model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "266c7fa7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The exponential synapse model assumes that once a pre-synaptic neuron generates a spike, the synaptic state arises instantaneously, then decays with a certain time constant $\\tau_{decay}$. Its dynamics is given by:\n",
+    "\n",
+    "$$\n",
+    "\\frac{d g}{d t} = -\\frac{g}{\\tau_{decay}}+\\sum_{k} \\delta(t-D-t^{k})\n",
+    "$$\n",
+    "\n",
+    "where $g$ is the synaptic state, $t^{k}$ is the spike time of the pre-synaptic neuron, and $D$ is the synaptic delay. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f30b788",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Afterward, the current output onto the post-synaptic neuron is given in the conductance-based form:\n",
+    "\n",
+    "$$\n",
+    "I_{syn}(t) = g_{max} g \\left( V-E \\right)\n",
+    "$$\n",
+    "\n",
+    "where $E$ is the reversal potential of the synapse, $V$ is the post-synaptic membrane potential, $g_{max}$ is the maximum synaptic conductance. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7de41ac6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 2. The AMPA synapse model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07ffde7f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "A classical model of AMPA synapse is to use the Markov process to model ion channel switch. Here $g$ represents the probability of channel opening, $1-g$ represents the probability of ion channel closing, and $\\alpha$ and $\\beta$ are the transition probability. Specifically, its formula is given by\n",
+    "\n",
+    "$$\n",
+    "\\frac{dg}{dt} =\\alpha[T](1-g)-\\beta g\n",
+    "$$\n",
+    "\n",
+    "where $\\alpha [T]$ denotes the transition probability from state $(1-g)$\n",
+    "to state $(g)$; and $\\beta$ represents the transition probability of\n",
+    "the other direction. $\\alpha$ is the binding constant. $\\beta$ is the\n",
+    "unbinding constant. $[T]$ is the neurotransmitter concentration, and\n",
+    "has the duration of 0.5 ms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0858af",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Moreover, the post-synaptic current on the post-synaptic neuron is formulated as\n",
+    "\n",
+    "$$I_{syn} = g_{max} g (V-E)$$\n",
+    "\n",
+    "where $g_{max}$ is the maximum conductance, and $E$ is the reverse potential."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a8e0ffa",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Synapse Models in Silicon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6c96d37",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The implementation of synapse models is accomplished by ``brainpy.dyn.TwoEndConn`` interface. In this section, we talk about what supports are provided for the implementation of synapse models in silicon. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e5f55f7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 1. ``brainpy.dyn.TwoEndConn``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aa075a6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "In BrainPy, `brainpy.dyn.TwoEndConn` is used to model two-end synaptic computations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "297b0de9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "To define a synapse model, two requirements should be satisfied:\n",
+    "\n",
+    "1\\. Constructor function ``__init__()``, in which three key arguments are needed.\n",
+    "  - `pre`: the pre-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
+    "  - `post`: the post-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
+    "  - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb).\n",
+    "\n",
+    "2\\. Update function ``update(_t, _dt)`` describes the updating rule from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0f5d5a8",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 2. Variable delays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e9c232a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "As seen in the above two synapse models, synaptic computations are usually involved with variable delays. A delay time (typically 0.3–0.5 ms) is usually required for a neurotransmitter to be released from a presynaptic membrane, diffuse across the synaptic cleft, and bind to a receptor site on the post-synaptic membrane.\n",
+    "\n",
+    "BrainPy provides several kinds of delay variables for users, including:\n",
+    "\n",
+    "- ``brainpy.math.LengthDelay``: a delay variable which defines a constant steps for delay.\n",
+    "- ``brainpy.math.TimeDelay``: a delay variable which defines a constant time length for delay."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Assume here we need a delay variable which has 1 ms delay. If the numerical integration precision ``dt`` is 0.1 ms, then we can create a ``brainpy.math.LengthDelay`` which has  10 delay time steps."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b9ced2ed",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "target_data_to_delay = bm.Variable(bm.zeros(10))\n",
+    "\n",
+    "example_delay = bm.LengthDelay(target_data_to_delay,\n",
+    "                               delay_len=10)  # delay 10 steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_delay(5)  # call the delay data at 5 delay step"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_delay(10)  # call the delay data at 10 delay step"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Alternatively, we can create an instance of ``brainpy.math.TimeDelay``, which use time ``t`` as the index to retrieve the delay data."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "t0 = 0.\n",
+    "example_delay = bm.TimeDelay(target_data_to_delay,\n",
+    "                             delay_len=1.0, t0=t0)  # delay 1.0 ms"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_delay(t0 - 1.0)  # the delay data at t-1. ms"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_delay(t0 - 0.5)  # the delay data at t-0.5 ms"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a2bf84",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 3. Synaptic connections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f83608c5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Synaptic computations usually need to create connection between groups. BrainPy provides many wonderful supports to construct [synaptic connections](./synaptic_connections.ipynb). Simply speaking, ``brainpy.conn.Connector`` can create various data sturctures you want through the ``require()`` function. Take the random connection ``brainpy.conn.FixedProb`` which will be used in follows as the example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "61de48c2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "example_conn = bp.conn.FixedProb(0.2)(pre_size=(5,), post_size=(8, ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88b50ec8",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "we can require the connection matrix (has the shape of ``(num_pre, num_post)``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b8e2ac09",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([[False, False, False, False, False, False, False, False],\n          [False, False, False,  True, False,  True, False, False],\n          [False, False, False, False, False, False,  True, False],\n          [False, False,  True, False, False, False,  True,  True],\n          [False, False, False, False,  True, False,  True, False]],            dtype=bool)"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_conn.require('conn_mat')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dff17faf",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "we can also require the connected indices of pre-synaptic neurons (``pre_ids``) and post-synaptic neurons (``post_ids``):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3344a58d",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(JaxArray([0, 0, 1, 2, 3, 3, 3, 4], dtype=uint32),\n JaxArray([1, 7, 6, 7, 3, 4, 6, 7], dtype=uint32))"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_conn.require('pre_ids', 'post_ids')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e86024",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Or, we can require the connection structure of ``pre2post`` which stores the information how does each pre-synaptic neuron connect to post-synaptic neurons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8db2a319",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(JaxArray([0, 2, 4, 6, 2, 4, 3, 6], dtype=uint32),\n JaxArray([0, 4, 6, 6, 7, 8], dtype=uint32))"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_conn.require('pre2post')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "```{warning}\n",
+    "Every require() function will establish a new connection pattern, and return the data structure users have required. Therefore any two require() will return different connection pattern, just like the examples above. Please keep in mind to require all the data structure at once if users want a consistent connection pattern.\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44fa4941",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "More details of the connection structures please see the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc2af88d",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Achieving efficient synaptic computation is difficult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ecabe94",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Synaptic computations usually need to transform the data of the pre-synaptic dimension into the data of the post-synaptic dimension, or the data with the shape of the synapse number. There does not exist a universal computation method that are efficient in all cases. Usually, we need different ways for different connection situations to achieve efficient synaptic computation. In the next two sections, we will talk about how to define efficient synaptic models when your connections are **sparse** or **dense**.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e494598",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Before we start, we need to define some useful helper functions to define and show synapse models. Then, we will highlight the key differences of model difinition when using different synaptic connections. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bd522429",
+   "metadata": {
+    "code_folding": [],
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Basic Model to define the exponential synapse model. This class \n",
+    "# defines the basic parameters, variables, and integral functions. \n",
+    "\n",
+    "\n",
+    "class BaseExpSyn(bp.dyn.TwoEndConn):\n",
+    "  def __init__(self, pre, post, conn, g_max=1., delay=0., tau=8.0, E=0., method='exp_auto'):\n",
+    "    super(BaseExpSyn, self).__init__(pre=pre, post=post, conn=conn)\n",
+    "\n",
+    "    # check whether the pre group has the needed attribute: \"spike\"\n",
+    "    self.check_pre_attrs('spike')\n",
+    "\n",
+    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
+    "    self.check_post_attrs('input', 'V')\n",
+    "\n",
+    "    # parameters\n",
+    "    self.E = E\n",
+    "    self.tau = tau\n",
+    "    self.delay = delay\n",
+    "    self.g_max = g_max\n",
+    "\n",
+    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
+    "    self.delay_step = int(delay/bm.get_dt())\n",
+    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
+    "\n",
+    "    # integral function\n",
+    "    self.integral = bp.odeint(lambda g, t: -g / self.tau, method=method)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0d47e7ef",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Basic Model to define the AMPA synapse model. This class \n",
+    "# defines the basic parameters, variables, and integral functions. \n",
+    "\n",
+    "\n",
+    "class BaseAMPASyn(bp.dyn.TwoEndConn):\n",
+    "  def __init__(self, pre, post, conn, delay=0., g_max=0.42, E=0., alpha=0.98,\n",
+    "               beta=0.18, T=0.5, T_duration=0.5, method='exp_auto'):\n",
+    "    super(BaseAMPASyn, self).__init__(pre=pre, post=post, conn=conn)\n",
+    "\n",
+    "    # check whether the pre group has the needed attribute: \"spike\"\n",
+    "    self.check_pre_attrs('spike')\n",
+    "\n",
+    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
+    "    self.check_post_attrs('input', 'V')\n",
+    "\n",
+    "    # parameters\n",
+    "    self.delay = delay\n",
+    "    self.g_max = g_max\n",
+    "    self.E = E\n",
+    "    self.alpha = alpha\n",
+    "    self.beta = beta\n",
+    "    self.T = T\n",
+    "    self.T_duration = T_duration\n",
+    "\n",
+    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
+    "    self.delay_step = int(delay/bm.get_dt())\n",
+    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
+    "\n",
+    "    # store the arrival time of the pre-synaptic spikes\n",
+    "    self.spike_arrival_time = bm.Variable(bm.ones(self.pre.num) * -1e7)\n",
+    "\n",
+    "    # integral function\n",
+    "    self.integral = bp.odeint(self.derivative, method=method)\n",
+    "\n",
+    "  def derivative(self, g, t, TT):\n",
+    "    dg = self.alpha * TT * (1 - g) - self.beta * g\n",
+    "    return dg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d3640a4a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# for more details of how to run a simulation please see the tutorials in \"Dynamics Simulation\"\n",
+    "\n",
+    "def show_syn_model(model):\n",
+    "  pre = bp.neurons.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
+    "  post = bp.neurons.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
+    "  syn = model(pre, post, conn=bp.conn.One2One())\n",
+    "  net = bp.dyn.Network(pre=pre, post=post, syn=syn)\n",
+    "\n",
+    "  runner = bp.DSRunner(net,\n",
+    "                       monitors=['pre.V', 'post.V', 'syn.g'],\n",
+    "                       inputs=['pre.input', 22.])\n",
+    "  runner.run(100.)\n",
+    "\n",
+    "  fig, gs = bp.visualize.get_figure(1, 2, 3, 4)\n",
+    "  fig.add_subplot(gs[0, 0])\n",
+    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['syn.g'], legend='syn.g')\n",
+    "  fig.add_subplot(gs[0, 1])\n",
+    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['pre.V'], legend='pre.V')\n",
+    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['post.V'], legend='post.V', show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dde06bd8",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Computation with Dense Connections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e5abebb",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Matrix-based synaptic computation is straightforward. Especially, when your models are connected densely, using matrix is highly efficient. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "984c65a4",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### ``conn_mat``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a5bad33",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Assume two neuron groups are connected through a fixed probability of 0.7. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "102c71e7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "conn = bp.conn.FixedProb(0.7)(pre_size=6, post_size=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a791b6c",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Then you can create the connection matrix though ``conn.require(\"conn_mat\")``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4bbb027f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "JaxArray([[ True,  True,  True,  True,  True,  True,  True,  True],\n          [False,  True,  True,  True,  True,  True, False,  True],\n          [ True,  True,  True,  True,  True,  True,  True,  True],\n          [False,  True, False,  True, False,  True,  True,  True],\n          [ True,  True,  True, False,  True, False,  True, False],\n          [ True, False,  True,  True,  True,  True, False,  True]],            dtype=bool)"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conn.require('conn_mat')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c925c9f4",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "``conn_mat`` has the shape of ``(num_pre, num_post)``. Therefore, transforming the data with the pre-synaptic dimension into the date of the post-synaptic dimension is very easy. You just need make a matrix multiplication: ``brainpy.math.dot(pre_values,  conn_mat)`` ($\\mathbb{R}^\\mathrm{num\\_pre} @ \\mathbb{R}^\\mathrm{(num\\_pre, num\\_post)} \\to \\mathbb{R}^\\mathrm{num\\_post}$). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2553fc",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "With the synaptic connection of ``conn_mat`` in above, we can define the **exponential synapse model** as the follows. It's worthy to note that the evolution of states ouput onto the same post-synaptic neurons in exponential synapses can be superposed. This means we can declare the synapse variables with the shape of post-synaptic group, rather than the number of the total synapses. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b8e7b088",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class ExpConnMat(BaseExpSyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(ExpConnMat, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # connection matrix\n",
+    "    self.conn_mat = self.conn.require('conn_mat')\n",
+    "\n",
+    "    # synapse gating variable\n",
+    "    # -------\n",
+    "    # NOTE: Here the synapse number is the same with \n",
+    "    #       the post-synaptic neuron number. This is \n",
+    "    #       different from the AMPA synapse.\n",
+    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    # pull the delayed pre spikes for computation\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    # push the latest pre spikes into the bottom\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    # integrate the synapse state\n",
+    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
+    "    # update synapse states according to the pre spikes\n",
+    "    post_sps = bm.dot(delayed_spike, self.conn_mat)\n",
+    "    self.g += post_sps\n",
+    "    # get the post-synaptic current\n",
+    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4acb4081",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6723a0894e2549f28c3ada067d457ea9"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(ExpConnMat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1eb27017",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We can also use ``conn_mat`` to define an **AMPA synapse model**. Note here the shape of the synapse variable $g$ is ``(num_pre, num_post)``, rather than ``self.post.num`` in the above exponential synapse model. This is because the synaptic states of AMPA model can not be superposed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "37736f86",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class AMPAConnMat(BaseAMPASyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(AMPAConnMat, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # connection matrix\n",
+    "    self.conn_mat = self.conn.require('conn_mat')\n",
+    "\n",
+    "    # synapse gating variable\n",
+    "    # -------\n",
+    "    # NOTE: Here the synapse shape is (num_pre, num_post),\n",
+    "    #       in contrast to the ExpConnMat\n",
+    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    # pull the delayed pre spikes for computation\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    # push the latest pre spikes into the bottom\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    # get the time of pre spikes arrive at the post synapse\n",
+    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
+    "    # get the neurotransmitter concentration at the current time\n",
+    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
+    "    # integrate the synapse state\n",
+    "    TT = TT.reshape((-1, 1)) * self.conn_mat  # NOTE: only keep the concentrations\n",
+    "                                              #       on the invalid connections\n",
+    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
+    "    # get the post-synaptic current\n",
+    "    g_post = self.g.sum(axis=0)\n",
+    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fab4f7cb",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "7fda5a9970ea48b7969e426b7c45a1f8"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(AMPAConnMat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a02e48",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Special connections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69362ac5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Sometimes, we can define some synapse models with special connection types, such as all-to-all connection, or one-to-one connection. For these special situations, even the connection information can be ignored, i.e., we do not need ``conn_mat`` or other structures any more. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b3f691",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Assume the pre-synaptic group connects to the post-synaptic group with a all-to-all fashion. \n",
+    "Then, exponential synapse model can be defined as, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b41ef340",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class ExpAll2All(BaseExpSyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(ExpAll2All, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # synapse gating variable\n",
+    "    # -------\n",
+    "    # The synapse variable has the shape of the post-synaptic group\n",
+    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
+    "    self.g += delayed_spike.sum()  # NOTE: HERE is the difference\n",
+    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d1f3cca3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "54ad84a96a4744ad9549798958f64f86"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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2sYWUghQREUFDQ8OQF7EeLUhSSjpam6lo7VMWIB0qEE4yEyN1qQA60fv8ZSVG6Xr+9MCv1uxlT2Ur9182m3HJfqzVs+0J6O+Bpd8bfr+pFyiKxmf3K26rUfL/3ixkZ0ULf7p0lv/ao+x9Few2RfnzhJmXKS6oD3+rZPGNgt+uLWJ7WTN/uGSm/9qj7HkZpN1z+QrOUAK5P7sf2qpHdeo/vrOPzaWN/O6iGUwaE/rtUQKa5j8SWVlZVFRUUFdXd9z25s4+2ntsmE3Q16C/bvdlLX38c2sz07OT9BkEKxUXYlZCJBtLGoI9GtWRKKUZMhMi+WB/bbCHozrO55XMxEh2VTQHdSwGQ/PWriqe21LOrafks3Jquv9OZLcrClLeCkibPPL+Z94Lxe/CWz9QArl9zIh6d281T20s4+aTx3PW9DE+HcMj9rwMqZNhzAzP9hdCkfGR5fDpn+GM3/h02g/21fDEF6VctzSX82Zm+HQMj9j7imL1Sxk58HuAlb+C/W/Dh/fCBX/36bQfF9fx2KeHuWbxOL4+J7CFKH0lpBQkq9VKXt6JqZq/WrOXJ75QalEW3XMWkWH6qqPz4huFtPcpFohNJY3BHo7fyEyMpLq1m75+u3/86kEmMzGSurYe3dZ6ykyIpKmzj44em1euGwP/U9Pazc9e282srHju/NrIGUHu8DhLqGILtJTDab/w7MAR8YrS8OrN8OX/YN43vR5bXVsPP3llN9My4vi/MyZ5/X7w0AXV1QxlG2HZnd6l7o+ZocTzbPonLLgJEt2XchmKhvYefvTSbiaPieWusz1QOt3gUZZeR71iyTv1p94dPDkfFt6k1EhafKvXAelNHb388MWdTEyL4Wfn+hbM7lGrkbZqpTTByl9BziKfzuOK5lYpvQYxg+LCaOux0dLZF+yh+IWsxEjsEqqau4M9FL+QlahYN/XqhnLKp0srp4aRUvLDl3bR3dfPfZfP9v/Dx95XwBwOk872/D0zL4Ock+CDe6G3w6vTSSm56+VdtPfY+Ovls/0b1Hv4Y6UY5ISV3r/XqXR8dr9Xb5NS8tNXd9Pa1cf9l88m3OLHh6tDHwLSN/lO/iFYo+CTP3v1NiklP399D02dvdx/+Wz/PjyWfARlX4wcW+UhGlSQ9HdzlkgEkJ0UBUBZo76UQInyMJaTpMRE6E4+hwsxxzl/DTqTz/HbGdNypMG7Bc7Avzy9qYxPiuv42TlTRlWE1aNmrlLCvrdgwukjBzAPPvjKX0FHrWJl8YIXtpazfl8td501mYnpvseteNTM9eA6CI9XKmd7S3ymEm/15VPQUuHx217ZXsm7e2v4vzMLmDLWi890EB5ZWA6+r9Q+Gjvb+xNEJcGCGxUXZF2xx29bs/Mob+2q4nsrC5ieGT/yG4bAoySCQx8q8qV76B4dAc0pSOU6tiCNS1YW2CON+lyAcr4i8ulNAXQyTqcKvJapae3mD2/vY+mEZK5e7J1bxycaSxT32oTTvX9vziIoOAs+/yt0NXn0lrq2Hu59q4hFeUl886Rc78/pDVLCwQ8g/xQw++hCXnan8vuzv3q0e2NHL795q5D54xK5YdnIxTxHhZRw6APIP833ytgn3aG0HvnUs+KRzZ293PNGIbOyE7hF7Urgg5FSsQDmrVCt8rdmFKS4CAvhFhPlOr45Oy1IR/RmgZASgZLmH2Y26W6BdVrIUmPCibSadTd/zse2hCgrsREW3c2flrnnjUJ6+u3c+3U/FBN0x6EPlN/jT/Xt/af9XCkwueFBj3b/zVuFdPfZuffCGZj8Vc/JSUsFtFYorUV8JSEb5lylVNhuPTri7ve+VURbt43fXjTDf/WqnDQdho46GLfU92NEp8CCG2D3C9BwaMTdf//2Ppq7+vjdhQGQr6Uc2qpg3EmqHVITCpKzgWtWYqQ+XWwSEBATbiElJkx3LhonZpMgKylSt/IJIchJiqJMpxYyp3y6UwA1yof7anlrdxXfOW0CuSmjT+n3qMxMyUcQn6NUz/aFMTOUlPFNj0J367C7flJcx+s7jvLtU/KZkDb6lP4Rg7QrNiu/s31wr7my7E6lTMAIrsQvDtXz8vYKvrViPAWjcB0OMJILqnyL8tsX96ErS+4AkwU2PjTsbpsPN/LclnJuXJbH1AzfXYdORkwiqFBJPhc0oSA5yU6K0rWLDXAssPqVcZzOF9icZJ3PX3KUrq24WqGrt59fvL6HCWkx3Hyyn10XTux2OPwpjF8xuuasy+6EnhbY9p8hd+nuU+TLS4nm2x70WVOFiq1giYT06aM7TmKuogRu+w/0tLvdpcfWz89f3UNOUhR3nOZFuv1oqNgMYbGjb4kSm64E3X/5tFJU0w19/XZ+9upuMhMi+e7KAMlXvsUxf9NUO6QmFCSnCyM7MYryRv1ZkOBYq5FxydG6W2ClPHY/dSqAHjdV1ABKkLYi4Dg9yofr/EVT3tRJv10/8mmRf31aQkVTF7/5+vTAtWpoOKAoNqN1YWTOhfGnKG62PvcZrf/5vJQjDZ38v1XTA1cyo3yzUh/I7Hl15yFZcrviStzxtNuXn9xwhJL6Dn69alrg5KvYonz2JhXOt/g2sHUNqeQ+vfEIB2rb+dUF04gKC1BJkIrNinxqzJ8DTShITrISI2np6qOlS59p8KAoEEdbuuixqdPXJ9TISY6mvcdGk05LGYxLjqK7z05tmzp9i0KNcclR9PVLqlr0+aCiBWrbunn440OcOS2dxeOTVTvuiHV0Kh1NuzPnjf5ky+6E9hrY+ewJLzW09/DQhwc5fXIayyamjP5cDobtdm/rgepdkDVfnZNlL4CshYobalCPtqaOXh5Yf4CTC1I5dVKaOudjBBdUbydU71HP/ZQ+VQn23vToCX32Wrr6+Nv6A5yUn8zKKSrKN1yWZV83VKk4fw40oyAJjqUZ6zWGBZQFSEp9ljOAY5lQek0V12ugvRO9ljLQEve/f4Bem527zlave7xHVGxVXDTJKrhM8lZAxlz4/G8nKBB/W3+Azr5+fnJOAOWrLYT+XsUCoRZLboOmUti/9rjNf//gIO09Nn4WUPmKlPpOGbPVO+aS26C9WqmL5cJDHx6kuauPn507JTCJA6D0irP3Dd0X0Ec0oSA5lcY8RyDiYZ0trs4gdNDnAuSIQQeOlTLQkxtROnup4KLE60m+Y+Iduz51JJ+W2F/dxvNbyrhmybiB+2HAqNwGmXPUSaEWQkkZbzoMB94f2Hywtp2nN5XxjYU5qgRme0xNofJ7tPFHrkw+DxJyjsvYK63v4MmNpVy+IDuwvchq9ii/VYzPIf90pSXLhn8MLNLljZ385/NSLp6bxbQM32seeU2tY/7SVJQPjShITpyLa2m9vhQkVwZqBelMCXSidwtLZkIkJgFlOp2/jIRIrGbBEUNBCgp/fGcfMeEWvuOnwN5hXRg1e9RxrzmZcj7EZsCmhwc2/endfURazX4J7B02i622ECwRvmfnucNsgYU3Q9kGxb0F/Om9/VjNJp/bwQzHsN3uawvBGg0JueqecNG3oHr3QAbZX97bj8mEz+1ghj0dw8xfzV715w8NKUhCCCKsZsbGR1Cq08UHlFo6UWFmynQUjK4EaSs2iAirmfS4cH1ZIFwsLGEWExkJkbpSIJzNeMFRqiExSlcWTq2wo7yZ9ftq+daKfBKjwwJ78roiJXVdTReG2QoLb1RKB9TuY09lC+/ureGm5eNJiQlX7zyeULNXsYaoEcDsyuyrlIV76+Psq27lrV1V3LAsj7RYdVpheEzNXiV7TaUCigPMuExxu255nIO1bby+8yjfPCmPMfFBkC91ku8FPodAEwqSa/n03ORo3VmQXLOE9F5LB2BcUrSuF1i91wrKTorSbTX0UOav64pJjLKy2k8VpYe3sBQpv9OmqnvSud9U+rpt/id/XXeAuAgL1y3LVfccDoYNQq/Zq677yUlUEky/GHY+zz/f20FsuIUblp3YkF0Nhmw1IqVDPpXnDiA8BmZdAXtf4V/vbiXSaubmk/1UEXy4+astVNc96kATCpIruSlRlOp48QH9L7A5yfpeYPVeK2hckmFBCjRfljXx0f46bjp5PDHhAUqbdqW2SFFkElVe3KOTYeal2Hc8y+aiQ9y4fDxxEeqlaXtER73SI05t5c/J/Bugr4Po/a9w3dJcEqICbP1rr4GuRr8oEIBSWbu/l/j9z7P6pFySAm3d7KhXZPTD/GlCQXINEs1Njqaxo1dXqf6u8oGywJY1dmLXSa0ZZzNeJzlJUdS09tDdp49SBq4WQFBqBTV09NLeYwvamNTE3fXZ2m2jubN3yPcYeEBvh1J80QP+uu4ASdFhrF6S698xDUVtEaQUqO7CAGDRLZhsXayO+JTrluaqf/yRcAb4+sPCApA5lyPhBay2ruOGpf6xHg3LQACzn7Lm0qZwIHImV5k/4GY/Wf+GZcC6qb58mlCQXPkqdBTPSYqix6bvWjqg30yocToPtM/ReaC9X7HbYcNDcP90+G0G/C4TXroe6g8O+ZY9lS18XFzHTcvHE+1H69Gw3e5ri/y2wBbJcWyyT+bGiA+IDfNf0cQhXVD1B5TfKeoHTgMcrGvnH+2nMJFy4uu2+OUcMEy3e2fPNDXKM7jhcH0Hf2tdQY6oIbHqM7+cA45/SDuOBsd3J0V9+TSjIDmf0J2prXp2szn7Kh3WU6yVy9Wd61ByS+r0IZ+zGa8Tp3x6mb/BFrI8PV6fgaDfBi9eC+/+BJLy4PRfKvEbB96HR5bC9ifdvu3RT0qICbdw1eKcAA/YQXer0sTVTwrSY5+W8AJnEN9dCSUf+uUcw9JYogRSx2b45fCPfXKY98RS7OHxsPVxv5xjWBpLwBoFsWP8cvh/fVrChyzCHpUKW/7ll3MMS+Mhxf0bl6X6oTWhILkqxXpM9XfNEgIYn6rU/yipd9/HR2sMfqoZn+pQkHQi32CcCoReFMDB5CRHYRJQUqfP+fMb7/8Cit6AM34D166B5d+H8+6H27dAzhJYczt89tfj3lLR1Mlbu6u4cmF24GNznNTtU377QUGqaulizY6jJMy9CKKSh+3P5jcaS5T0cLUzvIDa1m5e/bKS8+fnY5pzFRSugfZa1c8zLA2HFPn8ULSxvr2Hl7ZVcP7ccZjmrYYD70JzuernGZaGEuWBww/z59ERhRBnCSH2CyEOCiHucvN6jhDiQyHEl0KIXUKIc1QfqYOBVH8dKUiDGRsXQYTVpNsFNjbCSmpsOId1Kl9kmJnMhEjdKhDhFjPZSVEc0vF3UHWOfKG0nVhwk1Ig0XWxih0DV70I0y6Cdb+EHc8MvPTvz0oRwHUBiF0ZMovNjy6oJz4vxS4l3zx5Esy5GvathdYq1c8DDN3t3qlA+IH/biilz27nxmXjYd51SrVnl/lVkyFbjTT6T74nNxyhx2bnxuXjYd5q5QP+8im/nMv5lTmhVlfjIUjyT0PjERUkIYQZeBA4G5gKXCmEGBzN9nPgBSnlHOAK4CE1B6l8Hse7MPRWC8lVtzeZBLnJ0bpaYAc/u4xPiaZEJwusazNeJ+NTdSbfoBkcnxKtWwVedaSEd+6C+BxY+Sv3+5itcOE/lRYca+6Aiq20dPbx3JYyzp+VQUZCZECHfBxNh0GYlarQKtLW3cczm8o4Z8ZYpYDs3NVKO4wv3bsa/YK9X5HPDwpER4+NpzaWcebUMUrYRGqBYinc/r8hNDU/0G9T2p0kq69AdPX2878NpayckqZUPU/IgfxTFQXJHqAEHLsdGg9Dsn8UQE8sSAuBg1LKEillL/AcsGrQPhKIc/wdDxxVb4gnordUf3fflfzUGN0ssO4YnxqjKwVwME4FYsjKxBpnfGoMh+vbg5ppKYT4gRBCCiFSHP+fIoRoEULscPzcHbTBuVL8LlTthFN+rNSNGQpLGFz2X4gdCy9dzysb99LZ28+NywOT+TRkM9fGEkjIVrVLOsCLWyto67Fx03LH4pacD+NPhW3/9csC69bC0lKh9GDzgwLxyvYKWrr6uOlkl/mbu1qxeBz5XPXzuW3m2lKmFPj0g4XltR2VNHX2KdYjJ3NXK/Fqh9SPJXM+pB0nYmsl9Pf4zULmiYKUCbg6FSsc21z5FXC1EKICWAvcocroXBCDgnz1luo/mPGp0ZQ3dtJj00cq/GDyU6Np6uyjqUOfqeJ5KdG099io02kmYl5KNN19dqpau4NyfiFENnAGUDbopU+llLMdP/cEYWgnsvFBiM+GmZePvG9kIlz8OLKlgszP72ZBbmJge1q5wxmjoyJSSp7adITZ2QnMyk449sL865QF1qU/m19pLFF+q6xASCl5cuMRpmfGMTcn8dgLU1dBeLxiRQoEDQ75VFYApZQ8ueEIk8fEsigv6dgLk86BqBTY/oSq5xuSRkeGXrBcbB5yJfCElDILOAd4UghxwrGFEDcLIbYKIbbW1dV5cfjjtWK9ZXkNzhICRUGyS300rXVtxutET4Hars14nTgD7Q/pwA3l2ozXycD8Bc8KeD/wI4Yp/hwSNB2Bw5/AnGs8t8DkLKJs2rc5o/9j7swLcMCrOxoPq14gcsOhBkrqOrhm8bjjX5h0DsSkBy5Ye2CBVVcB3FLaRHFNO9csHnf8vS8sCmZeCoWvQ1eTqud0y4ACqK58X5Y3U1jVytWD5bOEwewrYf/bgQlG95N8TjxRkCqBbJf/sxzbXLkBeAFASrkBiABSBh9ISvmolHK+lHJ+amqqbyOGgS7PB2u1v7gOxfgU/Syw7tC9fKn6UuIHk+9QAIMhnxBiFVAppdzp5uUlQoidQoi3hRB+6B3hJTufBYSyaHjB79vPpZQMlhTdC72BeUhyG6Td2QjdzaovQE9tOkJClJVzZ449/gWzVVEmD7ynejaU22auzWVgDlPcmiry1MYjxEZYOH+Wm9IBc68FWzfsekHVc7pt5tpSpqTAx6Sreq6nNh4hOszM1+cMdiYBc65V3HoqB6MPBGm7bmwuB5MF4vxTosETBWkLMFEIkSeECEMJwl4zaJ8y4HQAIcQUFAXJGxPRiBxXyTcpCqtZ6FtB0puFZZAFIitR6Qqvh0Bf12a8TjLiIx2ZiNqfPzcGJNJiw4kOM/tt/oQQ64QQe9z8rAJ+CriLL9oOjJNSzgL+Drw2zPF9tGZ7ya4XIO9krwKcK5u7eHd/E19M/QWi+Qh88if/jW8kmg4rv5PUsyDVtHbz7t4aLp2XRYTVTWFIZzZUINxQLRUQl6lqinhdWw9v76ni4rlZRIW5Kew5dhaMna3EWvk7RrGlAuKzVE3xb+ro5c1dVVw4N9N925tABqO3VCjKkdpNhh2MeFVIKW3A7cC7QBFKttpeIcQ9QogLHLv9ALhJCLETeBb4plQxOnXwkSxmE7nJ0bpRkAZn6cGxVHg9KBDusJhN5CRF6UOBcMNAJqJOLUhCCManxnDIT/MnpVwppZw++AcoAfKAnUKIUhSL9nYhxBgpZauUst3x/rWA1RnA7eb4qlizh6X+oOLCmXK+V297bnMZEli+8usw8wrY8KBi6QgGjU4FST0L0nOby+m3S65aNM79Dgk5MPFrygLb7+c4U6cCoSIvbC2nr19y9WD3oSvzVkPtXqjcruq5T8AP8r24rZxem314+fwYjH4cLRVKfJ+f8EhtllKulVIWSCnzpZT3OrbdLaVc4/i7UEq5VEo5yxEc+Z7fRuxgQpr/bs6hgpIJpV8ZlUwofSoQ4MhE1PX8BT7VX0q5W0qZJqXMlVLmoiSNzJVSVgshxgiHKU8IsRDl/tYQ0AG6UvyO8rvgTI/f0m+XvLi1ghUFqUrq++m/UJ7+1/s/3txtt3unBSlhmMXQC+x2yQtby1k2IWUgltQt866D9molA1Al3LYaUXmBlVLy4tZyFuUlDYSCuGX6JUp16+3/Ve3cbrvd+0G+57eUM29cIpPHxA2949RVEB6nqhXQaUI4zvYSCgpSsHFXZ2ZCWgxHGjp0kuUl3VpAx+sk1X9ws1Mn41OjOdLQSb/Gm/IObsbrZHxqNOVNXfTaPGtIGqq4c5GCEkd2tKUrlJoOXwLscViyHwCuUNOS7TXF70DaNK/ca18cqqe6tZtL5zlu+vFZsOR22P0iVGzz00CHoaVCyUoKi1LlcJsON1LZ3MWl80ewakw8Q2n94c9g7f4+aKtS1cKy7UgTpQ2dXDp/hEU7Ik4pDLrnZejx00OUrRfaqlWVb2dFC4fqOrh03gjHDIuCGX4ORu+3KWn+KlvIXNGEguSO/NQY7BJK67Wf5TUU+anRNOs4FT4/JYbefjsVTfqcw/Gp0fTbJWWN2ldy3TE+NRopgxuI7rAk1Tv+/oeUcprDkr1YSvlF0AbW2wFlGxRXkRe8tK2CuAgLp09JO7Zx2fcgOg3e+3ngCgw6aamEeDeBuD7y0rYKYsItnDF1hL5gZgvMvQYOrlcyAf1BWxVIu6oL7EvbKogKM3P2dA/6ns29FnrbFSXJH7RWAlJl+coJt5g4Z3BwvTvmrXYEo7+o2vmPo71aKSxqKEgnVvJ1mi/17GZzBmrrQcbBQcygL/ncmZCcmXoHa7WvIA3+/oHO5k9tKrYqmTy5yzx+S1t3H+/ureb8WRnHBy+HxypFJsu+gEMf+GGwCm5dUK2VqjUB7eix8faeKs6dMZbIMA+Caudco5guVXLTnNDtvqVC+a3SAtvd189bu6o4a/oYot0FLw8meyGkTlZPvsEbnPIlqOOC6rH188bOKs6cNsazvoBjZyk/29UJRj8hi21g/r7qLjY3pU6cN2c9BGoP5YKamBYLwAGNy+hu/uCYfMU1GpdviO/+sXIUbQEcjfoM5aXKT43BJOCAxufPL5RtAARkLfD4LWt3V9HdZ+cSd+6LOdcqC8GHvw2sFamlQjUL0jt7quns7eeSkdxrThKyYcLXlNYV/gjWVnmBfXdvNW09Nvfz5w4hlGDmyq1Qs1eVMRyHyvKtL6qlpavPc/lAka9mDxz1QzC6ygquOzShILkjKsxCZkKkLhSkochMiCQqzMz+am0vsEMRH2UlPS6c4hp9yhcdbiErMZL9OlUgIqxmxiVH63b+RsWRLyB9OkQmePyWl7dVMj41mtmulaWdWMLg5B8qi+kBP+XADLawdLdCT6uSBq8CL2+vYFxyFPPHJY68s5N531QtWPuEViPOzECVFtiXt1eSmRDJ4rxkz98083KlDtO20Qdrn9DM1alAqFQj6OVtFYyJi2DpBLeJoe6ZcQlYIlWSb1CrkRZHnayvuoLkLkgblCd0vShI7uQzmQQT02I4oHkLhHsLGUBBeqwuFtih5JuUHkuxxhXcob5/ABPTYtivg/lTlf4+qNgC45Z4/Jbyxk42lzZy8dwst+5oAGZ/Q8km+/DewFiRWh31gFVYgKpauvjiUAMXzskcWj53+DNYu6UCIpNUCUCvbevmswN1XDgnE5PJC/mik5UyELueh76uUY/jOFodAfbW0Tc6buzo5aPiOlbNycDsjXwR8TDtQv8Eo7dUQETC8P0NR4kmFKShmJAWQ0mQG2aqwXD3uoL0WPZX60MJdMek9FgO1rZrPpNtKCamx1JS305fv7Yz2YZi0phYjjR0hlImW/CpLYS+Tshe5PFb1u6uAuD8mcM87ZutsOLHSuPb/WtHO8qRaVFPQVq7uxqAC9xVlh4OfwZrt1WrZl15Z081dgkXzPbheHNXK9XKCwfXXx4lbdWqVQh/Z081/Xbp/fyBEqztj2B0FeUbCs0oSO501glpMXT32alsVlnzDiEmjYmlvr2HRp1mshWkx9LdZ6e8UduZbEM9FU8aE0Nfv6RU4+UahnpmnJgeS79d6ragqU9U7VJ+j53t8Vve2l3FrKx4cpJHsGbMvFwp2vjR71W3Ip3ggmp1umhG72J7a9dRpo6NG+hR6BUqBWuf0O2+vVq1Fhxv7qqiID2GgvRY79+cu1yZ01HWRDqh231bNcSqI99bu48yPiWaqWOHqX00FNmLHMHo6tR8GohpVVG+odCEgjTUbWCiIwhW6y4apY6O+yVoYrozkFm7Mg5VRwegYIwin5bdNMOV2tFDIPpwy/CkdGcigXbnT3Wqd0FYjMfVp8saOtlV0XJiXzJ3mC2w7PvKOQ6tH+VAR6ClAoRp1E/plc1dbC9r9kw+d/grWLutBmI9SMcfgZrWbraUNnLuDB+tUSaTkvJ/5HOoKx71eAZor4GY0ctX397DhkMNnDtzrHfuUScDwejboHr3qMczgEryDYcmFKShcC6u+zQe4zEck3SgIA2HU8k9oFP5JqQpmV5aVgCHIy8lGotJ6DaRwCeqdsKYGR7393rL4V47Z4aHCsTMyxWrzqf3+TpCz2g9qixAZg9S1ofhbYd853oqnzvmOytrvzOqsQxgt0NHrSoWpLd3VyElnDtzFIv17KuUpqtqVda290N7rSoWFqf70GcFF2DWFUrTXBWCtQHFTNZeY1iQnLjTXOMirGQmROpCQRpKMU+PCycuwqLpBchdrzknesj0Gs5CFmE1k5scrWkF0F0zXidhFhN5KdGatpCpir0fqvco9V885M1dR5mdnUBWoofBwpYwOOkOxeJQttHHgZ7ICd3u26pVsbC8sauK6Zlxw7cWGYkJX3MEaz/h8yGOE6+zQalTpYJ8b+6qYvKYWCak+eBecxKTBpPOgZ3Pgq3Hp0McVyeos0EpoqiCheXNXUfJT40eeFj3iagkmHqB0ry517dwimNZeijVuft7DQsSDO9qnzI2lv3VrYEbjB8YTj4hBAXpsbquNTMpPVbTCsRITEzXd6aXXjIRVaHhEPR1wJiZHu1+uL6DvUdbOc/bp/O51yoZWP60IrXXjNrCUt7Yyc7yZt/dT07MFkVmtYK125Wg8dHKV9XSxdYjTaOzjjmZt1pRbPa9OfpjtTnkG6UCWNvWzabDjZw7M8M395orc1dDTwsUvja644CLfIYFaVgmj4njUJ1eerK5p2BMLPtr2oaNddEyE9NjOVSn40yv9FhK6zt0m+lVkB5LWWMnnb22YA8l+NTsUX6PmeHR7u/sUW70Z3u7wIZFw+Jb4cC7qsZ1HFfUVQUXxrt7FfnOmaHCk/6cq0cdrH0sgLlG+T1KBeJdx/x51HpjJMafBvE5o3ZDSaf7CUYt33t7axT3oRoKYO4ySMpXx802oOAaFqQhKzEDTB6rZNFouR6SZOgsIVAW2JauPmrbfDO9Bh/3zXidaD3Ta7g6T6AogHaJZjO9hmrG66Qg3VkxXLvfQdWoLwYEpEz0aPd1RTVMz4wjM8GHWjULb4SwWPjsfu/f64bj5rjfBh31o7awvF9Yw+QxsYxLHoV7zclAsPaTvgVru96EVLIgrSuqJT81mnxfsvMG4wzWPvwxNJZ4/fbj5q9NLflqGJccNfAdHxVCKFay8o1QW+T9210lVEnBHQlNKEjDMXmMkna4r0q/Jn5n6qiW45CGw5nppVc31KSBTD1tu4KHYiATUafXp1fUF0NCjkfF+erbe9he1sTKKT4uYpGJsOB62Puq4tpTk856QCqxMT7S1NHL1iOjkM8d869TrCOjDdZWwcLS2t3HxpIGVk5VUb45VymZg6Ptz6aCAtjRY+OLgw2snJI+eveak1nfAJNVBfkc86dSmYah0IyCNNT85CZHEWYxsU/jcUjDMWkgW0+7Mg739ZqQFoPFJCiq0rB8w9xA8lKiCTObtK3EDzOBucnRhFtMhoIESpp26iSPdv1gXy1SMjoFYvFtyoLz+d98P4aD45q5qrAAfVRcS79dqqtAOIO1t3pfWft4C0sNhMePqsr0x/vrsNklX1NTAYzLgIlnwpdPe20lOy5Iu61GqTJtjfB5KJ8eqKO3366ughuTCpPPVYLR+7q9eutxQdrtNUopDT9W0QatKEjDhN5YzCYK0mM0nck2XJYQQFJ0GGPjI9h7VJsKxEihUxFWMxPSYijUqnzDVgoCq9lEwZgY3c6f2SSYPDaOpk4/NBTVEvZ+aDgAKQUe7b6usIax8RFMy/Ch+J6T2HQlNmfns8fcKmrQNnoFaV1hLamx4czMjFdpUBwL1j70ATSV+n6c9tEXGVxXVENSdBhzcrzoLecJ876plCDY/7bvx2gffQbi+4W1xEdamZ/rB/m6mqDoDd+P0aZekc/h0IaCNAKTx8RpWkHyhGkZcZpdYD1h6lh9yzdtbDx7j7boNtD+5VuW8JfLPE9t1yUt5WDr9khB6u7r59MD9eq4L066Q0lZ3/jQ6I7jyigtSD22fj4urmPllDTvepN5wlxnZe0nfT9G2+gy9Pr67Xy4r5bTJqd515vMEyasVKxko6mJ1F47Kvdov13ywb4aTp2UitWsspqQt0LpKThq+QwFaYDh7iGTx8RS19ZDfbtWg5hHZurYOErq2unq1V4m1HDNTp1MzYijtq2HOg0Goo8UpA0wLVOxsFS3emdWDhVGks+i9k1UizirIHvgYvviUD1dff3quJ+S8pSGoFv+DV3NPh/muFYjAwqSb4vsppJG2nts6rpnnMRnKU1svQzWPq7bfUcdRKf6PIQtpY20dvtJPtf+c81lHr/tuG73HfWjkm97WRNNnX3qukedOIPRSz+F+oMev815D5I45y9F/bENQhN3tZGeuaeM1Xag9kguGoCpGfHYpbbjkIZjWoZiht97tCXII/EPzh5Geyv1OX8GODLYgOSRM9jWFdUSHWZm8fgkdc699HvQ2wZbH1fneO21o4rRWV9UQ4TVxNIJflrE5n1zdMHanQ2jWmDXF9USZjGxfKKf5JtztfLbVytZZz1E+T62dUU1WM2Ckwt8V7KGZc7VIMy+W5FGOX+eogkFCRiyVxkcU5AKq/S5uAIDcQpadUMNN39wTIEo1Gig9kgWsilj4xBCw/OnVhaLnmkqhYh4iE4edjcpJR/tq2XZxBTCLWZ1zj12puKa2fgI9KnQvLu9elQumg/317E0P4UIq0ryDWbC15R2Kz4Ea9PfB93No1IgPtxfy+LxyUSHj64Ny5Ak5MCE0x3957ysL9bfB90tEDX8dTgcH+2rY0FuEnERVp+PMSyxY2DS2bDjGbB52Yhd2qGrcVTz5ymaUJBGittIig4jMyGS3Vp9OvfABZWVGEl8pFWTC6wnFrL4KCtZiZEalW9kosMt5CVHa9JCpte4KdVpKoXE3BF3O1TXwdGWbvWfzpfdqQT37njGp7cf1+1+FDEeRxo6KGvs9J/1ARQ31JxrvArWHuh239mobIjyzXpX0dRJSV0HJ/vLeuRk3jeh7SgcfN+rt8nOeuWPERT1oahp7WZ/TZt/5w8U+TrrYf9bHu0+4CLtbFKUpFEogJ7ikYIkhDhLCLFfCHFQCHHXEPtcJoQoFELsFUL49g0dBTMy49ld0Rzo0wYMIQRTx8ZRqMEF1lOmZcRpNpPNE6ZkxGnWQmbgAR4qSJ8U1wFw8kSVF6BxSyFzPnzxgPdWh8GMIsZjQD5/L7ADwdpe1tTpcCoQvsn36QHl/Sv8LV/BWRCd5nX/OdHlVABHOX9qX5+DyT8N4rO9tgKKztHNnzeMqCAJIczAg8DZwFTgSiHE1EH7TAR+AiyVUk4Dvqf2QEeysMzIiqe0oZPWbm2mGnviwZiaoWTr2TTYksMj+cbGU9rQQXuPFltWjCzgtIw4Kpq6aNFgOrzhYRsBux2aj3imIB2oY3xKNNlJHjan9RQhFCtSU6nP/a6Oa+bq4wL0cXE92UmR5CarLN9gBoK1n/IqWFt2Nih/jEKBGBsfwYQ0/9bgwWxVgpmL3/WupIFTAfTRwvLJgXpSYsKZMnYUzWk9wWRW+rMd/hjqD3j+voH5Cw0L0kLgoJSyRErZCzwHrBq0z03Ag1LKJgApZa2ag/TEwD/dUWtjT6X2LCyeOjCmZcTRY7NTorGWHJ56aKZlxCEl7NOYlcVz+RyB6BqLlTMcbB7QVqV0Fx9BQeqx9bOxpMF/wb2TzlHKDHz2V88vTAcDOnC/TcmG82EB6uu3s+FQPcsnpgYmbs0ZrO1BzSDncMQoFlhbv53PD9azfGJKYOSbf50y8K3/HnHXgeGMwsJit0s+O1DHyYGSb95qpdDpln+NuKvTRSq6HPMXChYkIBMod/m/wrHNlQKgQAjxuRBioxDiLLUG6CkzNKwgeYreM72mZWo7EH0kBgLRdSrfVxrnE37CuGF321raRHef3X/uJ5NJyWir2Q2H1vt2jO5mQPpkYdl+pImO3n7/u2ecOIO1vXFDjUKB2FnRQmu3zf/uQyfxWYrSu/1JjytPiw7fLWR7jrbQ1NkXOPli0mDa15W4uR7PejmORsH1FrWCtC3AROAU4ErgMSFEwuCdhBA3CyG2CiG21tXVeXxwT+rMOAO1d1VoU3kYKcsLID81mgirSXMyjtSM18mYuAhSYsI0J99IzXidpMaGMyYuQnPyefL9+8rjVJBGsCB9cqAOq1mweLwfb+4zLlWUhs/+6t37hFCMTgMLkPdBzJ8eqMdsEpw0wf+LF3B8sPYIDV4HrmGnCyrSF/nqFE+mv8oXuGPhzUrW1t5Xht1twMLSWQ8IpVeflzjjq5b5OwDdlQU3QU8r7H5h2N0GLICjdCF6gycKUiWQ7fJ/lmObKxXAGilln5TyMFCMojAdh5TyUSnlfCnl/NRU9TXUGZnxmrQgeZolZDGbmJ4Rz87yZv8OKEgIIZiVlcCO8qZgD8VvzM5OYKeOkwm+sjSVKk1G47OH3e2T4nrmjUv0X3o4gCUMltymFOIr3+L9+0exAH1yoI452Qn+Sw93x7zVSjzL5pHdNICiAEYmKsqVl3xSXMfMrAQSosK8fq/P5J0MKZNg86Oe7d/VCJEJPsn3cXEd0zLiSIkJ9/q9PpO9EMbMgM2PeeQWFp0NEB4HFv+P0RMFaQswUQiRJ4QIA64A1gza5zUU6xFCiBQUl9vw6ryXeOIP1XqgtifMzk5gz9FWem3aCtT21J89OzuBQ3UdmptDTy0ss7ITONLQSWOHl7U/goxRB2kEmo8oVhvL0AtnfXsPRVWtLA+E+2nuaqVZ6ed/9f69Prowmjt72V3ZEhj5XInLgKmrlGBtT9w0nQ0+KX+t3X3sKG/2f3r/YISABTfC0S+hYtvIu/tYJLKjx8b2I02Bnz8hFCtSbSEc+WLk3bt8mz9fGFFBklLagNuBd4Ei4AUp5V4hxD1CiAscu70LNAghCoEPgR9KKRvUGqSnoYZajUOSeJ4lNDsngV6bXVOd072JFZ2VnQDArnLtzKE38s12yKclK5Inday+8rQeVeJFhmFTiZJ+fVJ+AG7u4TGw6Fuw702o2+/RWwZuQT4qSJsONyIlgXOvubLoFuhpgV3PDbnLcUHMPigQW0sbsUtYEoj5G8ysK5Tu9VseG3KXAfk6fMtA3HakCZtdBub6HMyMS5Uiq8PI50T4qOD6gkcxSFLKtVLKAillvpTyXse2u6WUaxx/Synl96WUU6WUM6SUQ1+lfsSpIGktxsMbnAusXt1QszSoQHjDzKx4TAJ2lDUHeygGatJaqVgyhmFjSQPRYeaBjFu/s/BbYImEzx/w7n0+KkgbSxqIsJqYmRUg+VzJWgAZc2DTP0d8YhEd9T4pEBtLGgkzm5ibo3J3e0+IiIOZl8OeV465QIdAsSB5r0BsLGnAYhLMGxcE+cKilFiyojegrXrYXUWA2oyARippg2cujMToMHKTo9h+RHvKg6cOjMyESFJiwtihJQuLFxaI+Egr41Oj2aGhOCtPmvE6iQ63MDEtVlMKoBGkPQJSKhYkDxSk+blJ6ndHH4roZCU+Z9fz0FLh8dtkZ4NirbBGeHW6jSWNzBuXqF77FG8QQrEi1RdDyYfD79vV6FMA+saSBmbnJPivfcpILLwJ+ntGLIwpuhp9VpBmZsX7Nz5uOOZfD3bbiBmJviqAvqAJBcmbVgdzcxLZXtasqfYI3gxVCMHsbJ0HMmclsKNcW3PoDbOzE9ipY/m+cnQ2gq0b4oZ2sdW393Cgtt2/2WvuWHKb0pZhw0Mj7nq8C8o7BaK5s5d91a0szguCe8bJtAuVDvab/un2ZSWOTirz5eUC29rdx57KlsDPnytpUyB3uVJ52t5/wsuORio+KYCdvTZ2VQRZvuR8pZ/g1v+4LfzpjIMU3c0+t4nxFk0oSIDHj7BzxyVS395DRZMKDRtDFC0GMnsT4zs7J4G6th6qWjyr+xEKeFKmwcms7ASaOvsoa+z044jUxYjRHoZWR1LvMBakzYeV+KPF4wNzYx8gIUeJ79j2hKIYeIIPCsRmR/zR4mDErzixhCtWiOJ3oeGQ212i6EHY+5QAdi/YVtqEXQZh/gaz8CZoKYN97vuXKfLZvE7xd8YfBVVBAqWkQXs1FL7u9uVwehG2Lq/nz1c0oSB585zt9A9v05CbTQnS9m6BBQ0FMntpKJmVlQCgGTebt0HMx+LImtUfjB8INTuXEOJXQohKIcQOx885Lq/9xNEzcr8Q4syADGhAQRpcP/cYG0saiApk/JErS78LfR1KGvUwDCj5nQ1eBzFvLGkk3BKk+CNX5l+vpPwPUZk5HkcXAi8ViI0lDcGLP3Jl0rmK0rvxRIugEC7yealAbCxpwBys+CNXJnwNkifAhn+c4FoRQNzA/CUEZDiaUJC8YdKYWKLDzGwv046C5C0zBxQIfco4ZWwcYRaTZhQIbylIjyHSauZLI1B7NNwvpZzt+FkL4OgReQUwDTgLeMjRS9K/eGBBCnj8kSvpU6HgbNj0CPR60KbIhyyhjSUNwYs/ciV2jOJq+/Ip6Dkx0zdBOMoAeLnAbixpYHZ2EOOPnJgtsOjbULbBbcq/7/I1Bjf+yInJBItvVUoalG044eV44ZuC6/NwAnIWFfDUvmI2CWZlJ2jKggTeBcHGR1qZmBbDVg3J6I2LJsxiYkZmPFtLPXQJhADeyGcxm5iVHa+xa1QTPrZVwHNSyh5HwdqDKL0k/UvrUTBZlLYJbqhv76G4pj247plldyqxKV8+NfK+nd7FsDR39lJU3Rp894yTRbcolZm/fPqElwYWWC8sLG3dfeyubAm+e83JnKuVQokbHzzhJV8UiM5eGzvLm0Nn/mZdqYx/gxv5fLSQ+Yo2FCQvbfzzxiWyr7qNzl5tdIX3JVh3QV4S20qb6LeHmgPkRHwZ4YLcJHZVtNDVe2IwYqjhS6z1wtwk9h5tob0n9K/REI0lv10IsUsI8W8hhHM18KRvJOB72yO3tFRC7FjFteOGY/FHQVyAchZBzhL44u9uA2BBUfKt2BC97V614RiIPwqVBTZrPmQvVhSI/mPfr+NcUF4oEFsH4o9CRL6IOJh7Lex9DZqPXe4C4ZMCETLxR07ComD+DUqclUssmRC+W8h8RRsKkpfMzUmk3y7ZqZUYHR9YmJtEW4+NfdX6bHy6MC8Rm13ypU7diAvykrBLNFmSIhAIIdYJIfa4+VkFPAzkA7OBKuAv3h5f1bZHI9RA2lraRLhFaRMUVJbdCS3lsOflIXfxJcZj25EmwswhEH/kytLvQHMZFL523OZ4HxbYrUcaMZsEs3M8f4/fWXSL8nvTI8dt9km+0iZMAuaGknwLbwKz9UT5fIwh8xXNKEjeBDHPcUz0tiPacdF468FYkKc84W05HPoySim9yvICmDcuCSFgy+HQVyA8bcbrytycRMwmwRZNuBE9a8ar6hmlXCmlnO7m53UpZY2Usl9KaQce45gbzZO+kerTVq3EvgzBtrImZmUlEGYJ8u124hmQNlVpYmt336rIFxfUtiNNTMuMC358jisFZ0PyRPjigeNMoL5aWKaOjSMqLMjxOa4kZMO0rys1kbqPPST7okBsL2uiID2W2ED2zxuJ2DFK9uWXTx2XfenL9TkaNKEgeZsllBAVRkF6DJtLQ39xBd9cUJkJkWQmRLJFIzJ6S3yklUnpsRpRILwnOtzCtIy4AfeLgecIIca6/HshsMfx9xrgCiFEuBAiD6Vh9ma/D6ijFmLcK0jdff0UHm1hbrCzg0DxUSy7E+qK4MC7J76MqwLhmTWo12ZnV2UL84Kd3TUYkwlOuh2qdsLhTwCHC0p0IIUZwmM9Ooyt387O8pbgZ3e5Y/FtjlgrJa5MCI7JFxbj0SH67ZIdZc0hKt+t0Nc5UDhSAAmiA4nw+PocLZpQkMD7J/TF45PZWtpIX7+2mrp6w4LcREf/o9AMEnHFFwvEwrwktpc1YdPAHPrSzHVBbhI7ypvpsYV+nFWIhWj/UQixWwixCzgVuBNASrkXeAEoBN4BbpNS+vfD7euG7haIce+m213ZQl+/DJ0FaNpFSpr4Z/e7DS47FuSb4NHh9h5toddmDx35XJl5BUSnKVYkBwm0K7J5+H3dV91GV19/aCi4g8map8SVbXx4INYqgXZkZKLH8h2obaOtxxaa8zdmOow/BTY/CjaluXccHcjwuCHj/dRGEwqSL+v/4vHJdPb2a6Mvm4+tHBbmJVPf3kNpQ2gXHPRVfVuQm0Rnbz97j4Z2nJWv+umC3CR6bPaQb64cavq3lPIaR8/HmVLKC6SUVS6v3evoGTlJSvm23wfTUav8jkl3+7IzUzFk4jvMFjjpO1C+acCy4kqcly6oAflCcYG1RsCim+HgOqjZCzgsLF6614DQVCAAltyuFI50xFop8nluXQl9+e6AtirY/QLg/fyNFk0oSL6w0BGjs7GkIcgj8R8L85SLWgtxSL7gnEO9utkW5Crzt0mn8/eVoH1kBSkvJZrkmPAADmoE5lwDsRnw0e+O036dLhrAYwvS9rImshIjSY/zrm9bwJh/A1ij4Iu/H8ti81JBGhMXQUZ8iMo36RxImQSf/gWknXi8VwBTYsLISYry3xhHw4TTYcwM+OyvmLArFjJDQToRbz0YKTHhFKTHaEZB8sVFk58aQ1J0mCYWWF8sZOlxEYxLjgp5+byNkXOSHBPOhLSYkI9D8qYZ71eO9hrlt5saSFJKvixrCn715cFYI+DkHyiF+AY1dvUmiFlKybYjTaFrfQClntOca2D3i8R0V3ltgdhepsjny/05IJhMsPz7UFtITv2nDvk8n48vy5qZmxPC8gkBy74PDQfIqfnAsCC5w1cT/5LxyWwtbQr5OCRfF1ghBIvHJ/HFofqQjkMazdBOyk9mY0mDJuKQfOGk/GQ2H26k16ZP+XSPU0GKPlFBKmvspL69l7njEgI7Jk+Ycw3EZ8OHvx34ggrhCGK2RoElbMRDHG3ppqa1J/QUwMGcdAcAs8v+55WFpaa1m4qmroGs6JBl+sUQn8PsI/92yOeZi62hvYfD9R2h6R51ZeoqSMpnxuF/kUA79vDAlZPQhIIE3jUDdbJ4fDJdfRqJQ/KRpRNSqGrppqTegxYCQcTXJ5SlE1Jo67axK8TjdHx9AFs6IYXO3v6Qb6viy/fvK0G7o8hk9IlB2iEd32EJh+U/gIotSoyOA29cUCEtnysJ2TDrSiZXvUaaaEZ66j7UinxmKyz9Dmktu8gRtR4rgNsdrY5CXj6TGZbdSXLbPvJNVR7PnyqnDtiZRoGvFhYtxSH5uvwsn6DcmD8/WK/eYFRmNLatk/JTEAI+PxC68o1GwMXjkzEJ+OzAKKs5+xFfv39fCdprlKrTbiwu2440ERtuYWKaZynlAWf2VUpGm4sVyZsg3+1Hmoi0mpk8JkTlc2XZnZjsfUSJHq8UwDCLiWnBLvDpCXOupjMsGZOQHitI2440YTULZgSjgbK3zLycjgglzk+GJwTstJpQkHwlOSacyWNi+SyUF1dG54LKSY4iOymST0NcRl9Jig5jWkYcn4WwAjga4iOtzMxK0K18uqe9ZsgA7R3lzczKTsBsClHrmyUMTv4RHN0OxUpdpDg6PVYgvixvZmZWPJZgNOD1luR8DqWfCYDdwxidHeXNzMiMD36BT0+wRrI352rAcwViR7lSADOkCnwOhSWMwtzVANgDVAMJNKQg+erCOLkgla1HGukI8Z5Xo4mRWzYhlY2HQjtOZzRLxNIJKWwvawrpORzN/C2fmMLOihZau933yAoFQjWGM+i017oN0O7u62d/dVtotd9wx6wrIDEPPrwXpFQsSB64MHptdoqqWpmVPfK+ocKOcdfTIy3IhHEj7mvrt7P3aGvoz58L+7IuZV3/HGzZJ424r90u2VPZysysBP8PTCUOZl7I2v6F9OacHLBzakJBGo2FZUVBKn39MqTdbKONr142IYW2ntCN0xltAPmyCSn09Us2h2i6/2gdUEsnpNBvl2w8FJrXaAjH/wef9hq3CtK+6jZsdhn6C6zZCit+DNW7GF//IXGiAzwIgi2uaaPXZteGe8ZBU0w+C3oexjbx7BH3PVTXQVdff+jPnwt9lhhu7Psh/WNmjrjv4YYO2ntszNCQfDZLFLf2fQ9b+qyAnVMTCtJomJ+bSKTVzMfFoRvjMVpOyk9GCELblTgKC8SC3CTCLKaQjkMaTRDznJwEIq3mkI4jMwxIQ9BR5zaDbVdFM4A2ntBnXAopk1h8+B+OOjMjL5rOxJdZWpDPgUDQSrRH5tCdWpo/B95YeZ3Xp5bmLxhmbN0rSOEWMyflJ4e8gjSaBTYxOozpGfF8GqKBvqM1QERYzSzITeSTUJVvlCaWcIuZhXlJfBKiCqBhQBqC3k6lV1R08gkv7apoISUmjLGhWmDQFbMFTr+bxK4jxIhuj4J8d1c2Ex9pJTsp0v/jCwK7K1qICbeQlxwd7KH4hV0VLURazeSn6lM+tfBIQRJCnCWE2C+EOCiEuGuY/S4WQkghxHz1hjj6G/SKSakcaeikNERT4dXIEjplUirbjjTR3NmrwohCj1MnpVFc0055Y2i3VfGV0yancbi+g5K69mAPxcBTuhwu36gTFaTdFS3MyIwP3QJ8g5l8LtVxM5S/PYhB2lXRwswsDcnngicPNLsqW5ieGYcpVAPsh8GT57XdFS1My4jTRoD9IAKZVTvipyOEMAMPAmcDU4ErhRBT3ewXC3wX2KT2IB3H9/m9KwqUVPhQtiKN9j5z+pR07BI+2h+aMo72NnP6FCVT6IN9taMfjB8Y7fydNllx04SufNpbKPxOpyNmbJCC1Nlr40BtGzM05r7YOP47AMjYjGF3dQagayn+CDz/jjoD0LXkXgPP77HOAHQtxR9BcNz8nqiPC4GDUsoSKWUv8Bywys1+/w/4A9Ct4viA0QeJjkuOJjc5io/2h+bio0YQ7MzMeFJiwllXVDP6g6mNCvLlpUQzPiU6JOVT43kmOymKgvQY1heF3jVqBGkPwRAK0t6jrdglzNLYAlSVMI8VPfdhLzhn2P2KqlodAegJgRlYgHEGoGspQNsbDta109XXr634oyDhiYKUCZS7/F/h2DaAEGIukC2lfEvFsanKaZPT+fxQQ0inio8Gk0lw2uRUPi6uC/nWKr5y2uQ0NpU00q7TOTx9SjpbShtp6QrddH8DFzrdu9icAcxatLAckWOQYvi6OLsd2bJaVSBG0ved8zczM8HvY1ETp5XXU/k0Z0FympAC+MA2agekEMIE3Af8wIN9bxZCbBVCbK2r884VNFrz2hnT0um12UPazTZaTpucTlu3jS0hmA6vhovm9Cnp9PbbQzJbTw3z7+mT07DZJZ/o+BrVFUMoSLsrmhkTF0FaqHa4HyWaCkD3ASMA3cCJJwpSJZDt8n+WY5uTWGA68JEQohRYDKxxF6gtpXxUSjlfSjk/NfXE3kVDM3qVcf64RBKjrLy3t3rUx1IbtRTi5RNTCDObQs5No1ZQ3fzcRGIjLKwPMTebWi6oOTnKNRpqcUhGq5EhcLrYBmV97aps0dzTuSsjzbfmAtAHMdL3VcsB6DByELqWA9AhsFm1nihIW4CJQog8IUQYcAWwxvmilLJFSpkipcyVUuYCG4ELpJRb1RzoaK9Vi9nEyinprN9Xq9vO6dHhFhbnJ7OuqGbUqedqo8ZX0Wo2saIglQ/21dJvDzH5VLiZmk2CUyel8cG+2pBzk2p0rfAvnQ2KcmS2DGxq6+6jpK6DmRpzr4Fn39Gu3n7tBaA78OQ7qtUAdPDsO9rXr80AdPCsFI7dLrGruDaMqCBJKW3A7cC7QBHwgpRyrxDiHiHEBaqNJACcMW0Mbd02Nh0OvYrFaj2tnDVtDEcaOimsalXleGqgpq529vSxNHT0htQcqqmqnTV9DC1dfXwRSlW1Q0sXDR06G05wr+2rbgNgWmZcMEbkd/ZVKwHo0zL0Kd/B2nZsdqmNBrU+cKiunV6bXb/zV9fO9F+9y4cqJWR5FIMkpVwrpSyQUuZLKe91bLtbSrnGzb6nqG09UmuBXT4xhUirmff26tNFA3DmtHTMJsFbu6rUO2gIcerkVCKtZt3Kd3JBKjHhFtbqVD5d0dV4goJU5HgwmTJWuwvQcPejoipFAZyqYfmGU/gLB+YvNkCDUZ/hlhO9X5+FR1vp7O1XLT5OM1Wi1DCwRFjNrChI5Z291aHnolHpOMkx4ZyUn8xbu6tCys2mlosmKszCaZPTeDfE5lCt+Yuwmlk5JY13C6tDys1muNjc4MaCVHi0lYQoK2M0GKDtyRwXVrUQG24hK1F7AcyeXMKFR1uJtJoZp8EAZk/lC7OYGJ+iQfk8ELCoqpUws4n81BhVzqkJBUnNZfC8WWOpa+thU0g1r1V3oT93xliONHSy92houNnU1tPOnTmW+vbQcbOprYieM2MszZ2h42YLHTU0xOh0b0GaOjZOswG+I1FU1cYUXcvXyuSxsZg1GsA8EkVVbUxKj9VkBW1PKKxqZUJaDFaV5NPnpzQMp09OJzrMzOs7jgZ7KH7jjGljFDfb7tBx04ym19xgTp2UFnpuNhXvp6HoZlNz/nSBlA4LUuLAJlu/nX3VbZp2X8DQCrHdLimqatW0+wmGztKTUlJY1ar9+RtiAo/Jp8/5g2MKvFpoRkFS6wYdGWbmzGljWLunih5bvyrHVAM1H8iSosMUN9uu0HCzqZ0mHhlm5rQpabyzpxpbCLih1P6EXd1soZBxGQrXUMjR1wm27uMsSKUNHfTY7JpdYEe6x5Y1dtLZ269d+Ua4x1Y2d9HWbdOsfCMJWNvWQ2NHr2blG2mJrGvrob69R1UFUBMKkto36AtmZ9DWbQuZvmX+WH8umJVBWWMn28ua1D94CLBqVgYNHb18ciA05lBtVs3OpLmzT7VsDAOVcVMkslAPAczD4AzwnarTDChdBKAPgzMAXa/yFflBPk0oSKCuhWXphBSSo8NYszN03Gxqu/TPmTGWqDAzL22rUPfAPqK2fKdMSiMpOix05FP5eMsnppAaGx468hketuNx04et8GgrVrNgQpo6AaKBxjnHQz2QFla1YhJQkK5NF81Ap4ohHkgLj7YiBEweo3H5hrBpFzpiUidrVEE6dn26f90fGXqaUJDUNrBYzSbOnTmWdYU1tHQGv++VPxwY0eEWzp4+ljd3VtHVG1xXoj8sZGEWE6tmZ7CusJbmzl71T+ANfpDPYjZx0ZxMPtxXS317j/on8ALDweYGNwpSUVUr+akxhFk0cVv1mqKqVsanxhBhHb5Xm1YpqmplXFIU0eGWkXfWIEVVrWQmRBIfaQ32UPxCUVUrY+IiSIwOU+2Y+vwme8Bl87Ppsdl5fWflyDtrlEvmZdHWY+O9wtBrr6IGl8zLorffzhshZAlUk4vnZWGzS10nFGgWp4stMmlgU2FVqy7cT0MpxIVHW3XhnhlSPp3M31AC6kW+oeZPCdBW1/qnGQVJbQv/9Mx4pmfG8ezm8pAIQvVHltCivCSyEiNDxk2jNtMy4pkyNi4k5PNH2nNBeiwzs+J5ORTkC/YAQo3uZuV3ZAIA9e091LX16EKBcEdzZy9HW7o1G+ALw39H27r7KGvsZMoYLcs39Gtdvf2U1ndoe/6GuQv12Po5VNeuunyaUJD8pb9csSCHoqpWdlW0+OcEHuIvBc1kElw8N4vPDtZT3tjpl3N4gj/Vz0vmZbGzomXAvx4M/NnM9ZJ5WRRWtbKrotlv5xiJEHh+CD2cClKE0pJCDxWKh8MZwKz1FPGh2F/tlE+f87e/pg27hKk6nb8DNUqLmK+kggT4JUr0gtkZRFrNPLelTPVjhwqXL8hGAE9vCq6M/iosd/HcTMItJp7ceMQvx/cUf1lYvj4nk6gwM09uCLJ8RpT28XS3gCUSLOEA7HMoEFoN8HXFnUK8r1o/CqC7B9Iih4I0WQcKhLvnmX0OBX6yhi1kTtzN375q/yjw2lGQ/EBchJVzZ45lzY6jtHUHN1jbX+tPRkIkX5uazvNbyujuC06wtj8tEAlRYVwwK4PXvqykpSs4c+hP+eIirHx9TiZrdh6lqSM4weiGAckN3S0D1iOA4po2UmLCSI4JD+KgRsdwSnBxTTvxkVbSYrUs39CvHahpIybcQmaC9lqoOBnOBVVc006E1UROUlQAR6QyI8xfmNlErsotYjShIPnzBn3N4nF09Pbz4tbgxXn4ewG6dkkuTZ19oVV5WkWuXZJLV18/r2wPfqyOP7h2yTh6bHZe3FYe7KGEBEKIXwkhKoUQOxw/5zi25wohuly2P+K3QQxWkGrbmZimfevDUByoaaMgPUa3lsTimjYmpOlXvgO1inwmnbZQOVDbzvjUaNVbqGhCQQL/uTBmZScwf1wi//nicFCbn/rzsj0pP5n81Gj+t6HUj2cZHn/KNyMrntnZCTy58UjQAu79eV+dPCaOhblJPLWxDHuQrtEQvK3eL6Wc7fhZ67L9kMv2W/x2dhcFSUrJwZo2JqZrs/6Rk4E5HnSJSSk5UNvOBI0rgMfqBJ3Iwdp2Jmq0fpWT4eoEHajRvgI/XB0rp4KrNppQkPy96F2/LI/yxi7WFdX49TxD4e81XQjBtUty2VnREqTK2v5f1FefNI6Sug4+Kg58Ze1A6GTXnjSOssZO1u8LfGXtUMjyDDm6WwYy2I62dNPR289EjRZQHIm6th5auvoo0LgCOBSNHb3Ut/dqtgDmSLR09VHd2q15BX4oOnpsVDR1+WX+NKEg+ZszpqaTmRDJ458dDvZQ/MYl87KIj7Ty8EeHgnJ+f1uuz52Rwdj4CB4Jlnx+trGcNW0MWYmRPPzRweAoLKFnQrpdCLFLCPFvIUSiy/Y8IcSXQoiPhRDL/Xb2ruYBC1JxjRIgWqBxC4STwVmZxTXtgHYraA9m8NfHOX96USAGz9/BWuf1qY/5G8zBWuf1+RW1IIF/F1iL2cR1S3PZfLiRHeXN/jvRcPhZg4gOt7D6pFzeL6zhgOOGoCfCLCZuXD6eTYcb2XZEf/3nLGYT3zp5PNvLmtl8uDHYw/E7Qoh1Qog9bn5WAQ8D+cBsoAr4i+NtVUCOlHIO8H3gGSGE27QdIcTNQoitQoitdXU+WB1dXGzO75PWFYihbkG6USCGEFA38zfEdr0ouEPFhx27Pg0Lkt+4YmEOCVFW/r7+QMDPHSh7wDdPyiXSauaRj0sCdEaFQBk8rliQTUKUlUc+DqwVyZ91kFy5dH42ydFhPBxw+QKPlHKllHK6m5/XpZQ1Usp+KaUdeAxY6HhPj5SywfH3NuAQUDDE8R+VUs6XUs5PTU31dnDHKUjFNe2kxISr2uIglDhQ20ZClJVUDWfoDUdxTTsx4RbGxkcEeyh+obimjQiriaxE7WboDcfB2nbCzCbG+SFDTzMKkr8t/DHhFm5clsf6fbXsqQx84chAeDCSosO4YmE2r++opKIpsIUjA5EcEh1uYfUSxUpWHGArWSDki7CauW5pLh/tr2Pv0cBeo6HkYRNCjHX590Jgj2N7qhDC7Ph7PDARUP9poLcDZP8xC5IOAnxdGfxAowT46ifDa/ADjTPDSzfyDZq/g7Xtuspgc+ci9UcGG2hEQQqUBeLak3KJi7DwQBCsSIHipuXjMQnB39cfDNg5A2mB+OZJucSEW7j//eKAnTOQIUHXLMklNiKw8oVgIaQ/CiF2CyF2AacCdzq2nwzsEkLsAF4CbpFSqu+P7HYopxEJAxlseghgdrd8SikprmnTRQD6UOrBgZp2fczfMC5SPcQfDedC9Nf1qQkFKVDERVi5bmke7xXWBNSKFMig24yESK5anMNL2ys4VNcesPMGisToMG5cnsfbe6qD2p7DX8RHWrllRT7rimqDlJEYfKSU10gpZ0gpZ0opL5BSVjm2vyylnOZI8Z8rpXzDLwNwaTNS2dyl6wy22rYeWrttuglAH0xDew8NHfrOYKtp7dHt9dnRY6Oyuctv16dmFKRAmT+vX5ZHQpSVP7yzLyDncxJI6+5tp04g3GIKqBXC31lertywLI/EKCt/fi+AVpYA8s2TckmJCePP7+4P2Dn14n5QhQELUjwHdBIA64rr41qxTgKYj8NFQGcAs54UCNf5OxaArh8F19VFeqDWv/PnkYIkhDhLCLFfCHFQCHGXm9e/L4QodKTdrhdCjFNzkIEKggXlCf2O0yby6YF6Pg5CTZ1AkBITzg3L8nhzV1VALGWBTkuPjbBy6ykT+KS4jg2HGvx+vkB7oKLDLdx26gS+ONTApwf8f40G8vunCVwUpIEMGh1YWNwpwXpSINzp+Adq9aNAuHsI1UsGG7ifv2I/K4AjKkiOoMcHgbOBqcCVQoipg3b7EpgvpZyJ4vv/o9oDDeTz6zWLx5GTFMXv1hYFrLp2oJ/Pb1w+nqToMO55ozAgCkygDRDXLBlHZkIkv35jL7Z+u9/PF2gLyzcW5ZCdFMk9bxTSFwj5/H4GDeFqQarVdwbbQUcGW0qMPuU7UNNObLiFMXH6zGA7UNtGpNWs6R5zw+HMYPNXjzlPLEgLgYNSyhIpZS/wHLDKdQcp5YdSSmda1EYgS91hBpYwi4kfnzWZfdVtPL9Fn/2v4iOt/PDMSWwubWTNzqN+PVcw7A8RVjM/O3cK+6rbeGZzmV/PFYy6jeEWM784dyoHatv534Yjfj2XUUh7EE4FKTJRFy0qnBxrVXFswp3y6cHF6rSwuF7OB2vbydeJfAwxf/lp0brIYHPXSuWgn3qwOfHkqJmAq5ZQ4dg2FDcAb49mUIMJxg36nBljWDw+iT+8s4/69h6/nitYC9Bl87OZkRnPvW8V0d5jC84g/MjZ08dwUn4yf3mvmMaO3mAPR3W+NjWdkwtS+ev7xdS1+fcaNXChqxkAGR5LSZ1yg9YrJXUdjE/RhwLojpJ6Y/60jL+/f6qqXUKIq4H5wJ+GeN3n6rWBVvCFEPzm6zPo7LVx71tFATlfoDGbBL9eNY3ath6/lzYIxvOLEIJfXTCN9h4bf/Rz0H2w5Pvl+VPptvXz+7f9LJ/2H0DVo7sFwmJo6LLT2m1jfKq+FiDn81pLZx8NHb26UyCcD6TtPTZqWnvI19v8OeTr7uvnaEuX/ubP8bvXZqe8qcuvCqDFg30qgWyX/7Mc245DCLES+BmwQkrp9nFWSvko8CjA/PnzPbabBMvCMiEthm+vyOeBDw5yybwslk5I8ct5ghkEOzcnkSsWZPOvT0s4Z8ZYZmcnqH6OYLpoCtJjuWFZHo9+UsJ5MzNYNtEfcxg8AfNTY7hp+Xge+ugQ580ay6mT0lQ/h+FiG4SjinZJXQeAbhagwTrwoXolwFcvCuBgJf+wc/5S9Dl/h+s7kBImpERy+PBhuru7gzIutcgx2XjsgrG0V5dSVGeir9/OI+eNISm6l6KikY0YERERZGVlYbVaPT6nJwrSFmCiECIPRTG6AviG6w5CiDnAP4GzpJSBbzfuR249dQJrdh7lxy/v4u3vLic2wvMPVyv89NwpfFxcxw9e2MFb31lOhNWs/kmCaIL4/tcKWFdUw49f3sU73/PPHAbTwvLdlRN5v7CGn7y8m3fvPJn4SD/IZ4RpH6O7GSISOOxUIHSywA7GqUDk6VS+Esf85elEwR3M4Xpl/nLCu4iNTSI3N1fTsVZNnb1YGzspSI8l3GqmpasPGjqYkBpDVPjwqoyUkoaGBioqKsjLy/P4nCO62KSUNuB24F2gCHhBSrlXCHGPEOICx25/AmKAF4UQO4QQazwegYcE6wYdYTXzl8tmcbS5i1+/Uei38wTzso2LsPL7i2dyqK6D+9fpr3ZQhNXMny6ZRVVLF79dG9j6VoEg3GLmz5fOoq69h9+86b9r1MBBdwtExFFS10GY2URWon8yaIKF02JYUt+O2ST8liEULJwW+5K6DoSA3GR9KkgljkLAFtlPcnKyppUjd/TY+gEIs44cKSSEIDk52WsrmkcxSFLKtVLKAillvpTyXse2u6WUaxx/r5RSpjsq2M6WUl4w/BG9I9h1WOaNS+L2Uyfw0rYK1u6uUv34oeDCWFGQypULs3nskxK+OFiv6rFDQDzmjUvkpuXjeXZzGe/sqVb12KEwf7OyE7hlxXhe3FbBW7vUvUaD/f0LOXraIDyOQ3UdjEuOwqyDDCHgBDNoSV0HOUlRhFk0U094WAbPUkl9B5kJkf6xmAeBwQpQSV0HY+IiEEIfhV4HS9DbZ8diMmExeXZ9+vIZaOfKD/L83nH6RGZlxfOTV3ZT3qh+o9dQuH5/fu5UxqfG8J3ndlDbqq6/OgTE4/tnFDAzK54fvrSTsgZ15zAU5u97KwuYm5PAj1/eNfD0qBahIF/I0NMG4TFfkQwoPcvXrpv4Knccqu/Q3fW5dcPnrFi+FIAem51wiwmbzUZ6ejpHj6pfrkY7ClKQsZpNPHDlHKSUfOvJbXT19gd7SKoTHW7h4avm0tFj445nv1StwGKgK2kPRbjFzIPfmIsAbn1mG9196sxhaEinXKP/+MZcrGbBrU9vV0++UBEwVOhtx26NoayhkzwdplBLJHa75HBDhy7jj6RU7kmH6/WpAA7IV9euqfnr7x/5fjV30RIqKis5cuTIgIK0bt06pk2bRkZGhupj0oSCFCo36HHJ0fztyjkUVbdy1yu7VFv4Q0U+UFoK3HvhdDYdbuTeteqVNwgVC0R2UhR/uWw2eypb+emru1Wbw1AJYs5IiOS+y2ezr7qNH76k3jVq4EJPO20yAptd6uoJ3fUKrmzuotdm15WFxfUeVNPaQ2dvv27nr6GjN6RKUJSWljJ58mSuuuoqpkyZwiWXXEJnZye5ubn8+Mc/Zu7cubz44ou89957LFmyhLlz53LppZfS3n68JdxkMnHxJZfwzDPPYrPbCbeaeO6557jyyiv9Mm5PsthCgtBYfuDUSWn83xmT+NO7+5mYFsPtp01U5bihssACXDQ3i71HW3n8s8PkJkez+qTcYA9JVb42NZ07VxZw/7pi8pKjueN0deYwVDh1Uho/OmsSf3xnP3nJUXz/jEnBHpJ+sPdDXwcNfUrrjXwdLbADSCU+B/RTwsAVybEAZj0WUZTI40tQ2I+FS/z6jb0UHm1V9XxTM+L45fnTRtxv//79PP744yxdupTrr7+ehx56CIDk5GS2b99OfX09F110EevWrSM6Opo//OEP3Hfffdx9993HHeeyy6/g1lu+xbnX3AL9NtauXct9992nqkxONKEgSUJHQQK49ZR8Dta28+f3ikmLjeCyBdkjv2kYQjEI9qfnTKGssZNfv7GXrMRITp+SHuwhqcp3Tp/AkYYO/vJ+MTnJUayaPVxx+OEJRSvNt1fkc6S+kwc+OEh2UhSXzvf9Gg096YJIr7Kw1vYopRT0tMC6WlgGFAgdKUiuD6GHdKgAupu//JQYOmr937DbE7Kzs1m6VIkfuvrqq3nggQcAuPzyywHYuHEjhYWFA/v09vayZMmSE44zb9582tvbKT10gH115SxatIikpCS/jFkTChKEjosGlGj4P1w8k/r2Hn7y6m4So8P42tRRKhAhJB8oVbb/dsVsrnh0I7c+vZ1/f3PBqAplhph4CCH43cUzONrSxQ9e2Emk1cwZ08aM4ngqDk4FhBD85sLpVDZ3cdcru4kOt3DOjLGjOp4B0KMsPJVdVhKirLptUnu4voOYcAupMeHBHopfOFzXQYTVpNsmtYfrlRIUmYmRFLtUJvTE0uMvBt9DnP9HRytKqpSSr33tazz77LMjHmvVRZfyzppXaKg87Df3GmgkBikUCbOYeOTqeUzPiOPWp7fx3l51U8dDgagwC09ct5C8lGhu+O8WNpWExpOIWoRbzDx27XymZ8Zz2zPb+XCfbzVOQ9XCYjWb+Oc185iTncB3nv2S9wtrfDpOCBrIgkdPGwDlHSZdBviC0wWlZEDpUTGWUlJS305eSowumrgORkpCsgRFWVkZGzZsAOCZZ55h2bJlx72+ePFiPv/8cw4ePAhAR0cHxcXu6/Kde+ElrH31BT784ANWrVrltzFrQ0EK0Rt0dLiF/92wiGkZ8dz69Haf68+E8gKUFB3GUzcuIisxiuue2MInxd710IPQli82wsp/r1/I5DFxfOvJbbyzR/06V8EkOtzCf65bwPTMeG59ehtv7FQ/FfYrhcPFdrhNhEwArFq4uqBK6tr1pwAe54LSXwr8cS62ECxBMWnSJB588EGmTJlCU1MT3/72t497PTU1lSeeeIIrr7ySmTNnsmTJEvbtUwr73n333ax9642BfbPHFxAVFc1pp502YIHyB9pQkAitIGZX4iOtPHnDQubkJHDHs9t5auMRn44TmtIppMSE88xNixiXHM31T2zh1S8rvD5GKD+JOudwRlY83356O0/6MIehK90xJXBOdiLfee5L/vP5Ya+PEcryBRSHBamy06KpFGpv6Ort52hLty5LGIDS5LSiqVN/CqADm12GZAkKi8XCU089RVFRES+//DJRUVGUlpaSknIsdOO0005jy5Yt7Nq1i127dnHBBUrN6XvuuYdzzj1/YL9em511n23iueee8+uYNaEghWIQsyuxEVaeuG4hKwpS+flre7jnjUL67aE9Zm9Ji43g+W8tZkFuEnc+v5N/fHDA4+DkUJ8/gISoMJ66YRGnT07jF6/t4fdv7/N4DkPZQuYkPtLK/25YyBlT0/n1G4X85s1CL+pcaUDAQOFQkDqI1G2LijJHIdzcFH21GHFS0dSFXSplW/TI0eYubHZJbrI+56+v345dSsIDUOFdEwqSFogOt/Cv1Qu4fmke//78MNc/sYXGjl6P3quV5ScuwsoT1y9g1ewM/vxeMd96chut3X0evVcLFojIMDOPXD2Pqxbl8MjHh/jmfzZ7PIchF6XthgirmYeumsfqJeP412eHuebxzTS093j0Xg2IFxgcLrZ2IhinswXIOcdHGhxNTnXWg815CTvl0938OSQcmL8Qki83N5c9e/aocqxem/JgF4gWOJpRkLRwgzabBHefP5XfXjiDDYcaOPtvn7DRw8BmLcgHSmDzXy+fzS/Om8r6fbWs+sfn7KpoDvawVMNiNnHvhTP4w8Uz2HS4kfP//hmbDzcGe1iqYTYJfr1qOn++dBbby5o47++fseGQvoLv/Yoji61dRobUAqQmRxxtePRqYRmQT2cKoBO9z1+PoSAdjxZcGK58Y1EOr952EtFhFr7x2EZ+t7Zo+NYkGpNPCMENy/J45sZFdPX2c+FDX3Df+8UDmv1gtDZ/AJcvyOGlW5ZgNgkuf3QD975VOGTrDg2KxyXzsnj52ycRbjFx5WMb+fUbe4eWT4sC+osepcheWFQ8cRHWIA9GXQYsLI2dxEZYSIzSmXyOp9AjjZ1EWs2kxuqrhMGABbCxkzCLibE6LWHQa7MjEISZDQVpAK1YWJxMy4hnzR3LuHxBNv/8pIQz//oJnx+sH3L/UA1CH45F45N593sns2pWBg+sP8CqBz9na6l7a4vW5g9gZlYCb393Od9YmMNjnx7mnAc+5bMD7udQg+IxPTOetd9dzuol4/jP56Wc/bdP+XiILEUtzp9f6G3HhoWxyfHBHonfONKgpIiHcmLFaPgqyJedGKnLEgYAvf12rBYRkPnTjIKkRWLCLfzuopk8e9NizCbBVf/axLee3MohlTutB5P4KCv3XT6bf14zj+bOXi55ZAPfe+5LqluOlbfXsgUiOtzCvRfO4H/XL8TWL7n6cWUOyxxmbAjNStqeEhVm4derpvP0jYuQUrL635u58b9bKXVUGgZtWsj8Rk877UTqLn7FlbLGTsYl6dM9A4p8eouvcqWssVO37jVQLEiBsB6BRhQkrd+gl+Qn8/Z3l/ODrxXw+cEGzrj/E3726m4qm7sAbWR5jcSZ08aw/gcruOO0CazdU83Jf/qQX63ZS02roihp0ULmyskFqbx358n88MxJfFJcz2l/+YifvLKbiiZFUdL6w+jSCSm8e+fJ3HX2ZDYcqmflfR/z45d2Ue7IaNL6/KlFf3crbTJCl/Erzmu4u8+uy/gq5xXc3WfXtYLb3WfXpQL41huvc6h43wkZbKWlpWRlZWG3Hx/iMXv2bDZt2jSqc2qn1YjGb9ARVjN3nD6RKxfl8Pf1B3h6UxnPbynnglkZ7K9uY3qm9k32UWEWfnDGJC6bn83fPzjAkxuP8MzmMnptdqaMjQv28EZNhNXMbadO4OK5WTz44UGe31LOS9vK6euXumhaGm4xc8uKfC6ak8lDHx3imc1lvLy9Aptdkp0UGezhhQTd7S20ywhydPyEDvoNYHai+/nToQL41htvMG/56eQXTCbMYh7YnpubS05ODp9++ikrVqwAYN++fbS1tbFo0aJRnVMbFiQNuzAGkxITzq9XTefjH53KtUtyeWdvNa3dNrqGCJDVItlJUfzxkll8+INT+PrsDACyEvWzwI6Jj+D/fX06H/3wFK5YkAOgq5o4aXER/OqCaXzyw1O5apEiX6gVnQsWPZ2tunexQWiliPsDvSuAoXZ9lpaWMnnyZK666iqmTJnCJZdcQmdnJ+vXr2fOnDnMmDGD66+/np4epezIXXfdxdSpU5k5cyb/93//xxdffMHba9/kvnvv5rIzl1NZdnyx2yuvvPK4opHPPfccV1xxxajHrR0LkrYNSCeQmRDJ3edP5TunT+CFreVMHqN9C8tgcpIVRemus6dgMetsAoGMhEj+39en839nTgqpnkdqMSY+gl+vms4PzpyESW9fQB+xd7XQISOZqsMF1tVKr8cYFtdLONQUCDVwDVrOGSqG7O27oHq3uiceMwPO/v2Iu+3fv5/HH3+cpUuXcv3113Pffffxz3/+k/Xr11NQUMC1117Lww8/zDXXXMOrr77Kvn37EELQ3NxMQkICZ59zHvOWn87Xzl3FxPTY44592WWXMXv2bP7+979jsVh4/vnnefHFF0ctmiYsSHomISqMm0/O5+SC1GAPxW8kRYfpLiXalfhIKzHhmnnW8Jq4CH3L5w2yt50uEam7FHFXwsz67XIPSi2wjAT9WLQHIwQh6RLPzs5m6dKlAFx99dWsX7+evLw8CgoKAFi9ejWffPIJ8fHxREREcMMNN/DKK68QFXWiMjs4SDs9PZ3p06ezfv16duzYgcViYfr06aMesybuevpxsBkYGGgZc18HhMfqNkUcICspUpcWUSeZCZFYA5QFFQzGxkUQ7hKjcxweWHr8xeDvTEJCAg0NJxaptVgsbN68mfXr1/PSSy/xj3/8gw8++ODY62aT2+vT6WZLT0/nyiuvVGXM+r1KDAwMDFQmvL8DS6T+3OHAQJqXXuNznOuzHt1rcCxLL1Tjx8rKytiwYQMAzzzzDPPnz6e0tJSDBw8C8OSTT7JixQra29tpaWnhnHPO4f7772fnzp0AxMTG0NHeTvgQyu1FF13E2rVref7551WJPwIPFSQhxFlCiP1CiINCiLvcvB4uhHje8fomIUSuKqMzMDAwcIMQ4g4hxD4hxF4hxB9dtv/EcR/aL4Q4U81z2vvtRMpuIqJ1qiA50GP8kSt6TIF3JVRrWE2aNIkHH3yQKVOm0NTUxJ133sl//vMfLr30UmbMmIHJZOKWW26hra2N8847j5kzZ7Js2TLuu+8+AC6+9DL++8+/s2rlUg4dOsQjjzzCI488MnD8hIQElixZQnp6OuPHj1dlzCO62IQQZuBB4GtABbBFCLFGSlnostsNQJOUcoIQ4grgD8DlqowQbRcaNDAwUBchxKnAKmCWlLJHCJHm2D4VuAKYBmQA64QQBVJKVVJE6xqbSBeSqLhENQ4XcjgtEPq1sCgS6inj1JUBC1lKaM6fxWLhqaeeOm7b6aefzpdffnnctrFjx7J58+YT3r94yVJe/WAj6XERpMdFkJ+ff8I+r732mqpj9sSCtBA4KKUskVL2As+h3JxcWQX81/H3S8DpQiUnvZSSxo5eXfv8DQwMvOLbwO+llD0AUspax/ZVwHNSyh4p5WHgIMr9SxWqjh4BIE6nCpITvSpITkLVBaUWoWpBUovwADSpdeLJmTKBcpf/Kxzb3O4jpbQBLUCyGgPst0vKGjsJ02GauIGBgU8UAMsd7vyPhRALHNs9uVf5jKnodQASktPUOmRIEW5VAnv1WvPKubDqoairO5yB2eNDUL7c3Fz27NkzqmM4bSRhAVSQAprFJoS4GbgZICcnx6P3mITg/62axpL8FH8OzcDAIIQQQqwDxrh56Wco960kYDGwAHhBCOFV0IEv96L42RfwiUhgyezzvTmVZjhjajpP3rCQvJTQW2DV4LQpafzv+oVMSIsdeWcNsqIglSeuW6CLrgXuiA23kJsSTaR1iAw9P+CJglQJZLv8n+XY5m6fCiGEBYgHTsjfk1I+CjwKMH/+fI8ii0wmwTVLcj3Z1cDAQCdIKVcO9ZoQ4tvAK1Ipsb9ZCGEHUvDsXuU8vtf3onGT5zFu8jzPBNAgEVYzyyfqtx5buMWs63pzYRYTp0xyb92UUmo+TEUIMap6er505PDEVrUFmCiEyBNChKEEQa4ZtM8aYLXj70uAD6Se+oMYGBiEEq8BpwIIIQqAMKAe5T50hSOrNg+YCJwY7Wlg8BUiIiKChoYGXbXs8hYpJQ0NDUREeFcAdUQLkpTSJoS4HXgXMAP/llLuFULcA2yVUq4BHgeeFEIcBBpRlCgDAwMDf/Bv4N9CiD1AL7Da8UC2VwjxAlAI2IDb1MpgMzDQKllZWVRUVFBXVxfsoQSViIgIsrKyvHqPRzFIUsq1wNpB2+52+bsbuNSrMxsYGBj4gCOb9uohXrsXuDewIzIwCF2sVit5eXnBHoYmMSppGxgYGBgYGBgMwlCQDAwMDAwMDAwGYShIBgYGBgYGBgaDEMGKbBdC1AFHvHhLCkqmitbQ6rjBGHsw0Oq4wfuxj5NSBj3v2st70VdpfkIFrY4bjLEHC2/GPuR9KGgKkrcIIbZKKecHexzeotVxgzH2YKDVcYO2x+4pWpZRq2PX6rjBGHuwUGvshovNwMDAwMDAwGAQhoJkYGBgYGBgYDAILSlIjwZ7AD6i1XGDMfZgoNVxg7bH7ilallGrY9fquMEYe7BQZeyaiUEyMDAwMDAwMAgUWrIgGRgYGBgYGBgEhJBXkIQQZwkh9gshDgoh7gr2eIZDCJEthPhQCFEohNgrhPiuY3uSEOJ9IcQBx+/EYI/VHUIIsxDiSyHEm47/84QQmxyf/fOOZsUhhxAiQQjxkhBinxCiSAixREOf+Z2Oa2WPEOJZIUREqH7uQoh/CyFqHT3QnNvcfs5C4QGHDLuEEHODN3J10Mq9SOv3ITDuRYHGuA+5J6QVJCGEGXgQOBuYClwphJga3FENiw34gZRyKrAYuM0x3ruA9VLKicB6x/+hyHeBIpf//wDcL6WcADQBNwRlVCPzN+AdKeVkYBaKDCH/mQshMoHvAPOllNNRmkFfQeh+7k8AZw3aNtTnfDYw0fFzM/BwgMboFzR2L9L6fQiMe1HAMO5DwyClDNkfYAnwrsv/PwF+EuxxeTH+14GvAfuBsY5tY4H9wR6bm7FmOS6s04A3AYFSaMvibi5C5QeIBw7jiKdz2a6FzzwTKAeSUBpHvwmcGcqfO5AL7Bnpcwb+CVzpbj8t/mj5XqSl+5BjbMa9KLDjNu5DQ/yEtAWJYxPnpMKxLeQRQuQCc4BNQLqUssrxUjWQHqxxDcNfgR8Bdsf/yUCzlNLm+D9UP/s8oA74j8Mk/y8hRDQa+MyllJXAn4EyoApoAbahjc/dyVCfs2a/u0OgSXk0eB8C414UUIz70NCEuoKkSYQQMcDLwPeklK2ur0lFjQ2p1EEhxHlArZRyW7DH4gMWYC7wsJRyDtDBIBN2KH7mAA4/+SqUG2sGEM2JpmPNEKqf81cVrd2HwLgXBQPjPjQ0oa4gVQLZLv9nObaFLEIIK8pN6Wkp5SuOzTVCiLGO18cCtcEa3xAsBS4QQpQCz6GYtv8GJAghLI59QvWzrwAqpJSbHP+/hHKTCvXPHGAlcFhKWSel7ANeQZkLLXzuTob6nDX33R0BTcmj0fsQGPeiYGDch4Yg1BWkLcBERzR9GErg2Jogj2lIhBACeBwoklLe5/LSGmC14+/VKDEBIYOU8idSyiwpZS7KZ/yBlPIq4EPgEsduITduACllNVAuhJjk2HQ6UEiIf+YOyoDFQogox7XjHHvIf+4uDPU5rwGudWSRLAZaXEzgWkQz9yKt3ofAuBcFCeM+NBTBDrbyIBjrHKAYOAT8LNjjGWGsy1BMe7uAHY6fc1B86OuBA8A6ICnYYx1GhlOANx1/jwc2AweBF4HwYI9viDHPBrY6PvfXgEStfObAr4F9wB7gSSA8VD934FmUGIU+lKflG4b6nFECax90fG93o2TIBF2GUcqviXuRHu5DDjmMe1Hgxm3ch9z8GJW0DQwMDAwMDAwGEeouNgMDAwMDAwODgGMoSAYGBgYGBgYGgzAUJAMDAwMDAwODQRgKkoGBgYGBgYHBIAwFycDAwMDAwMBgEIaCZGBgYGBgYGAwCENBMjgBIUSyEGKH46daCFHp+LtdCPGQn875PSHEtSoc5zkhxEQ1xmRgYBBcjHuRQTAx6iAZDIsQ4ldAu5Tyz348hwXYDsyVx5oj+nqsFcDVUsqbVBmcgYFBSGDciwwCjWFBMvAYIcQpQog3HX//SgjxXyHEp0KII0KIi4QQfxRC7BZCvOPoBYUQYp4Q4mMhxDYhxLvOfjmDOA3Y7rwhCSE+EkLcL4TYKoQoEkIsEEK8IoQ4IIT4jWOfaCHEW0KInUKIPUKIyx3H+hRY6dJDyMDAQGcY9yKDQGAoSAajIR/lhnIB8BTwoZRyBtAFnOu4Mf0duERKOQ/4N3Cvm+MsBQZ37+6VUs4HHkHpq3MbMB34phAiGaXb9FEp5Swp5XTgHQAppR2lNP4sVSU1MDAIZYx7kYHqGJqtwWh4W0rZJ4TYDZhx3BhQet7kApNQbiTvKz0QMaP00BnMWKBo0DZnI9DdwF7paDAohChB6c68G/iLEOIPKP2aPnV5by2QwYk3OgMDA31i3IsMVMdQkAxGQw8oT0pCiD55LKDNjnJtCZQbypIRjtMFRLg7tuNYPS7b7YBFSlkshJiL0oTzN0KI9VLKexz7RDiOaWBg8NXAuBcZqI7hYjPwJ/uBVCHEEgAhhFUIMc3NfkXABG8OLITIADqllE8BfwLmurxcgNKV2sDAwACMe5GBDxgWJAO/IaXsFUJcAjwghIhHud7+CuwdtOvbwJNeHn4G8CchhB3oA74NIIRIB7qklNWjGbuBgYF+MO5FBr5gpPkbhARCiFeBH0kpD4zyOHcCrVLKx9UZmYGBwVcJ415k4MRwsRmECnehBEiOlmbgvyocx8DA4KuJcS8yAAwLkoGBgYGBgYHBCRgWJAMDAwMDAwODQRgKkoGBgYGBgYHBIAwFycDAwMDAwMBgEIaCZGBgYGBgYGAwCENBMjAwMDAwMDAYxP8Hl8whRQaAgnMAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(ExpAll2All)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d37e8b1d",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Similarly, the AMPA synapse model can be defined as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "01ce8789",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class AMPAAll2All(BaseAMPASyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(AMPAAll2All, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # synapse gating variable\n",
+    "    # -------\n",
+    "    # The synapse variable has the shape of the post-synaptic group\n",
+    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
+    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
+    "    TT = TT.reshape((-1, 1))  # NOTE: here is the difference\n",
+    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
+    "    g_post = self.g.sum(axis=0) # NOTE: here is also different\n",
+    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "51a07101",
+   "metadata": {
+    "lines_to_next_cell": 1,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "87afdd52cced43e68e45459881315171"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(AMPAAll2All)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eb7c494",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Actually, the synaptic computation with these special connections can be very efficient! A concrete example please see a [decision making spiking model](https://brainpy-examples.readthedocs.io/en/latest/decision_making/Wang_2002_decision_making_spiking.html) in BrainPy-Examples. This implementation achievew at least four times acceleration comparing to the implementation in other frameworks. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d819b14f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Computation with Sparse Connections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d0e7131",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "However, in the real neural system, the neurons are connected **sparsely** in essence. \n",
+    "\n",
+    "Imaging you want to connect 10,000 pre-synaptic neurons to 10,000 post-synaptic neurons with a 10% random connection probability. Using matrix, you need $10^8$ floats to save the synaptic state, and at each update step, you need do computation on $10^8$ floats. Actually, the number of synapses you really connect is only $10^7$. See, there is a huge memory waste and computing resource inefficiency. Moreover, at the given time $\\mathrm{\\_t}$, the number of pre-synaptic neurons in the spiking state is small on average. This means we have made many useless computations when defining synaptic computations with matrix-based connections (zeros dot connection matrix results in zeros).\n",
+    "\n",
+    "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b67256b8",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4806dc08",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### The ``pre2post`` operator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "882dd9de",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "A notable difference of brain dynamics models from the deep learning is that they are sparse and event-driven. In order to support this significant different kind of computations, BrainPy has built many useful [operators](../apis/auto/math/operators.rst). In this section, we talk about a set of operators needed in ``pre2post`` computations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "059255e0",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Note before we have said that exponential synapse model can make computations at the dimension of the post-synaptic group. Therefore, we can directly transform the pre-synaptic data into the data of the post-synaptic shape. [brainpy.math.pre2post_event_sum(events, pre2post, post_num, values)](../apis/auto/math/generated/brainpy.math.operators.pre2post_event_sum.rst) can satisfy your requirements. This operator needs the synaptic structure of ``pre2post`` (a tuple contains the ``post_ids`` and ``idnptr`` of pre-synaptic neurons). \n",
+    "\n",
+    "If ``values`` is a scalar, ``pre2post_event_sum`` is equivalent to:\n",
+    "\n",
+    "```python\n",
+    "post_val = np.zeros(post_num)\n",
+    "\n",
+    "post_ids, idnptr = pre2post\n",
+    "for i in range(pre_num):\n",
+    "  if events[i]:\n",
+    "    for j in range(idnptr[i], idnptr[i+1]):\n",
+    "      post_val[post_ids[i]] += values\n",
+    "```\n",
+    "\n",
+    "If ``values`` is a vector, ``pre2post_event_sum`` is equivalent to:\n",
+    "\n",
+    "```python\n",
+    "post_val = np.zeros(post_num)\n",
+    "\n",
+    "post_ids, idnptr = pre2post\n",
+    "for i in range(pre_num):\n",
+    "  if events[i]:\n",
+    "    for j in range(idnptr[i], idnptr[i+1]):\n",
+    "      post_val[post_ids[i]] += values[j]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff96270d",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "With this operator, exponential synapse model can be defined as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "94d26b81",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class ExpSparse(BaseExpSyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(ExpSparse, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # connections\n",
+    "    self.pre2post = self.conn.require('pre2post')\n",
+    "\n",
+    "    # synapse variable\n",
+    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
+    "    # NOTE: update synapse states according to the pre spikes\n",
+    "    post_sps = bm.pre2post_event_sum(delayed_spike, self.pre2post, self.post.num, 1.)\n",
+    "    self.g += post_sps\n",
+    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "afd6a770",
+   "metadata": {
+    "lines_to_next_cell": 1,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2eca3090d9f5424a90c3143aa46cd05e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(ExpSparse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eed2af26",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "This model will be very efficient when your synapses are connected sparsely. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6300cda5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### The ``pre2syn`` and ``syn2post`` operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f39c2f8",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "However, for AMPA synapse model, the pre-synaptic values can not be directly transformed into the post-synaptic dimensional data. Therefore, we need to first change the pre data into the data of the synapse dimension, then transform the synapse-dimensional data into the post-dimensional data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae7c55b3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Therefore, the core problem of synaptic computation is how to convert values among different shape of arrays. Specifically, in the above AMPA synapse model, we have three kinds of array shapes (see the following figure): arrays with the dimension of pre-synaptic group, arrays of the dimension of post-synaptic group, and arrays with the shape of synaptic connections. Converting the pre-synaptic spiking state into the synaptic state and grouping the synaptic variable as the post-synaptic current value are central problems of synaptic computation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a546a3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "![](../_static/pre2syn2post.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4aeef36",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Here BrainPy provides two operators [brainpy.math.pre2syn(pre_values, pre_ids)](../apis/auto/math/generated/brainpy.math.operators.pre2syn.rst) and [brainpy.math.syn2post(syn_values, post_ids, post_num)](../apis/auto/math/generated/brainpy.math.operators.syn2post.rst) to convert vectors among different dimensions.\n",
+    "\n",
+    "- ``brainpy.math.pre2syn()`` receives two arguments: \"pre_values\" (the variable of the pre-synaptic dimension) and \"pre_ids\" (the connected pre-synaptic neuron index).\n",
+    "- ``brainpy.math.syn2post()`` receives three arguments: \"syn_values\" (the variable with the synaptic size), \"post_ids\" (the connected post-synaptic neuron index) and \"post_num\" (the number of the post-synaptic neurons)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8400124a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Based on these two operators, we can define the AMPA synapse model as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fa62799e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class AMPASparse(BaseAMPASyn):\n",
+    "  def __init__(self, *args, **kwargs):\n",
+    "    super(AMPASparse, self).__init__(*args, **kwargs)\n",
+    "\n",
+    "    # connection matrix\n",
+    "    self.pre_ids, self.post_ids = self.conn.require('pre_ids', 'post_ids')\n",
+    "\n",
+    "    # synapse gating variable\n",
+    "    # -------\n",
+    "    # NOTE: Here the synapse shape is (num_syn,)\n",
+    "    self.g = bm.Variable(bm.zeros(len(self.pre_ids)))\n",
+    "\n",
+    "  def update(self, tdi, x=None):\n",
+    "    _t, _dt = tdi.t, tdi.dt\n",
+    "    delayed_spike = self.pre_spike(self.delay_step)\n",
+    "    self.pre_spike.update(self.pre.spike)\n",
+    "    # get the time of pre spikes arrive at the post synapse\n",
+    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
+    "    # get the arrival time with the synapse dimension\n",
+    "    arrival_times = bm.pre2syn(self.spike_arrival_time, self.pre_ids)\n",
+    "    # get the neurotransmitter concentration at the current time\n",
+    "    TT = ((_t - arrival_times) < self.T_duration) * self.T\n",
+    "    # integrate the synapse state\n",
+    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
+    "    # get the post-synaptic current\n",
+    "    g_post = bm.syn2post(self.g, self.post_ids, self.post.num)\n",
+    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "3ccfcf3b",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c3017b75427f4417a03cf78631510b33"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x216 with 2 Axes>",
+      "image/png": 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tFkO6R/XOQ58cZMuhFv58+fwxBb8OZAtp0FWzr7adP757gDNnZfL1MTYw1WqmV7/DxY9e3EF8lI17Lp03psxXrY4N4O8flLKzqo0Hrl40pq4X4chi074KNwpGvlP3uDIy4qMMGoOkjA2MZ4lwSYiLtBIbaTWccmv061LP7DnSxp/fP8DX5mZx0QKVmxaEmT6Hkx+u2UFijI3fXjLXUKUzAO5bV8Lu6nZ+e8lcMhJGLsWgN3YcbuXvH5Zy8cIczp2bFW5x/Eb3CpKUEB1hMaSVxePKyEiIoqW7f6A1h1GQUg5MBEZbaD0ZlalxkYZU/kC5Lo12U6Jn7E4X//Pil6TERvKri+aMWYHQqtpx/wel7Kvt4J5L541ay0lv7Kpq44GPSrlscS5nzZ4YbnFUpc/h5McvfUlmQhT/e8Hs0d+gIXSvILlcEqsQpMdFGm+Rda9E6fFROF2Sth57mCVSF5eXgmQ0V41LgsUiSIuPMpyLzWO1zUiIotfuorvfeH0C9chjn5Wzr7aDX100h5S4kYvu+YOWXDWl9R08+PFBLlmYw2kzM8d5NKEp56HTJfnZK7tIi4/i/50/voB6obGxATz8cRml9Z38+pK54wqjGChgGsILU/8KkgSLUBYio9UK8r5TB+O5EF1SUf7AeC42KSUWAelxkQa0bA4qSGBmsmmBqpZu/rK2hDNmZXKmwSwQUkp+/spuYiNt/Oxr2s98CpSnvqhgV3Ubd50/y3AZeeWNXfztw1K+Ni+LU6ZPCLc4AWMABUkiBKTFRxruTl36LESGs5BJSXSEhYQom+HO3VEuNoMptp4bOI9y22gw5VZveNptCIEqLgythfb8a2sVG8ubuf2cGQPX3HjQ0vhq2nr403sHOHFaBl9TITZHS2OTUvL//rObKKuFu9QoNRGGseleQZJSKq6MuCjD3cl6LEiZicqk0NBhrIVIuq1/GQlRhhuby2tsTZ3Gih/zWJCMel3qjbXF9Xywr54fnT6NHBUbB2vhim3rsfObt4pZUpDC5WPMWhsKrbgPf/1mMXani19dOPaYMV+0UlPuzV01fFbayE/Ons6ExMCz1obDLBQZAE7pjkGKj6Shw1gFBz0L0UT3xVVvsIXI5XZDZSREUd/RG25xVMVj2ZyQEIXDJWnuNo7y7tH1jHpd6gm708Vv3ypmckYc31hVGG5xVOf+D0tp7bHzywtnB1QtWw9sPdTCGztr+PZJk8lP02YvsrHS53Dyu7f3MWNiAlcvLwi3OGNG9wqSSyql8TMSouh3ugwVyOxRkFLiIom0WgypRFiEYEJitOEWWY91zHPnVN9unPF5xyAJYSpI4eS5jZWUNXbxs3NnEmFVazr3tBoJ783m4eZunlhfwaWLcpmdnaTacbXQjkNKya/f3EtGQhTfPlG9gpBKG5Xw8+TnFVS19PCLr81SrcnuQKsRsw6S/3iCYTMNeDfrWYisHjeUgRZZAJdLUW4nJERR324s65/TpVyXE9zxY0ZSbj3uwgirhbS4KBoMNDY90d5r5y9rD3Dc5DROnaG/ANjRuOedfVgsGK4RLcBbu2rZVtnKj8+cZrhGtC1d/fztg1JOnp7B8VP13SpF9wqSy+W+U/csRAZSIjyt5TyxLEZS/kBRAK0WRYnosTvp7DNOurjLHRs3IcGIirvy12oZVG5NQs8DHx6ktcfOz86dabiiidsqFffTzSdMGlPVZS3T53ByzzuK+2n1YvXiqrTCfR+U0NXn4Gfn6j/jUP8KktuCNODKMNDdrMeC5IllMdLYwB2ng2BCosfKYpyFVkrF3D3BgIHMA9clyviMdN70Qn1HL4+vL+fiBTnMyVHP/QTerUbCx5/e2096fCTfPmmy6scOdzuOF7dUUdncze3nzFDN/eRBCBHWsR1p7eGZDYf4+pI8pmUmqHrscLTAMYCCNOimAagz0N2s505dCGMuRJ5iigNWFkOdO4nVIoiOsJIQbaO+3TjKrUdBsngsSAZT3PXAQx+X4XBJfnDa1HCLojqbK5pZX9rEd06abDj3U5/DyQMflrK4IIWTpmWEWxzVeeCjUgC+d+qUMEuiDrpXkKQ7WyguykZ8lM1Qk7UnJkdxIUbT2m2nz+EMs1TqIL2tEEaM03Fbx8Bj/TOO8ue5Q1XOXTSNnf0DDWxNgk99Ry/PbjzExQtzKEyPU/344XbW/XVtCenxkbrOfhqOl7ZUUdPWyw9Pn2o4t+iR1h5e3FzFZUvyyE0xRlae7hUkz506GHAhcv/1jrEyiqvGs8h6lD8wztjA7WJzz38TEoyVpecxcStZekobHKO1itEyD39cht0p+d4pQb5LD4POu6Wimc9KG/n2iZOJibQG5TME4XFDeVuPjp8SvODlcCW7PPjRQSSSW05W3y0KXoq7mcXmP56CfOCup2NEV4bAcHE63mNLjLERabMYZmxw9HWpuEcNdF16Jw/EG8/6p2XqO3p5ZuMhLloQHOtRuPnrOrf1aEV+uEVRnZe2VHHEwNajFzYfNpT1CAyhIMkBzdJo9XQGY5CMF6fjGZvFIrxS/Y2zyCoV3pX/jVbG4KjkAYMp7lrn8fUV9DtcQY3xCNfivbOqlU9LGvnWCZOIjTRW7JHD6eIfHx9kYX5yUK1H4eKfn5bjksGzHoUL3StIEm9XhrEWIumTxQYYpuaMy+ccGc09enQMUjR9DhcdBiljIL2TBzzuUYMo7lqmq8/BsxsOcdbsiRSFwHoUymwhgEc+LSchysZVy4NrPRIi9GN7d08dVS09fPvEyUFVQJWxhZa2HjsvbK7kvHlZQbUeeb43s9VIAEh3NWZQekMZqZ6Od5xOWnwUFgNWLR5wQxkuTkdxH4KXlcUgSoR3DFKGAQPstcpLWw7T3uvgphPUq7ysFapbe3hrVw1XLMsjwWAd7aWUPPJpGQVpsZwxKzPc4qjOmk2VdPU7DXld6l5B8hSKhMG7WaOk+nvH6VgtipJklEXWe2zgjtMxkIvNU34CMJwS4fJS3KMjrCRG2wyl3GoRp0vy2PoKFuUns7ggJaif5bFvhNIQ//hn5QB8Y1VR0D9LENqxbatsYcfhVm48vkj1uke+CEJrQrI7XTzxeQUrJ6WpXo/Ll4E6SGaQtv94moKC8dLFvRciMFaxyKHG1t7roNdunDIGA8qfwbL0jlVuow2juGuV9/fWUtnczbcMeJfe3mtnzebDfG1uFjnJMeEWR3Ue+aScpJgIVi/ODbcoqvPWrhpq2nr51onBV2zDgQEUpME7daNVLfYOhgVjxekcOzbjKREWn+vSKEqEd/IAaENxF0L8jxBCCiHS3c9PFkK0CSF2uB93hlXAcfLY+gryUmM4c/bEoH9WqGO0/7Wlis4+BzedYLxFtqqlm3f31nL18nzDBZ6Dcl1Oyojj5GnG6wUIBlCQQB51JwvGWYgGg7SNF6cj3aniA26oRINZ/1yDC01ClI3oCIthxiY1prgLIfKAM4FKn5c+lVIucD/uDoNoqlBS18Gm8mauWV4QdBeNN6FwZUgpeXbjIRbkJTMvNzn4H4i7HUdIPgnWbDqMAK5eEZqil6EM0t5d3caXh1u5bkUBlhBclwOuX7PViP9415vxLER1BollGQzSVv5OSIyiqbPPEFWLBwN9ledGazYskQPKn6dMg2GUW1/3qLu8RhizR/8M/JTwtg8LGs9urCTSajGki2ZjeTMHG7q4OsiZa+HA7nSxZvNhTp0xwZCuw2c3HiI6wsLFi4x3XXowgII0aEEy2kI0VJyOS0JTp/7Hd+zYPAH2xlBuFcV98PmEhCgDje1Y5bbf4aKtxx5yWYQQFwLVUsovh3h5pRDiSyHE20KI2aGWTQ26+x38e1sV58ydSJq7KGewESFsNvLsxkoSo22cNy87ZJ8ZKt7fW0djZ58hW6a099p5dccRLpifTVKMsbIOvdG9U9TlXQgJbcRDqIVvnM7EJOUupKatd8CdqFd8F9m0uEgirIJao1iQpMRiGbz/mJgUze7qtjBKpB6+MUgTk5Rrsaatl+TYSNU/TwixFhgq+ObnwM9Q3Gu+bAMKpJSdQohzgf8AQ3Z2FULcDNwMkJ+vLUvGG1/W0NHrCMsiG2xzXGNnH+/sruGaFQVBaysyHKEwdj678RA5yTGcGMKmtEqGXvAH9+r2arr7nSG9Ls0stjHgnS0EkJkUTW2bMRSkwYauygCzBhainrDJpBYu72qDKBW1JyZFG2Js4EkeGHyenRxDTVuvIYqY+sYgZbkV92D97qSUp0sp5/g+gDKgCPhSCFEB5ALbhBATpZTtUspO9/vfAiI8AdxDHP9hKeUSKeWSjAxtdVh/duMhpmXGs7QwuKn94eClLVXYndKQ7rXyxi7WlzZx1fL8kMaNhQIlbqySOTmJzMsNbmp/uPFLQRJCnC2E2C+EKBVC3D7E6ycKIbYJIRxCiNU+rzm9MkleU0twD9IrBgkgx0ALkcsnBsmjIB1pNYAC6DM2UBbaGiOMjaMLmIJy7vocLlq6Q++GUhvfGKTsZPd1GWLlVkq5S0o5QUpZKKUsBKqARVLKWiHEROE2cQkhlqHMdU0hFXCc7Ktt58uqNq5Ymh/S9h+h+CgpJS9tOcyywlSmTEgI/geGmJe2HMZqEVxmwLixXdVt7KvtCPl1GQ5GVZCEEFbgfuAcYBZwpRBils9ulcA3gOeGOESPVybJBeOU9xhcPhYkz0JkhO7icsANpQwwNS6SKJvFEFYW3xgkgOyk6JAvssHCu/wEDFpZjrTqf3zHxiBFY7UIrSm3q4HdQogvgfuAK6TO7ppe2VaNzSK4cEF44nOC+XXtONxKWWNXWALPg91qxOWSvLK9mhOnpoc8FCIUWWwvb6sm0mbh/JDHjWmz1cgyoFRKWSal7AfWABd67yClrJBS7gRcQZBxRLx7XsHgQlRjADebrxIhhCArKZojhhjb0YssKDFWde29uIyQpTeE4g7GvC6tFkFmQlTYlVu3JanR/f/fpZSzpZTzpZQrpJSfh1W4AHG6F9mTp08IWXB2KHl5WzVRNgvnzA1+XadQs6GsiZq2Xi4xYHZXv8PFa18e4YyZmSTFGjc424M/ClIOcNjreZV7m79ECyG2CCE2CCEuCkQ4f5DHxHooC1G1ge7UhddZykqKMUSM1WAAupcFKTkau1PS2KX/QG2Xj+s3K9l48WPev7usZOO4R7XA+tJG6jv6uHRRIFOtugTrNqXP4eT1nUc4a/bEsPRdE4KgmiH+va2ahChbWPquCSGCGsT88YEGmrv6uSQM1+VgkLax6iAVSCmXAFcBfxFCTPbdQQhxs1uJ2tLQ0BDQwX1jkAYsSAZQkAbimL22ZSVHG3dsA+dO/wutYtkcJD0uigirMET8mG/yAGCoAHst8PK2KhKjbZw603gVij/c10Brtz0si2yw6epz8PbuGr42L4voiNBm5oWCl7dVkRYXGdLMvHDij4JUDeR5Pc91b/MLKWW1+28Z8BGwcIh9xpxF4pISr2xq0uIiibRZDOHK8O6a7iE7KYa6Dv0Xi/QN9AVjZelJnxgkI2Xp+RYwBSV+zCjJEeGms8/BO3tqOX9+NlG20C+ywY67fXlbFRkJURw/ZcikQl3z7p5auvudhnSvtXb3s664ngsWZBNh1X0CvF/4M8rNwFQhRJEQIhK4AvArG00IkSKEiHL/nw6sAvaOVdihcPlkC1ksRorTUf56j29iUjROl9R9raeBGCSvKzA72RPIrO+xwbHJA+DO0jPodZmVFGOYLL1w896eWnrtrrBbWIKh67Z12/lwfz0Xzs/GFqZFVhC8ViP/2XGE3JQYlhSEpyyD4j0Mzuje3l1Lv9PFJQvDo/wNthoJHaNeoVJKB/A94F2gGHhRSrlHCHG3EOICACHEUiFEFXAZ8JAQYo/77TOBLe5Mkg+B30kpVVaQjt2WlWQMN9RQsR4DKdU6VyJc8ljrWEpshGGy9Hxdv+Cxsuh/bCNfl/ofX7h5c2cNOckxLMo3Xu2j9/bWYndKzp9vvMrZLV39rC9t5Lx52SHpTRZq3txZQ2FaLHNyEsMtSsjwq5K2u9DaWz7b7vT6fzOK6833fZ8Dc8cp48iyMdRCFMPG8uZgfmxI8KmlCHhn6fUA+p1Afasxe/43jvXvaNcvKFl6tW01uFxS1xOolBIhhi5jUNPWy5wcYxePCybtvXY+LWnkupUFYasxE8xWI2/tqiE3JcaQBQbf21uL0yX52tyscIuiOs1d/XxR1sS3T5xk+NpH3ujekeibTg1KIHNte6/u43Q86e6+MUhghEBmT6Dv0RglS09y7EJjlCw9ZWxHY6QsvXCydm8d/U4X587TwiKr7vzZ1mPns9JGzp2bFdZFVojgZEK9uauWvNSY8FpYRHBco+/uUZS/c8Oo/A1cM2arEf/xjUECJZbFCHE6nuvAe3yJMTZiI61hrzkzXoaKYwHjZOm53FYWb4ySpTfUb85IWXrh5K1dNWQnRbMwLzncoqjO+3vrsDvDu8gGi9bufj7XgPIXLN7aVUNBWiyzs7867jUwgoLkOjbrIjvJGMG+QxVT9Lih9G5lGWpsYKwsvWOUP4Nk6fnWeAJjZemFi/ZeO58caOQcDVhYgsFbu5TYqvlGdK/tqcNhYPfa5web+JpBlb+R0L2CJOGYk2YUc/9QcTqgWCL0HqfjctdcH+rcGcH6N1QWm1Gy9IayjoGxeumFg3XFbveaRhZZNV01bT12Pi1p4Ny5E8O+yAajTuSb7tiquWGOvwtGht57GnCvgXcWm7EKRQaVIWOQDOLKkMMuRPp3Qw2VCQXGsv75LgRGydLzrV7vIctAvfTCwTu7a5mYaEz32of76rE7JedoRPlTk/ZeO+sN7F57Z08t+alfPfcaGEBBGioeIjHaRlykVfftRoYaG0BOSgwNnX302p1hkEpdhoofA/23ihlKiRBCkJMcY4CxDXNdJisB9np3j4aDXruTTw40cvqsCWHPcAxGLOz7xXVkJESxIDdZxaOOHTWtY58caMDhkmFpLTIkKo6tq8/B56VNnDErUzPKXyhr0RpAQTp2kRVCkJNihIXo2BgdgLyUWKTUtxIxXAxSXqqiIB1u7g61SKoyVAwSQG5qLIeb9XveYOjfHEBeaiwOl9S9hSwcfFHWRI/dyWkzNbLIqki/w8Un+xs4dXr4lT841q0/XtYV15MSG6GJulVq6zCflTbS73RxmgZa3oRDPzOAgiSPzTkG8lNjdb/IuuTQP+b8tFhA30rEcFlssZE20uMjdT02GDoGCSA/NYZKA4xtqMkqP1W5LvU+vnCwrriO2EgrKyelhVsU1esgba5opqPPoYlFVm0cThcf7q/nlOkTsGpA+VObdcV1JETbWFqYGm5RwoLuFSSGu1NPURQkPfeGGiq+ChQLEsDhFv3eqQ8XgwTuc9ei70V2OPdoXkosbT122nv125JjOOuY57qs0rmFLNRIKfmguJ7jp6RrqsGpWlPn2uI6Im0Wjp+qjd5ragZpb6tspbXbrhnLn5qtRlwuyQf7Gjh5+gRN9F7zKO6miy0Ahr9Tj6Wr36nr3lBKR/hjBzchIYpIm0XXVhY5TIYeeKx/+l5khwtkzkvVv/Vv2OSB5GgsAt0rt6Fmb007R9p6OV0ji6yaSClZV1zPqslpxEb61bhBV6wrriPCKjhxmjaUPzX5sqqVxs4+TpthPMufvxhAQRr6btYI5v7hYpAsFkFuSozuF1kYJsYqVYkfczhdIZZKPYZ1jxpAQRruNxdhtZCdrH8XYqhZV1yPEHCKRhYiNWM9Sus7qWzu1oyFRW3WFtexvCiNhOiIcIuiOuuK67FaBCdPzwi3KGHDAAqSHNJjnmcABWm4hQgUd4ae79SHi0ECZWxOl6RGx7WeRnWP6thCNpzVFtzXpY5/c+FgXXEd83OTyUiICrcoR6GGq2ZtcT2AtuKPVGo1UtHYxcGGLk2NTajYamRtcR2LC1JIjo1U54DjJAydRvSvIMlh7tSNkA01XDAsKOPT+yILQ9+tDlhZdK0ADh2DlBQbQUK0TedjGz4TKC81RtexcaGmuaufndVtnKoR65HafHygnplZiQO16YzExwcaAAx57urae9lX22HIsQWC7p3Cw92pGyEbSg5RbNBDfqoS7NvWYycpRn/m3QEFaQj731FxOpNDKpZquIZOrgQUK4ueLZtyGKstKGNr6Oijp99JTKR2Ao61yvrSRqSEEzQSwAzDX7eB0t3vYOuhFm5YVaTSEbXFpyUN5KfGUpAWF25RRsRut1NVVUVvr/8W+e5+B49ckEVmYi/FxcVBlM5/nA4Xj1yQRZqjgeLi5oDfHx0dTW5uLhER/q+XuleQRnRDpep7IXINE4ME3q6abpLCXN5+TAy42I59KSspGqtF6NpCNppyW1LfEWKJ1GO4LDYYLEFR1dLN1MyEUIqlSz4taSAx2sY8jRRQ9Ga8rpqNZc3YnZITpmorhkWNLDa708UXB5u4aGGOGiKpxlCtRqqqqkhISKCwsNDvGlCVzd3E9DqYmZWgmQKRPf0ORH0nBWlxARsFpJQ0NTVRVVVFUZH/CrvuXWwuKbEMM4r8VH3H6UiGdtPAoJWlSqfjG4hBGkJDslktZCdH6/vcjai4x1DV0oNLpxWnR4pByk3Rv3s0VEgp+bSkkVVT0g1ZQ+eTkgaibBaWFIa/gKLabK9spavfqTnlbyh6e3tJS0vzW9GRUtLZ6yA+2qYZ5Wi8CCFIS0sLyIoGhlCQYDijcF5KLEdae3WbDTVyrIe+g9CHq6TtQe9uqBEDmVNj6XO4aOjsC61QKjFaDBJAZZN+z12oONjQRU1br+YWWbWCYT8raWT5pDRN1XYaYJyD+7SkAatFsHJy+At7+jJUAHogik6v3YnD5SIhSvcOpqMYi7KnewUJhl+I8lP1nQ01XHwVQFJMBInRNt26oQaDtEfI0tPp2MDtHh3m5Hm7R/WIHMFqmxEfRXSExQzU9oNPS5QgXy3FH6lFTVsPJfWdnDBFe2NTwyrySUkj83OTNBf/qYbBp6PPAUB8tPYUpC1frOeMk084apvD4SAzM5MjR46o/nm6V5BGi0ECHVtZXCNf8Hk6diF67nGGG15+WiyNnUqwrx6RjBDIrPMsPcnw7SiEEGaqv598WtJIYVrswPWgHca/yn5a0gjACQYsoNja3c/OqlbNWf7UorPXQXSENeTVs53O0ef6RctXcqS6mkOHDg1sW7t2LbNnzyY7O1t1mQygII3kynCb+3U6WQ+XKu6hIC2WQzp1ZQwWihw+kBngUHNXyGRSk5HcULkpMQgBFY36PHcj/eZAOXd6/c2Fin6Hiw1lTZpeZMdTK+izkkYyEqKYrsFA/fG24/j8YBNSosnq2eMNQHe5JF39TuJVdq9VVFQwY8YMrr76ambOnMnq1avp7u6msLCQ2267jUWLFvHSSy/x3nvvsXLlShYtWsRll11GZ2en11EEFouFiy5dzZo1awa2rlmzhiuvvFJVeT1oz4YWIC7X8NlCWUkxRFotVDTpc5GVDK9AABSlx/HenjrsTpcmeuUEgssdFjbc+IrSldTZ8oYuZkxMDJVYqjGSezQ6wkpOcgzljfq8Lkey2gIUpMW5F5Hhf5tfdXYcbqW736mZ/mRqIqVkfWkjJ03LMOT5/6y0kfgoG/M1mHk4Gr98fQ97j7QP+7rTJem1O4mOsPqdODArO5G7zp896n779+/n0UcfZdWqVdxwww088MADAKSlpbFt2zYaGxu55JJLWLt2LXFxcdxzzz3ce++93HnnnUcdZ/Vll/Oj73+X2267jb6+Pt566y3uvfdev2QNFN0rSJLh3VBWi6AgLZayBr0uRMMXigSYlB6PwyU53NzNpIz40AmmAiMVioRBBanMoEpEUXocZY2dw76uZUa7Lk+Ylk6EVdDncGkzQFcDbChrQghYXqS9Lunj1WlK6ztp6upnhQYDmNVgQ1kTy4pSsensptQfnO55ORhZlXl5eaxatQqAa665hvvuuw+Ayy+/HIANGzawd+/egX36+/tZuXLlMcdZuHgxnZ2d7N+/n+LiYpYvX05qanB+R/pXkEZZiCZlxFFar8+FaLiGpx6KMtxKREOXDhUk5e9w44uLsjExMdqwyu3kjHhe2nJYl1aW0WQ+ZfoETpn+1a7AOxoby5uYMTFRM20c1GRDuVLEb0WRNhWk8bTjqO/opayhi8uX5KkrlFoIMeLYRrP0HKzvRAJTJqi/nvjOGZ7ncXHKOial5IwzzuD5558f5UiSK6+8kjVr1lBcXBw09xoYPAYJYFJGPJXN3bpM9R8tBmnSgJVFjwrgyDFIoG8ry3AtcDwUpcfR1e+kvkN/qf7DNVE28Y9+h4uth1o0aT1Sgw1lTWQlRQ/EgBqJTW7lb/kkbSp/48HlknTbncRFBcfqW1lZyRdffAHAc889x/HHH3/U6ytWrGD9+vWUlpYC0NXVxYEDB4Y81pVXXskzzzzDBx98wIUXXhgUecEgCtJIC9Gk9DjsTqnLtOPRrGPJsZGkxkXqMpZlpGa1Hooy4nQ5tsEA9OH3meRl/dMboynuJiOzq7qVXruLFZO0qSCN58xKKdlY1szyolTdWUb9YWNZM3GRVuZk6y8ucjS6+x1IKYmLDI5jafr06dx///3MnDmTlpYWvvvd7x71ekZGBk888QRXXnkl8+bNY+XKlezbtw+AO++8kzdef31g35kzZxIXF8epp546YIEKBoZwsY0YpzOwEHUOxLXohdHcNKAogAd1usjCKEpEehyt3XZauvpJidOPK8Iv5c8ThN7YpclicyMxUoaeyehsKFOsEMs06oLyMBY3VFljF42dfZq2sAhGdkONxMbyJhYXajf+aDy/ys5+JwKCZkGy2Ww888wzR22rqKg46vmpp57K5s2bj3nv3XffTY/dSUldx0Ca3o4dO4IipzfaPMsBMGoMUrriS9XjnfpoYwNlodWjlWUwBmnk+DHQnwvRH+UvOymGKJuFsgZ9jQ1GztALNUKI/xVCVAshdrgf53q9docQolQIsV8IcVY45fRmY3kz0zLjSdWo0j8e5XejW/kzovuwuaufA3WdhhwbQFefw529pnu1QDV0/02MFoOUEhdJSmyE7hZZcFuQRtlnUkY8DR19dPTaQyKTWshRsthAv8qt9EP5s1iEbpXb0ay2YeDPUsoF7sdbAEKIWcAVwGzgbOABIUTYU+rsThdbK5pZoWELi4ex1AraWN5ERkKU5q31YxnbpvImAM26Rr0JtIaVyyXp7ncSF6T2IoWFhezevTsoxw4mBlGQRp6tJ2XE626RBf/G5u2q0RPSDzdUbkoMEVahu1T/0UoYeJiUEae7sYFuYpAuBNZIKfuklOVAKbAszDKxu7qNrn4nyzXuXhsLeok/GqtoG8qaiY6wMDcnWVV51GSsY+u2O5FSql4gUk3CcUUZQEEa/YublK7PhcifO/XJOg329ccNZbNayE+NpVxnY/NH+QNFua1s7sauswxLDcYgfU8IsVMI8ZgQwtM6Pgc47LVPlXvbMQghbhZCbBFCbGloaAiqoJ4sqGUadtOM9cxWNndT296r6fij8bCxvJnFBSlE2nS/bB5Dl7v/Wmxk2I2smsKvMy2EONvtxy8VQtw+xOsnCiG2CSEcQojVPq9dL4QocT+uV0tw8HbTjG5B0qMbarRig6D0LLMI/RVU9CeQGaAoPV537lF/lD9QXIhOl9RdW47R3NpqI4RYK4TYPcTjQuBBYDKwAKgB/hTo8aWUD0spl0gpl2RkBLf1x5ZDLRSlx5GREBXUz1GDQAOZt1S0ALC0MGWUPcNPoGPr6LWzr7adJQXaVWy9CXR83f1K9WytBp97M55WKoEyqj3N7be/HzgD5S5ssxDiNSnlXq/dKoFvAD/2eW8qcBewBGVcW93vbVFDeH/v1L1TqufnJavx0SFB+pHFFmWzkpsSy0GdFcP01w01OSOOTw404HC6dPHjBW8Fyb/r8mB9J5N1VOjTn+QBdT9Pnu7PfkKIR4A33E+rAe9qfrnubWFDSsn2yhZOnKbd/mvjYVtlC/FRNqZO0F7/tfHy5eE2pIRFBdpW/oZrIj0SUkq6+x0kxUQEQSJ948+KswwolVKWSSn7gTUo/v0BpJQVUsqdgK+v4CzgfSlls1speh8lYFIV/L1T9yw+equo7W+sx7TMBA7UdYRAIhXxI5AZYGpmAv1OFxU6asrr7x3OVHcjT72dO8noyQOhQgiR5fX0YsATCfoacIUQIkoIUQRMBTaFWj5vDjf30NjZz6J8jS+yYzy52ypbWZCXHJQ2FeFmW6VyT79ARzfY/tLncOF0SWKDVP9ILV579VUOHth3zPaKigpyc3NxuY5WPxYsWMDGjRvH9Zn+KEh++/LH+t6xxgCM1q7CQ2FaLJE2C/t1thC5/KxYPH1iPOWNXfQ5nMEXSiX8VW5nTNSfEiFHacTrIT7KRm5KDPvrdKa4u0JrQRqF3wshdgkhdgKnAD8CkFLuAV4E9gLvALdKKcP6A/EsslpXkDwE4qbp7HOwv7adRfnJQZNHLYQQAbtptlW2MHVCvOatLJ6fZSDj6+7XR/zR66+9SlnJ/mO2FxYWkp+fz6effjqwbd++fXR0dLB8+fJxfaYmfBZjjQHwpGqOZoWwWS1MyYhnX61+FlnwNOIdfSGaPjERh0vqKlDb3xikKRPisQh0de78Vf4ApmcmsL92+O7aWsSfAqahQkp5rZRyrpRynpTyAilljddrv5ZSTpZSTpdSvh1OOUFZZOMirUyfaDwX1M7DrbgkLNS4C2osuFyS7ZWtulFsA6W7z4nVIogKYvB5RUUFM2bM4Oqrr2bmzJmsXr2a7u5u1q1bx8KFC5k7dy433HADfX1K66Xbb7+dWbNmMW/ePH784x/z+eef8+Ybr3Pvr+/khBVLOXjw4FHH9/Rm87BmzRquuOKKccvtj01tPL78auBkn/d+5Od7R8XfGCRQLBHrDzaq9dEhwd+CfNO9XDUzs/RRAt/fGKToCCuFaXEc0KOC5MfJmz4xgY8PNNDvcOkmO0bK4HT7NjrbKluYrwMX1FjiWAasY3nGUyLKGrto67GzqCA53KKMn7dvh9pdR21K7XeQJgQiYowWpIlz4Zzfjbrb/v37efTRR1m1ahU33HAD9957Lw899BDr1q1j2rRpXHfddTz44INce+21vPLKK+zbtw8hBK2trSQnJ/O1885nwapTufHaK49p8vz1r3+dBQsW8Le//Q2bzcYLL7zASy+9NLbxeOHPjLwZmCqEKBJCRKIUX3vNz+O/C5wphEhxp9+e6d6mCgHdqU9MoK69j9bufrU+PuiM1mfOQ1F6HBFWoSsry0AGoh+T8bTMBF25Rwdcv37sO31igmL901GmnpYsSHqhu99BcU2HrqwQgbhptlW2MjkjjqRYbbugQPldBuI+1JNr1POz9LdQpETiCtENT15eHqtWrQLgmmuuYd26dRQVFTFt2jQArr/+ej755BOSkpKIjo7mxhtv5OWXXyY2NnbUY2dmZjJnzhzWrVvHjh07sNlszJkzZ9wyj2pBklI6hBDfQ1FsrMBjUso9Qoi7gS1SyteEEEuBV4AU4HwhxC+llLOllM1CiP9DUbIA7pZSNo9bajf+ummAAbP2vtoOXVSxBU+sx+j7RdosTEqP15WVxfPz9Ve5fXdvLb12JRVV6/jr+oXB63J/bQczJurD+ifRVAySLthZ1YbTJXVhhQj01Hqy806fmRkcgcLM9soWEqNtuso0HRYfS09nr53yxi6K0uOIjA6ucus7HyYnJ9PU1HTMfjabjU2bNrFu3Tr+9a9/8fe//50PPvhg1ON73GyZmZlceeWVqsjsV9i6u3z/Wz7b7vT6fzOK+2yo9z4GPDYOGYfFXzcNMLD47NeRgiTxz4IEykK79ZAq1RNCgsvlXyo8KGOTEkrqOpmbmxRs0cZNIK7fSenx2CyC/TpSbk0LUuB4rBALdeSC8tcKUd7YRUu3ncW6ij/y34S07VArC/NT/HKZawV/R9ftblAbigDtyspKvvjiC1auXMlzzz3HkiVLeOihhygtLWXKlCk8/fTTnHTSSXR2dtLd3c25557LqlWrmDRpEgDxCQl0dQ5vab/kkku44447iI2NZd26darIrI+gh2Hwp+eVh8zEKJJiInTnqvH3Nzl9YgLVrT26KYY5FuufXs5dIK7fSJuFSRlxusrS86eAqcnRbDvUyqSMOFI02qB2PGyrbAW0XyPIQyCXbnuvnQP1+nGNBvqz7OpzEBWiBrXTp0/n/vvvZ+bMmbS0tPCjH/2Ixx9/nMsuu4y5c+disVj4zne+Q0dHB+eddx7z5s3j+OOP59577wXgsq9/nScf+hsnrlzGwYMH+cc//sE//vGPgeMnJyezcuVKMjMzB5Sq8aLtwgejIANYiIQQTJ+YoKs7dSklFj8vXE+gdkl9py5+zAPWPz+GV5DqLtOgk2yvQJQ/UGKsdhxuDZ5AKuNv8oCJgpSSL6taOWFqerhFCQpfHm4lPsrGFCO4oHzYXaUUiFygg/IFgSKlpMfuDFnpApvNxjPPPHPUttNOO43t27cftS0rK4tNm44tWXbccat45YMN5KfGkhwbyeTJk4/Z5z//+Y+qMuvaghToQjRjYgIHajsC7nQcLlwBdE33jmXRE/4MT29lGgauL3/PXWYCVS09dLr7IWkdqb1ebJqmrr2Pho4+5ucmh1uUgPB3ltxZ3cacnER9uaD8HNzO6jYA5uVo37XvjT/j63cqBSJjNF7/KJzoXEHyPwYJFCWio89BVUtPEKVSj0C6puckx5AQZWPvEb1YWfyPQQKYmZVIcU27LpTbQGKQgIHSDMU1+jl3OloLw87OqlYA5uhskfUHu9NFcU07c3U0tkB0+11VbeSmxOjGNRrIjUtPv1I3NTYEiS+FhYXs3r179B01hq4VpEBikABmZys/4j1H2oIlkqoEcqdusQhm5ySyq1ofYwvU+jc3J5HGzn5q23uDKJU6BBKDBAwEnu+q0s+5My1I/rO7ug2rRTBLJzXKAjm1B+o66He4mKsz65i/7KpuY54OEkPGQo/diRCCKB1kBocLnStIgS1EMyYmYLMI3SgRgcZ6zM1JYm9NO3anb0s87RGo9U9PSkSgyl9mYjQZCVHsNuh1+VVnZ3UbUyfE68+V4Yex1vN71JUFCf9ajbR291PZ3M3cnORgi6Q60meEQ1nee/qdxERYdJNwMV4px+J90LWCFOhCFB2hlPnfqYNFFtx36gHsPycniX6HixId9PYatP75t/+srCQsAl0ot4Eqf6DEOOzUwdjAneavmXa12kZKya6qNl0pEIGwq7qNhGgbBamjF/PTG565Ru/nLjo6mqampqMUBCmlW0HSmdI+RqSUNDU1ER0dHdD7dJ3FFqgrA5SL/Z09tUg/q1SHk0BikADmuc3cu6vbmJWtbXO+DDAGKSbSytQJCbpQkAJ1/YKi3H6wv56uPgdxUdr+WUoJIcgKNgQ1bb00dfXryk0TyHW7q1pR/vQUoO0vRlGQcnNzqaqqwrsRvMPpora9j57YCNprtT3feHA4XdS192FviiA2MnCZo6Ojyc0dslzjsOjjmxmGgTv1AO5m5+YmsWbzYapaesjT+F1PoLEeBamxJETZ2FndyteX5o3+hjASqPUPFCXi4wP1mlduA3X9gjIJSwl7a9pZWpgaJMnUwd8WOCYMWKv1GKDt66bxpd/hYl9NB99cVRgagVRCCP/cLbuq2ihIi9VF+xQPnp+l9/AiIiIoKio6ar9Xd1TzX6/t4K0fnMBMjd9Meyhv7OJbz3zEXy5fwEUzc0Lymbq+DwzUTQMwz+1P1oObLdBYj8FAbe1nQ43F+jcvN0kXgdpjUf70FGMlzUKRfrOruhWbReimiTT479Y/UNdBv9Oli+r2Y2GnkV2jVW1E2SxMzTRe7So1MYSCFMhkPW1iPJFWCzurW4MjlIqMZSGal5tMsQ4CtV1jdEOB9pXbsSh/mYnRTEiI0oUL0Uzz959d1e1MzUzQRQ9BX0YzsuzUYYC2h9HsR81d/VS39uhybP6wq7qNmVmJRFj1pwKMZtlUE/19O14MLEQBjCLKZmVGVoIu7tTH0vPKE6it+dYVYxjbrKxELALNZ3sNLiyBDXBuTpIuFCTJ+DNKvgooAdqtuisy6C+7qttIjLaRr/FQBV/8uXYH4o90Zh3zJ9zE5ZLs1mH5gnDMOYZQkALNqPEsRJ6GqVol0CBtgPnui/7Lw9peaMfSzysm0qqLthxjsSCBYv072NBJW4+2++mN5br8KlLd2kNLt505eluI/Dy1u6vbmJubZMh4NM9NmB5jx0ajvKmLrn6nIccG4FRxXde1guT5GgL9fS7IS6aj18HBBm2nw8sAWo14yE+NJT0+ki2HmoMjlEqM1U2zuCCF7ZWtqv4I1GYsrl9QxiYlbHd3ftcqLpdZKNIfimsUK65eCkT6MtIvzOF0sb+uQ79jG2X62FvTTl5qDInR+gnQ9mak8Xkq9hvx3PXancy+6x2e+qJClc/St4IUYKq4B0+W0OYKbS9EksDHJoRgcUEKWzQ+trFWY15amEpnn4N9Gm5cOxbXLygNMa0WwdZD2j53ZqFI/yiuaUcIpUCt0Shv7KLf4dJV8PkAfsw7xTXtzJyov7H5M6UW17RjswjdBWj7M7YDdR302l1kxEep8pm6VpDGki0EUJAWS3p8FFsqtG9lGcuN+pKCVCqbu6nv0G6211gX2cUFKQCaViIGC0UGNsD4KBszsxJ0odyaLrbRKa5ppyA1VvN1rXzxJ2Rhr9sKoUsFaRR6+p1UNHYZcmygWDYnZ8QTZdNf4sBoFKt8XepcQQq8YrGyv2BpYQqbdeGGCnwhWlzoViI0vNAqgb6Bjy03JYbMxChNW/8GXL9jeO+SglR2HG7VdBaiZGyK+1eN4pp2XS+yI7tpOoiwCiZn6MsK4WEkD9uBug5cEmZm6dfyN1KmV3FNOzP0PLZRrsvYSKtqiQP6VpDca8hYLRGHm3uobdOulUWJ9Qj8fXOyk4iyWTStRLhcY7MgCSFYUpjKVg1b/8bq+gVYUphCj905cCekRcxmtaPT1efgUHM3M3TopvGHfbXtTM6IJ9KmvyVktCtXbStEKBltbK3d/dS09ep0bP65RqdPTFCtsrv+rm4vPFryWGNZAM0HM49lkY20WZifl8xWDY9tPG6aJQUpHGnrpbq1R2Wp1GGsrl9QLEig7fg4MwZpdPbXdSB1aoXwN45Fr0G+o1Fc005cpJW8FH2VL/AHT+KAHhWk0ZBSKtYxFW9K9K0gjWMhmpWdSEyEVdPxHuMpyLekIIU9R9rp7neoK5RKjDW+CgaVCK3GkHnKR4zl3E1MiiYnOcawyu1XBT1bITwM56Zp7uqnrr1Pt2MbrdVIcW2HqlaIUDJUqxFvBq5LHSYODIxtmNdr2npp73UwS8WbEl0rSGOtNwMQYbWwMD+ZTeVaXojG3jV9aVEqDpfUdDDzWN00M7MSiI+ysaFMm+dujHUiB1hWlMrGsmbN1umS41BuvyoU17STEGUjNyUm3KIEjJFdUKPhsUIYcWygnLu0uEgyEtTJ8tISnutyhornTucKkvJ3rJP1iklp7K1pp6mzTz2hVMQ1jq7pywpTibAKPittVFcolRiPdcxmtbBiUiqfH9Tu2GDsVpZVU9Jp6upnX602q6GbFqTR2VfTwYysBEPGag0uRPqzQoxGdWsPHb0OwypI+2o7mJmVaMjr0jNfTlfROqZrBUmOMZ3awwlT0wFYf7BJNZnUZDxd6+OibCzMT2G9phWksf9Ij5+SzqGmbg43d6solTqMx/ULytgATZ87A86vquFyyYGFSM8M76bpICMhinSVas2EmpEu3X0DMTr6VP48HoehTp2nuKfe63IN5x7dW9NOboq6xT11rSCNJxgWlNYOidE2PitpUFEq9VCa1Y79/SdMSWfPkXaau/rVE0olxpsJdbxbudWihWw8rl9Q4pCmTIjnUw2ODcbWRPmrRFVLD519OrZCjHJqje6CAphuwOxDXRf39IN9Qbguda0gyXEuRFaL4LjJ6XxW0jhi0F64GK+VZdXUdKREk66o8WZCTc6IZ2JiNJ+VaG9sg67f8VnINpU30edwqiSVeozHPfpVwChFFIeaEe1OF6X1nbq1sHgY1jpW205BWizxOivu6ctQ65mRr8teu5Pyxi7Vg891rSANLETj6PN7/NR0jrT1UtbYpZJU6uGS4+tgPC8niYRomyaViLH0mfNGCMGqKemsP9iouWDmQdfv2I9x/JR0eu0uTQbZS7MO0ogcqOtACJims1YO/lDR2EW/06VrN81I1+7+2g6mZ+p5bMO/dqCuA6tFMHlCXOgEUpGRxlZa34lLqm/507WCNF4LEgzGIWlRiXCNIwYJlGDmlZPS+FSDFjI1OsIfPzWN1m47e45oq6jieGOQAJZPSsVqERq+LsMthXY5UNdBbkoMsZH6tEKMdMN5oE5p8D11gn6ViOHoczipaOpmmo4VpJE4UNdJYVqsIVuMHKhTYsfUvinRtYKkhiujIC2O/NRYPj6gvTgkNWI9TpyWQXVrD6X1nSpJpQ5qZEIdPyUDIeDD/fUqSaUO441BAkiIjmBxfgof7jfmdakWQoj/FUJUCyF2uB/nurcXCiF6vLb/I1QyldZ3Ms0ICsQQN1Ul9Yp1bMoEfVvHhqrxVNHYjdMlddfEdSiGuh0ure80hvI3xOBK6juxWQSF6epax3StIKlhQQI4beYEPittpKtPW0UV1ahYfPrMTADe21ungkTqoYYVIiMhigV5ybyvubEpf8erRJw+awLFNe2ay9TTYAzSn6WUC9yPt7y2H/Ta/p1QCOJwuihr6GKKjhfZkS7bkvpO8lNjiY7QrxViuOGV1CtWCCNax3rtTg41dTFVx4rtSIaQkrpOitLjiLCqq9L4dTQhxNlCiP1CiFIhxO1DvB4lhHjB/fpGIUShe3tQ7+IGFqJxztZnzppIv8PFpxrLZnONM04HlIyoeblJmlMiUGFsAGfMymRXdRs1bdppO6KWO/OMWRMBWFusrXM31kbDXwUONXfT73QZcpEFKKnr0PUiOxIH6jqxCJiUoc8YnZEob+zCJWGKESxIQ1Ba3xEUy9+oCpIQwgrcD5wDzAKuFELM8tntRqBFSjkF+DNwj9drQbuL87gyxjtVLy1MITk2gvf2aGshUiNOB+CMmZnsONxKfbt2GvOqNbYzZykWsrUaUgDVsiAVpccxZUK85pRbDVqQvieE2CmEeEwIkeK1vUgIsV0I8bEQ4oRQCFIyEKOjfyXCV823O12UN3YxRefKn9Jq5NjtpfUdFKTF6ds65p5zfMdX4g6x0HPigGfK8XWP9tqdVDZ3B+WmxB8L0jKgVEpZJqXsB9YAF/rscyHwpPv/fwGniRCkuXi+pvF+lM1q4bQZmazbV4/d6Rq/YCqhVrbQGbPdSkSxdmJ11KrGPDkjnqL0OE25EAdcvypYe8+YlcnG8mbauu3jP5gKSClDnsUmhFgrhNg9xONC4EFgMrAAqAH+5H5bDZAvpVwI/DfwnBBiyBQXIcTNQogtQogtDQ3jsyKXut00eo7RGe7MHmrqxu6Uul5kR6KkrlPX520kSuo6sAjlpstoHGxQMtjCYkECcoDDXs+r3NuG3EdK6QDagDT3a0G7i1MjGNbDmbMzaeuxs1lDvdkk6typT89MIC81hvf21o7/YCqhViaUEIIzZmWyoayJ9l5tKBFqWZBAUZCcLqmZQHQ1MvQC/0x5upRyzhCPV6WUdVJKp5TSBTyCckOHlLJPStnk/n8rcBCYNszxH5ZSLpFSLsnIyBiXrAfqOslJjiFO53V0hqKkzrgxOv0OxTpmBMvfUJTUdVKYFmfIDDZPAlK4LEjjwa+7uLHewQ0GaY9/sj5xagbRERbe2l0z7mOphVpWFiEEZ8+eyPrSRlq7tVFVW81MqLNmT8TulJpxkaqpuC/ITSYzMYo3dmrjulRzbGoghMjyenoxsNu9PcMdHoAQYhIwFSgLtjwl9Z2GyIKC4d00eq2j40EgjhnboaYuHC6p+yyvgZ/lMecuODE6ocSzXBxzXdZ1YrWIoFjH/FGQqoE8r+e57m1D7iOEsAFJQJO/d3FjvYNzub1haqyzMZFWzpg1kTd31tDv0IabTc16MxcuyMHulLy5SxsLrUSqFua7KD+ZvNQYXt3he1mGh8Hf7/hHaLEILpifzUf762nRQMuYQbd2WMXw5vdCiF1CiJ3AKcCP3NtPBHYKIXaguP2/I6UMqnnY6ZIcbOjUvRViOPdpSX2nrus7jYRH+TOii63f4aKiKTgxOlqgpL6DwrRYIm3q23v8OeJmYKoQokgIEQlcAbzms89rwPXu/1cDH0gpZbDv4jyTtVqWiIsXZtPSbecTDdREUjvWY3Z2IpMz4nh1+xFVjjdeXC71zpsQgosW5LC+tFETgehqlZ/wcNHCHBwubSi3A4kRGtGQpJTXSinnSinnSSkvkFLWuLf/W0o5250cskhK+XqwZTnc3E2/w8VUnVshPPhmY5bUdejewuLBN9C3pK4TIZSYRiPgPb7yxi7D1HeCY5MHSuo6g6b8jaoguWOKvge8CxQDL0op9wgh7hZCXODe7VEgTQhRiuJK85QCCOpd3OBkrc7xTpiaQWpcJK9owBIxGOuhzvGEEFy8MIdNFc1UtYS/ro7a1ZgvXJCDS8JrX4ZfAXSp6PoFmJWVyNQJ8fxnu5auS20oSFriwECMjjEWIm889Z0MMbYhLt0D9R3kp8YSE6nvGJ2hfpZGqe80VGkRpfp5V9ASB/yySUkp35JSTpNSTpZS/tq97U4p5Wvu/3ullJdJKadIKZdJKcvc24N6F6dmDBJAhNXC+fOyeH9vXdgDftW2joGiRAC8ukMLSoS6Y5syIZ65OUnaGJvbQ6uqhWxhDlsOtYS9aKTWYpC0hFHcNEOd2kp3fSe9j204Suv07xodjhKzvtOY0XUlbTWzhTxcvCiXfoeLN74MrztDrRpP3uSlxrK0MIV/ba3SQINXqUoavDcXLcxhV3Ube8Pcmy0YcToXLshGCHhpy+HRdw4ipgVpeErrO8lKiiYhOiLcoqiC9wzhUf6M4z4c/N/hdFHW2Kn7+k7eeI+v1ADVz73xHluw647pXEFS18UGMD83iRkTE3hmw6GwNngduFNX+Vb9quX5lDd28fnBJlWPGyguqX415ksX5RBls/DMxkOqHjdQgnFd5qbEcvK0DNZsPhzWWl3BGJtRKK03Rh2doc6tJ5V6sgGsEL7Dq2xW6jsZ4twNsc3o16UIYn0nXStIasfpgOLOuHZlAXtr2tlW2aregQPEMza1F6Jz5mSRGhfJ0xsq1D1wgASjGnNybCTnz8/mP9ur6Qiji1Rt16+Ha1YUUN/RF9bK2mo0iDYiUkrKG7uYZMBCfKC4MiYkRBnGOuZNeWMXYEwXlMslKW/qYpJBgs99KW/sIjclJmjWMV0rSMHKqLloQQ4JUTae/qJC1eMGgtqBvh6iI6xctiSXtcX11LaFL+PLFaRqzNeuKKC73xnWgOZguH4BTp4+gZzkGJ7ZED4LmdoZekahobOPzj6HoSoVexvQyxu7jDU2r//LGtwKkgHHV93aQ7/DZbBzN3j2yho7KUoPnvKnawUpWPEQcVE2Ll2cy1u7amns7FP12P4SDOuYh6uXFeCSkmfD6IqSQernNT8vmbk5STz5xaGwxVkFK5DZahFctTyfzw82DWRMhZpgKX96Z2CRNcCd+lCu77KGTkOMDY61ypc1dpESG0FybGR4BFIR35vOAeuYARQk36tSSkl5Q3CttrpWkIIRyOzhupUF2F0uHl9fHoSjj87g2NQfXX5aLGfOyuTJzyvo7HOofnx/CGY/rxuPL6K0vpN1+8LTnmPgzjsIw7tyWT4xEVb+8fFB9Q/uB9KMQRoSz0JkpDt1Dy1d/bR02w2xyA5FeaNxlD9fBq5LA7oPGzr66Op3BtU1qmsFKZgZNZMy4jl3ThZPfX6Itp7Qx7O4ghSD5OGWk6fQ3uvg2TC5a4LZEf68eVnkpcbw9w9LwxJoH6wYJIDUuEiuWp7PqzuOhCXl34xBGpryxi4ibRayk2PCLYpqeH455U3GUv4E4igfm5Hch4PtOJQBljd2ER9lIyM+KoxSqYRPq5GyENyU6FpBCnZGzS2nTKajz8FTn1cE5wNGIJiLLCiuqOOnpPPIp+X02p1B+YyRUApFBmdsNquFb584mS8Pt/JFGLL1gu2GuumEIiwCHvk06K3FjsGMQRqasoYuCtNisRrhi/EZQnmDca0QnX0O6tr7DKMg+XKwoZOi9DhD3tB43NqmgjQMAxakIE1Ks7OTOHXGBB5bXx5yV1QwY5A83HLKZBo7+3h+U2XwPmQYlEKRwTv+6sW5TEiI4i/rSkJuRQp2McWspBguXZTLms2HqWnrCc6HDIMZgzQ05Y2dTApisGg48LZCWC2C/NTYMEukHp5A3woDxeh4M2D9a+wyXHbe4Ng6ibJZyE4KntVW1wpSMGOQPPzw9Km0dNt5KMQxH8Gqg+TNyklpHDc5jb99UBr6yuEqV9L2JTrCyvdPncKm8mY+CHEskgyBG+p7p04BCX9670DQPmMoPAuLEQwlauFwuqhs7jakhQWUTKH81FgirLpeLgbw/lmWGSxGx/tn2Wt3Ut3aYxjrmG88rsc1Gsw1UtdXfCjuZuflJnPB/Gwe+bQspHfrA7EeQfwMIQR3nDOT5q5+/vFR6BXAYBshrliWz6T0OH779j4cISyuGIpiirkpsXxjVSH/3lYV0srhg9elqSF5qGrpwe6UxlmIfLO8GowTo+NLeUMXQkBhmvHGV9ncjZTGiR3zpSwEsWO6VpBCdTf7k7Om43LBH98N3d36YLZQcAc3NzeJixZk8+hn5VS3hlIBlEF300RYLfz07BmU1neyZnPoWnSEqh3HrSdPITE6gt+8VRwyN6KndILpYRvESKnUvrhckoom4ylIg4G+nWQnBa/QYLiQUinNABjO9YuU2J0uKpu6TQVpJEKVUZOXGssNxxfx721VbCwLTdBvKGM9fnL2DCxCcOd/doduoQ1imr83Z83OZHlRKr9/Zx/1HaEpjBmqhq5JsRH86PSpfFbayOs7Q9M70OzFdixljcapgQRHW61r23vptbsMFcfifekaLkbHa3CGcx96nbeqlh4cLhn035yuFaRQZtT84LQp5KXGcMfLu0KS9RXKWI+c5Bj+58xprNtXz1u7aoP/gQSvUKQvQgh+c8lceu0ufvn63uB/IKFVbq9dWcj83CTufn0Prd39Qf+8wdi4oH+Ubihr6CQpJoKUWOO14QhFplC48BQaNOLYQHEfTkiIIj7KFm5RVKe8UbGOmRakERg09wd/IYqNtPGbi+dS1tjFfetKgv55wa6D5Ms3jitkbk4Sd722h+au4C+0kuDGV3kzOSOe7506hTd31vDenuArgJLQWOFAqa7920vm0dJt5+43gq8ADtbANC1IHjzBokZLpZZycCEymptGorSH6TBYexgPEhmSGJ1wIAldexhdK0ieyTpUGTUnTM3g60tyefDjg3x+sDGonxVK5Q+U2kH3XDqP9h47P3npy6C72kIRg+TNd06azKysRG77986gB9uH2g01KzuRW0+ezMvbqnl1R3B70IUiAF1vGM1NI3zcNLGRVjITDVBo0I1HuS83UHsYD94/S+W6NObYPO1hUuKC2x5G1wpSOKr63nX+bIrS4/jhmh1B7dMWjliPWdmJ/OzcGazbV8/j6yuC+lkuV2jPW6TNwt+uWkifw8V/rdmBM4h92jzKbShT4X9w2lSWFKTw81d2D9R2CQbBLmCqN3r6ndS09VJkwCwoUBbZwjTjWcfAqw2HAc9de4+d5q5+itKNU7vKm/KGLgpDYB3TtYIUjqq+cVE27r9qEa09dm59dhv9juCkj4cq0NeX648r5PSZmfzmreKgVqEOZquR4ZicEc//XTiHTeXN/Oat4qB9TjiKKdqsFv565UKsFsHNT2+hI0h1rcxCkUdT6W73UmBIV4aksqmbQoMtskIoa8eh5m5sFkF2cnS4RVINz8/yUJP7ujSQ8udR0qVUfnehKM2gawXJFaJUeF9mZiXyh9Xz2FjezO3/3hkUd9Sg+zC0YxNCcO/l8ylKj+M7z2wdSBUNBuFYZC9dnMs3jivk0c/KeXZjcPrQeWKQQj28nOQYHrh6EWUNXXzvue1Bqf0UigrveuKQu09ZgYGqTHtOrdMFh1u6yU8NcCGS0qtjs3apbOomNyUGmz8FMJ12aC6H2t1QvRUa9kNHLbhC36bJHyoGFCQ/rkuXC1oroWYnVG6EIzuguQz6gjf3j4d+h4sjbT0hqeyu6/D2cE7WFy7I4VBTN/e+f4Cs5Gh+fOZ0VRW1cMZ6JEZH8Oj1S7nogfVc//gmXrh5pepNOENRKHI4/t95szjU1MWdr+4hNTaSc+ZmqXr8cDZ0XTUlnf+7aA53vLyL2/69iz+snqdqpVkzBuloPBYkI7Xh8FDbphTAHHFsbdVw4B049Dk07IO2KujvVBSHyHiIToLkfEidBOlTIWcRZC2A6MSQjWMoJMq5yxtubFJCxWdQ/BqUfQzNB8E1RLspaySkFELaFMheCLlLIGexMu4wUulW3PNShhlfYynsehFK10HdbnAMUwIlfqIytszZkLtUGV9KYVgngKoWpQDmsNdlVyNExkHE+NcsXStI4Tb3f//UKRxp7eH+Dw8iEPzPmdNUWxRDVShyOPLTYnnim0u5+p8bufKRDay5eQVZKva8cQW51chIWC2Cv121iOsf28T3n9/O/RbBWbMnqnb8UJUwGI4rl+VT397Hn9cewGYR/PaSuaopSeGy2mqVyuZuEqJtJBsoxX/ATdM8ghXi8Gb47F7Y/zYgITFHWUTzV0BUAlhsigWipwVaD0Hp+7DjGc8nQMZ0KFgFk06GohMgJiUUQzuKQ01dzM/LPnqjlFD8Onz4G2goBlsMFB4PM89TlLyoREUp6u+E3lZoPQxNpdB4YPC7QCjfxaSTYfKpUHCcKou1P3gC0A81d5MeH0Wcb4p/3V54/07lfAgL5CyBpTcpymtsmjJeZz/0tkHHEWgqg6YS2P40bHpIOUbcBJh0Ekw6BSafAok+32HQxsbA2GCI67K3DT7+PWx+FM78P1j2rXF/ps4VpPDezQoh+M3FcwH4+4el9Nqd/OzcmaosRi4NuDLm5Sbz1A3LuPbRTVz+0AYe/+ZSJquUFRFOCxJAfJSNJ765lGsf3cT3ntvG7y6Zx6WLc1U5dqgz9Ibiv06fitPl4r4PSulzOLln9TyibOOvFmwWijyaQ03dFKTF+qcwth+BivVQs0NxaXQ1gL0HLFZl0Y1Lh/hMRdnImKEoESmFyuthYEjrWE8rvPcLZcGMTYMT/gfmXa4ssKN9B93NUL1NcVFVbYIv18CWR5WFOmuBW6E4BfJWgC242UltPXbaex0UeLsPOxvgP9+B0rWQMRMufADmXOK/ctPbpoyvajNUfAqbHoYv/g7WKEVxnHIaTDkdJswK+qJV2dx9tALhdMBHv4HP/qJY7075OSy6HhIy/Tug06EojFWb4dAXUPYh7HpJeS1jhqIITj4NClcFXRkcuC69x3fgPXj1VuU3Nf9K5VpSAV0rSFrIqLFYFCUp0mbhn5+VU9nczZ8vX3Cs5h4gWlmIFuan8MxNy7nxic1c8sDnPHTtYlZMShv3cWUYLUgeEqIjeOrGZXzn6a38z0tfUtnczQ9Pnzpu60g4rWPe/OiMaURFWPnDu/s50tbLw9cuJjl2fAtPuJIHtMrh5m5mZCUMv4OjH3a+ANueUpQCUBbMlALlTjx+gvJjcPRCY4ni1ulpGXy/NQqy5inujZzFiosjuSAkd4VVzT3YLIKsJHcQc/0+WHMVtFTAcT+Ak26DqABumGJTYerpygOUuJ7qrVD2kfL4/D7FKhURp1htppymLLxpU1Qfb1WzUupjwMVWsxOeuxx6muHs38HSb4E1wDk8OklR8CafAif9FPq7BpWJgx8olpv374SELEWZmHKaspDHpqo6NlDGNz83WXnS2w4vXAPlH8OCq+HMXwX+mVYbTJyrPJbcoMQt1e+Bg+6xbX4UNjwAtmjFMjjldOXhj+I8hrHFRFjJiI9Sfjuf3Qvr7obMuXDVC4obVyV0rSCFoqGrP1gsgl9eMJvCtDh+9eZeLnngc+67ciHTJ44wcY7CgHVMLSHHwYK8ZF65ZRXffGITV/9zI/99xjS+c9JkrONYJWWYLUgeEqMjeOKby/jZK7v467oSdla18sfL5pMWP/a6LzKUVTBHQAjBradMITclhp+8tJOv3fcZf71iAUsKxz4hexR3LZy7cON0SQ63dHPmcO7ZfW/C27dB22HFInHqL2DqWTBhJlhHcMn1tinKUsM+qC9WlIgtjykLEEBcBuQth7xlirUlewHY1KtT5HHT9DtdFKbFKkHMtbvgyfPBEgHfeBMKVo7/g6wRimUlfwWcfDv0dSgKYuk6ZdEteVfZLylfUTqmnAZFJ47bHSeEoN+dwFCQFgtHtsNTF0JkAtz4HmTNH+/IFCLjjlYI248o4ypdC/veUFyOwqIovlNOV5SmnEXjshh6fpf9Tpdi+ettg6cvUayWF94PC68Z/7hAKaXvUZhW/QD6u5U4tNK1yuPdO5RHUt6g5azopHHFnnmPbXp6gnKVfvAr+PSPMPcyuOBvqluvdK0gacXKAsqP7obji5gyIZ7/fnEH5//9M247ewbfOK5wTIqE1mI98tNiefmWVfzslV384d39rC9t5J5L5w0f5DgKWrGygFIj6Q+r5zEvN4lfvVnM2X/9lHsuncupM/w0P/sQ7hgkXy5ckENhWhw/WLOdrz/0Bd87dSq3njJ5TC43rV2X4aS2vRe7Ux4bC2Hvgdd+oATBTpgNV/9LWSD8/c6ikxRLUe6SwW1OO9TtgeotULUFDm9UFllQ3HPZC91Kk/sRn6HKGPNSY6HpIDx5gWLZ+cbrSixOMIhKgOnnKA9QLFUHP1AUpj2vwLYnB+NmJp+qLLzZiwK39HiRb2mEJy9VvvPr31Ase8EiMVtRUBZeo7isjmxTxla6Fj76HXz0W0X5m3SK2wJzGiSMPTayIDkSXrwOar6Ey55U4qiCRWTs0cpgyyE4uE4Z365/w9YnlLi03GWDCtPEeWPuWZSXGgubHlGUo0XXw3l/CUr/I10rSOGOQRqKE6dl8M4PT+S2f+3k/97Yyyvbq/jlBXNYXBDYXY8W06mTYiL4+5ULOWlqBv/7+h5Ov/djbj1lCjefOCngbtjhjkHyRQjBdSsLWVqYyg+e384NT2zhzFmZ3Hn+LHKHywQZBi3EIPkyPy+ZN75/PHe+uof71pXw+pdH+OUFszlxWmALabgTI7SEJ8X/qBidznp47utKqvTJdygxOiNZi/zFGqFYirIXKEG1ns86vAkOb1D+bvyH4qYCRYnxKEv5KyB9+pgWkKnJKG41JFz/WvCUo6FIKVTcOUtuUBSK6i2DCtMnv4ePfwdRSTDpRMX6MunkgDKscuIkcS9fqxz7mleCqxz5YrW5LYDL4JQ7lPissg8HFaY9Lyv7Zc4ZdMXlLlWUSD85rvw+xXV54f3BVY6GIqXA69zZlevTY1364P+UR1zGYOxSwUrF2uTnuTsxohjeuR2mnRM05Qh0riB53DRau5tNj4/in9cv4fWdNfzmzWIuffBzzpqdyfdPncqcHP/SP10aiK8aCiEEX1+axwnT0vnVG8Xc+/4BnttYya2nTObrS/P8tkpoIQZpKGZmJfLmD07gn5+V8bd1pZz6x4+5Ylket5w8hYlJ/hWU05J1zJuE6Aj+fPkCLl6Yw12v7eG6xzZx3OQ0fnTGNJb66XYLR3FWrVLZ5BPE3N2suGpaKuCK52DGucEVIH6CsvB5Fj97r2It8ChMJe/Dl88rr0UlQd5SxSWXt0yxTkUOXd/I+9K9suGvirvv2pchbXJwxzMSVtugO+6UnynfdfnHboXpAyXzDJQg97xlg8ph1vxj3I+e4d0R+aKS4n71vyF9SmjH40tsKsy5VHlIqcjlUZa+eADW/1WxnmXOVs6h57tIOjqxxDO24y27mLj3UVj2bfXcamPFGqEEbxeugtPvUhR7j6uxdK0SowdKcoJHoc9briiHXtZBj+s3gW4urfw/RVm/9JGgds7WtYLkkpoI9RgSIQQXzM/mtBkTeOiTMh5fX867e+o4eXoG1ywv4OTpGSMWKBuwIGm0lGdWUgz3X72Iq0sb+dP7B/h/r+7h/g8PctXyfK5YmseExJGVCa25obyJtFm45eQpXLggh79/UMJzGytZs/kwF8zP5poVBczPTRpRKZdSW1ZNXxQr5wk8s6GSBz86yGX/+IJlhalcs7KAs2ZnjqjkhquAqRapdFdizkqKBkefYjlqOghXv6haFk1ARERD/nLlAcqF2FymuOMOb1SKAJb+SnlNWGHiHMXlkTlbWYwmzDwq6Po0y1am1r4JJ/4kPOMZidhUmH2x8pBSUeLKP1ayrA5vGlSYrJGKkpS1wP13HlZpZ4VlL+f1vArLbh50C2kFIQbje47/oRKbdXiT+xxugB3PweZHlH0Tc7zGNp/o3gwS6eT3EQ8j06chzvhlOEcyNPETYP4VysPlgrpdyrV5eIPy12M9i4wfGBdZ87GkzMaCi7siniK2rwGueT4gi9pY0LWCJNGeK8OXuCgb/33GNG48voinPq/gqQ2HuOmpLUxMjObiRTmcPXsi84ZYcAeDtLU9vuOmpLNychqflTby8Cdl3Pv+Ae5bV8IpMybwtblZnDpzAonRx7oYFOVW22PLSY7ht5fM45aTp/CPjw/yyvZq/rW1illZiVywIJtz5kwcspS/S0qNjwyibFZuPL6Iq5bl8+zGQzz1xSF+8Px20uIiOX++MrYlhanHxM8NNFEOh9Aa41CzVyXmN25XFufLntCOMiGEYvVJmwwLrlK29bQMxjAd3qhYmPq9KianFBKRMYvbbVZWWz+mN3UG0Sf+NDzy+4sQkDFNeXhq3wy4Hzcq5+XL5weUioexQYSL5uh8Uk//3/DJ7S9RCe64ndOU506HYmHyjK3mS9j/FiC5CDg/SuASVsRFL4Ws/tKYsVgGlaDlNyvbWg8PXp9HdsCWx8HRQwJQEiWwCknL4h+Skrs46OLpWkHSqitjKJJiIvj+aVP5zsmTWVdcz5rNlTz8SRkPfnSQrKRoTpkxgeVFqayYlEZmYrRXNebwyu0PQghOmJrBCVMzqGjs4rlNlby24wjv760j0mph+aRUjpusKFJzshOxWS1KnI5GrWO+5KXG8uuL53L7OTP4z44jvLTlML97ex+/e3sfM7MSOX5KGisnp7G0MJWE6AjFOqZV85gPMZFWbjphEjesKuLT0kae31jJc5sqeeLzCtLjozhxWjorJynjy02JDWuVcK1xuLmb/LQ4KH5DyTI77geKRUPLxKTA1DOUByh38G2VSgHBuj1QvwdRu5tvWUupJZXY1Y8HvSZRUPB1P7pc0FIONTt49713aW5uIn3VbZwzjJtR01htg/Foy7+tbOvrhLrdbNvwIbt3bqU+7yx+HAIFIigk5ymPuauV5y4nNB6g+9A2nnv1darJ4I6z/l9IRPFLQRJCnA38FbAC/5RS/s7n9SjgKWAx0ARcLqWscL92B3Aj4AR+IKV8Vy3htRbo6w8RVgtnz5nI2XMm0trdz9riet7dU8vrO47w3MZKQEk9neh2UelFAfRQmB7Hz86dye1nz2D74Vbe3lXDxwcauOedfYBSoHFOTiJtPXbdLbIJ0RFcu6KAa1cUcLi5m3f31PL+3jqe/PwQj3xajtUimJ6ZQFe/Q3fnzWIRnDQtg5OmZdDZ5+DDffW8s6eWj/Y38PK2akCxqE1IVOI5tKT/CSG+D9yKMse8KaX8qXt70OYeUIpELpso4M3/VmqwnHanmocPDRaLEticUjgQM9VvdzLv/71Oclw0m7JnhVU81bBYBqxpL23O46P6Bl7KC3PckZpExUP+Ckrqsrlz2zxuzCkKt0TqYbHChJnYE6bwq38nk5Mcw10RobHtjPopQggrcD9wBlAFbBZCvCal3Ou1241Ai5RyihDiCuAe4HIhxCzgCmA2kA2sFUJMk1Kq0uFPq4G+/pIcG8nqxbmsXpyLw+miuKaDDWVNbK5oZnd129FF2nSGxSJYXJDC4oIUfgE0dPSxsbyJjWXN7KxqRQKF/jRS1Ch5qbHcdMIkbjphEr12J9sOtbChrInth1s5Ut3DJB13d4+PsnH+/GzOn5+NyyU5UN/BhoNNbD7Uwu7qNqIjLKq2nRkPQohTgAuB+VLKPiHEBPf2oM49bd122nrsXNr8qNL76aoX1clW0wBCQD8R5KYFN74jXHhWDCM1GPbgCVvwq0mtzvAs9aHse+iPGrYMKJVSlgEIIdagTEjeCtKFwP+6//8X8HehmAcuBNZIKfuAciFEqft4X6ghfEtX/0Csjt6xWS3MzU1ibm4S3zpRSaV1OF3+dZrWARkJUZw3L5vz5il9e1wu/bihRiM6wspxU9I5bko6MJjpZQQsFsGMiYnMmJjIN1Ypd6UaO3ffBX7nnmOQUta7twd17qls6mKVZRczjrwMK76ruDsMhhEb8HqIjrCQkaBecU2tMdb6dHoglNelP6tvDnDY63mVe9uQ+0gpHUAbkObne8fMS1urAq6/oyeMohwNhYYWWNURQujOfRgIGjt304AThBAbhRAfCyGWurcHde5xbX+KZyN/iz0hV6kCbSAsQmCzCCap1HdRa0TZrBSlxxvyNxoVoawZerZgD4fNIrAImJQRurFpIkhbCHEzcDNAfn6+3+974OpFpIyzt5SJiYm2EUKsBYYqKfxzlDksFVgBLAVeFEIEVM1wLPNP6pwzWN/dy6KzrlOqMBuICKuFZ29azszssbeF0DJ3nDuDfocr3GIEhbNmT+SZG5cPmV2rd2IjbTx70wrm5obu9+aPglQN5Hk9z3VvG2qfKiGEDUhCCdb2571IKR8GHgZYsmSJ3/6Jc+dm+buriYmJTpFSDluoRgjxXeBlqfg1NwkhXEA6fs497uMHPP/kTZpJ3qSZ/g1AhyxXoSG1VjGi8uAhOsLK8VPTwy1G0Fg5ObTXpT8+nM3AVCFEkRAiEiXw8TWffV4Drnf/vxr4wD1hvQZcIYSIEkIUAVOBTeqIbmJiYsJ/gFMAhBDTgEigEXPuMTExGSejWpCklA4hxPeAd1HS/B+TUu4RQtwNbJFSvgY8CjztDoRsRlGicO/3IkpAtwO4Va0sEhMTExPgMeAxIcRuoB+43n1zZs49JiYm40JoLeNmyZIlcsuWLeEWw8TEJIQIIbZKKZeMvmdwMecfE5OvFiPNPcZNkzIxMTExMTExGSOmgmRiYmJiYmJi4oOpIJmYmJiYmJiY+KC5GCQhRANwKIC3pKNkregNU+7QYsodWgKVu0BKmREsYfwlwPnnq3JutIIpd+jRq+yByD3s3KM5BSlQhBBbtBDcGSim3KHFlDu06FXuQNDrGE25Q4te5Qb9yq6W3KaLzcTExMTExMTEB1NBMjExMTExMTHxwQgK0sPhFmCMmHKHFlPu0KJXuQNBr2M05Q4tepUb9Cu7KnLrPgbJxMTExMTExERtjGBBMjExMTExMTFRFd0qSEKIs4UQ+4UQpUKI28Mtz3AIIfKEEB8KIfYKIfYIIf7LvT1VCPG+EKLE/Tcl3LIOhRDCKoTYLoR4w/28SAix0f29v+BuYKwphBDJQoh/CSH2CSGKhRArdfR9/8h9newWQjwvhIjW4ncuhHhMCFHv7oHm2TbkdywU7nPLv1MIsSh8kquDOf+EBnP+CR3m3HMsulSQhBBW4H7gHGAWcKUQYlZ4pRoWB/A/UspZwArgVrestwPrpJRTgXXu51rkv4Bir+f3AH+WUk4BWoAbwyLVyPwVeEdKOQOYjyK/5r9vIUQO8ANgiZRyDkpz6CvQ5nf+BHC2z7bhvuNzgKnux83AgyGSMSiY809IMeefEGDOPcMgpdTdA1gJvOv1/A7gjnDL5afsrwJnAPuBLPe2LGB/uGUbQtZc98V2KvAGIFCKb9mGOg9aeABJQDnu+Dqv7Xr4vnOAw0AqYHN/52dp9TsHCoHdo33HwEPAlUPtp8eHOf+ETFZz/gmd3ObcM8RDlxYkBk+mhyr3Nk0jhCgEFgIbgUwpZY37pVogM1xyjcBfgJ8CLvfzNKBVSulwP9fi914ENACPu03z/xRCxKGD71tKWQ38EagEaoA2YCva/849DPcd6/L3OgK6HI85/4QEXc4/5twzNHpVkHSHECIe+DfwQyllu/drUlFtNZVOKIQ4D6iXUm4NtywBYgMWAQ9KKRcCXfiYs7X4fQO4/eYXokyy2UAcx5qSdYFWv+OvKub8EzJ0Of+Yc8/Q6FVBqgbyvJ7nurdpEiFEBMrk9KyU8mX35johRJb79SygPlzyDcMq4AIhRAWwBsXM/VcgWQhhc++jxe+9CqiSUm50P/8XyoSl9e8b4HSgXErZIKW0Ay+jnAetf+cehvuOdfV79QNdjcecf0KKXucfc+4ZAr0qSJuBqe4I+0iUYLLXwizTkAghBPAoUCylvNfrpdeA693/X48SG6AZpJR3SClzpZSFKN/vB1LKq4EPgdXu3bQody1wWAgx3b3pNGAvGv++3VQCK4QQse7rxiO7pr9zL4b7jl8DrnNnlKwA2rzM4XrEnH+CjDn/hBxz7hmKcAdbjSNI61zgAHAQ+Hm45RlBzuNRzH07gR3ux7ko/vR1QAmwFkgNt6wjjOFk4A33/5OATUAp8BIQFW75hpB3AbDF/Z3/B0jRy/cN/BLYB+wGngaitPidA8+jxCrYUe6abxzuO0YJrr3f/VvdhZIpE/bvepzjN+ef0I3BnH9CI7c59/g8zEraJiYmJiYmJiY+6NXFZmJiYmJiYmISNEwFycTExMTExMTEB1NBMjExMTExMTHxwVSQTExMTExMTEx8MBUkExMTExMTExMfTAXJxMTExMTExMQHU0Ey8QshRJoQYof7USuEqHb/3ymEeCBIn/lDIcR1KhxnjRBiqhoymZiYhBZz7jEJF2YdJJOAEUL8L9AppfxjED/DBmwDFsnBZoljPdZJwDVSym+pIpyJiUlYMOcek1BiWpBMxoUQ4mQhxBvu//9XCPGkEOJTIcQhIcQlQojfCyF2CSHecfeEQgixWAjxsRBiqxDiXU8PHR9OBbZ5JighxEdCiD8LIbYIIYqFEEuFEC8LIUqEEL9y7xMnhHhTCPGlEGK3EOJy97E+BU736ilkYmKic8y5xyTYmAqSidpMRplgLgCeAT6UUs4FeoCvuSeqvwGrpZSLgceAXw9xnFWAbyfvfinlEuAfKL12bgXmAN8QQqShdJ8+IqWcL6WcA7wDIKV0oZTKn6/qSE1MTLSEOfeYqIqp1ZqozdtSSrsQYhdgxT1RoPTBKQSmo0ws7ys9EbGi9NXxJQso9tnmaQi6C9gj3U0HhRBlKB2bdwF/EkLcg9K76VOv99YD2Rw78ZmYmBgDc+4xURVTQTJRmz5Q7pyEEHY5GOTmQrneBMoEs3KU4/QA0UMd232sPq/tLsAmpTwghFiE0ozzV0KIdVLKu937RLuPaWJiYkzMucdEVUwXm0mo2Q9kCCFWAgghIoQQs4fYrxiYEsiBhRDZQLeU8hngD8Air5enoXSpNjEx+Wpizj0mAWFakExCipSyXwixGrhPCJGEcg3+Bdjjs+vbwNMBHn4u8AchhAuwA98FEEJkAj1SytrxyG5iYqJfzLnHJFDMNH8TzSKEeAX4qZSyZJzH+RHQLqV8VB3JTExMjIw595iA6WIz0Ta3owRMjpdW4EkVjmNiYvLVwJx7TEwLkomJiYmJiYmJL6YFycTExMTExMTEB1NBMjExMTExMTHxwVSQTExMTExMTEx8MBUkExMTExMTExMfTAXJxMTExMTExMSH/w8808LceL75dQAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_syn_model(AMPASparse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92903cb0",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We hope this tutorial will help your synapse models be defined efficiently. "
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "279.273px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/tutorial_math/tensors.ipynb b/docs/tutorial_math/array.ipynb
similarity index 93%
rename from docs/tutorial_math/tensors.ipynb
rename to docs/tutorial_math/array.ipynb
index bef8335d1..57aa97b84 100644
--- a/docs/tutorial_math/tensors.ipynb
+++ b/docs/tutorial_math/array.ipynb
@@ -9,7 +9,7 @@
     }
    },
    "source": [
-    "# Tensors"
+    "# Arrays"
    ]
   },
   {
@@ -35,7 +35,7 @@
    },
    "source": [
     "```{note}\n",
-    "If you have the basic knowledge about [NumPy](https://numpy.org/) (the ``tensor`` here is the same as the ``ndarray`` in NumPy), you can skip this section.\n",
+    "If you have the basic knowledge about [NumPy](https://numpy.org/) (the ``array`` here is the same as the ``ndarray`` in NumPy), you can skip this section.\n",
     "```"
    ]
   },
@@ -50,9 +50,9 @@
    "source": [
     "In this section, we are going to understand:\n",
     "\n",
-    "- What is a ``tensor``? \n",
-    "- How to create a ``tensor``?\n",
-    "- What operations are supported for a ``tensor``?"
+    "- What is a ``array``?\n",
+    "- How to create a ``array``?\n",
+    "- What operations are supported for a ``array``?"
    ]
   },
   {
@@ -80,7 +80,7 @@
     }
    },
    "source": [
-    "## What is ``tensor``?"
+    "## What is ``array``?"
    ]
   },
   {
@@ -92,7 +92,7 @@
     }
    },
    "source": [
-    "A tensor is a homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. The dimensions of an array are called **axes**."
+    "A array is a homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. The dimensions of an array are called **axes**."
    ]
   },
   {
@@ -138,15 +138,15 @@
     }
    },
    "source": [
-    "A tensor has several important attributes: \n",
+    "A array has several important attributes:\n",
     "\n",
-    "- **.ndim**: the number of axes (dimensions) of the tensor.\n",
+    "- **.ndim**: the number of axes (dimensions) of the array.\n",
     "\n",
-    "- **.shape**: the dimensions of the tensor. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, the shape will be `(n,m)`. The length of the shape tuple is therefore the number of axes, `ndim`.\n",
+    "- **.shape**: the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, the shape will be `(n,m)`. The length of the shape tuple is therefore the number of axes, `ndim`.\n",
     "\n",
-    "- **.size**: the total number of elements of the tensor. This is equal to the product of the elements of shape.\n",
+    "- **.size**: the total number of elements of the array. This is equal to the product of the elements of shape.\n",
     "\n",
-    "- **.dtype**: an object describing the type of the elements in the tensor. One can create or specify dtypes using standard Python types."
+    "- **.dtype**: an object describing the type of the elements in the array. One can create or specify dtypes using standard Python types."
    ]
   },
   {
@@ -262,7 +262,7 @@
     }
    },
    "source": [
-    "## How to create a ``tensor``?"
+    "## How to create a ``array``?"
    ]
   },
   {
@@ -274,7 +274,7 @@
     }
    },
    "source": [
-    "There are several ways to create a tensor. "
+    "There are several ways to create a array."
    ]
   },
   {
@@ -298,7 +298,7 @@
     }
    },
    "source": [
-    "The basic method is to convert Python sequences into tensors by ``bm.array()``. For example： "
+    "The basic method is to convert Python sequences into arrays by ``bm.array()``. For example："
    ]
   },
   {
@@ -361,7 +361,7 @@
     }
    },
    "source": [
-    "Often, the elements of an array are originally unknown, but its size is known. Therefore, you can use placeholder functions to create tensors, like:"
+    "Often, the elements of an array are originally unknown, but its size is known. Therefore, you can use placeholder functions to create arrays, like:"
    ]
   },
   {
@@ -976,12 +976,12 @@
     }
    },
    "source": [
-    "Moreover, there are many other methods we can use to create tensors, including: \n",
+    "Moreover, there are many other methods we can use to create arrays, including:\n",
     "\n",
     "- Conversion from other Python structures (i.e. lists and tuples)\n",
     "- Intrinsic NumPy array creation functions (e.g. ``arange``, ``ones``, ``zeros``, etc.)\n",
     "- Use of special library functions (e.g., ``random``)\n",
-    "- Replicating, joining, or mutating existing tensors\n",
+    "- Replicating, joining, or mutating existing arrays\n",
     "- Reading arrays from disk, either from standard or custom formats\n",
     "- Creating arrays from raw bytes through the use of strings or buffers\n",
     "\n",
@@ -997,7 +997,7 @@
     }
    },
    "source": [
-    "## Supported operations on ``tensor``"
+    "## Supported operations on ``array``"
    ]
   },
   {
@@ -1009,7 +1009,7 @@
     }
    },
    "source": [
-    "All the operations in BrainPy are based on tensors. Therefore it is necessary to know what operations supported in each tensor object."
+    "All the operations in BrainPy are based on arrays. Therefore it is necessary to know what operations supported in each array object."
    ]
   },
   {
@@ -1033,7 +1033,7 @@
     }
    },
    "source": [
-    "Arithmetic operators on tensors apply element-wise. Let's take \"+\", \"-\", \"\\*\", and \"/\" as examples."
+    "Arithmetic operators on arrays apply element-wise. Let's take \"+\", \"-\", \"\\*\", and \"/\" as examples."
    ]
   },
   {
@@ -1045,7 +1045,7 @@
     }
    },
    "source": [
-    "We first create two tensors:"
+    "We first create two arrays:"
    ]
   },
   {
@@ -1111,7 +1111,7 @@
     }
    },
    "source": [
-    "![](../_static/tensor_dataones.png)"
+    "![](../_static/array_dataones.png)"
    ]
   },
   {
@@ -1148,7 +1148,7 @@
     }
    },
    "source": [
-    "![](../_static/tensor_plus_ones.png)"
+    "![](../_static/array_plus_ones.png)"
    ]
   },
   {
@@ -1235,7 +1235,7 @@
     }
    },
    "source": [
-    "![](../_static/tensor_sub_mult_divide.png)"
+    "![](../_static/array_sub_mult_divide.png)"
    ]
   },
   {
@@ -1247,7 +1247,7 @@
     }
    },
    "source": [
-    "Aggregation functions can also be performed on tensors, like:\n",
+    "Aggregation functions can also be performed on arrays, like:\n",
     "\n",
     "- ``.min()``: to get the minimum element;\n",
     "- ``.max()``: to get the maximum element;\n",
@@ -1343,7 +1343,7 @@
     }
    },
    "source": [
-    "![](../_static/tensore_aggregation.png)"
+    "![](../_static/arraye_aggregation.png)"
    ]
   },
   {
@@ -1433,7 +1433,7 @@
     }
    },
    "source": [
-    "![](../_static/tensor_matrix_aggregation_row.png)"
+    "![](../_static/array_matrix_aggregation_row.png)"
    ]
   },
   {
@@ -1457,7 +1457,7 @@
     }
    },
    "source": [
-    "Tensor operations are usually done on pairs of arrays on an element-by-element basis. In the simplest case, the two tensors must have exactly the same shape, as in the following example:"
+    "array operations are usually done on pairs of arrays on an element-by-element basis. In the simplest case, the two arrays must have exactly the same shape, as in the following example:"
    ]
   },
   {
@@ -1497,7 +1497,7 @@
     }
    },
    "source": [
-    "However, the **broadcasting** rule may be relaxed when the shapes of the tensors meet certain constraints. The simplest broadcasting example occurs when a tensor and a scalar value are combined in an operation:"
+    "However, the **broadcasting** rule may be relaxed when the shapes of the arrays meet certain constraints. The simplest broadcasting example occurs when a array and a scalar value are combined in an operation:"
    ]
   },
   {
@@ -1606,7 +1606,7 @@
     }
    },
    "source": [
-    "Under certain constraints, the smaller tensor can be \"broadcast\" across the larger tensor so that they have compatible shapes. Broadcasting provides a means of vectorizing tensor operations so that looping occurs in C instead of Python. It does this without making needless copies of data and usually leads to efficient algorithm implementations. "
+    "Under certain constraints, the smaller array can be \"broadcast\" across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python. It does this without making needless copies of data and usually leads to efficient algorithm implementations."
    ]
   },
   {
@@ -1618,7 +1618,7 @@
     }
    },
    "source": [
-    "Generally, the dimensions of two tensors are compatible when\n",
+    "Generally, the dimensions of two arrays are compatible when\n",
     "\n",
     "- they are equal,\n",
     "\n",
@@ -1814,7 +1814,7 @@
     }
    },
    "source": [
-    "Tensors can be indexed, sliced, and iterated over, much like lists and other Python sequences. For examples:"
+    "arrays can be indexed, sliced, and iterated over, much like lists and other Python sequences. For examples:"
    ]
   },
   {
@@ -1990,7 +1990,7 @@
     }
    },
    "source": [
-    "For multi-dimensional tensors, these indices should be given in a tuple separated by commas. For example: "
+    "For multi-dimensional arrays, these indices should be given in a tuple separated by commas. For example:"
    ]
   },
   {
@@ -2244,7 +2244,7 @@
     }
    },
    "source": [
-    "Iterating over multidimensional tensors is done with respect to the first axis:"
+    "Iterating over multidimensional arrays is done with respect to the first axis:"
    ]
   },
   {
@@ -2307,7 +2307,7 @@
     }
    },
    "source": [
-    "Tensors support many other functions, including\n",
+    "arrays support many other functions, including\n",
     "\n",
     "- [mathematical functions](https://numpy.org/doc/stable/reference/routines.math.html)\n",
     "- [logical functions](https://numpy.org/doc/stable/reference/routines.logic.html)\n",
diff --git a/docs/tutorial_math/tensors_and_variables.ipynb b/docs/tutorial_math/arrays_and_variables.ipynb
similarity index 83%
rename from docs/tutorial_math/tensors_and_variables.ipynb
rename to docs/tutorial_math/arrays_and_variables.ipynb
index 48f68ea6f..54c6a1707 100644
--- a/docs/tutorial_math/tensors_and_variables.ipynb
+++ b/docs/tutorial_math/arrays_and_variables.ipynb
@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e32b5d37",
+   "id": "677f3629",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
-    "# Tensors and Variables"
+    "# Arrays and Variables"
    ]
   },
   {
@@ -34,7 +34,7 @@
     }
    },
    "source": [
-    "In this section ,we will briefly introduce two basic and important data structures: tensors and variables. They form the foundation for mathematical operations of brain dynamics programming (BDP) in BrainPy."
+    "In this section ,we will briefly introduce two basic and important data structures: arrays and variables. They form the foundation for mathematical operations of brain dynamics programming (BDP) in BrainPy."
    ]
   },
   {
@@ -46,7 +46,7 @@
     }
    },
    "source": [
-    "## Tensors"
+    "## Arrays"
    ]
   },
   {
@@ -70,7 +70,7 @@
     }
    },
    "source": [
-    "A tensor is a data structure that organizes algebraic objects in a multidimentional vector space. Simply speaking, in BrainPy, a tensor is a multidimensional array that contains the same type of data, most commonly of the numeric or boolean type. \n",
+    "An array is a data structure that organizes algebraic objects in a multidimentional vector space. Simply speaking, in BrainPy, an array is a multidimensional array that contains the same type of data, most commonly of the numeric or boolean type.\n",
     "\n",
     "The dimensions of an array are called **axes**. In the following illustration, the 1-D array (`[7, 2, 9, 10]`) only has one axis. There are 4 elements in this axis, so the shape of the array is `(4,)`. \n",
     "\n",
@@ -100,7 +100,7 @@
     }
    },
    "source": [
-    "To enable tensor operations, users should import the ``brainpy.math`` module:"
+    "To enable array operations, users should import the ``brainpy.math`` module:"
    ]
   },
   {
@@ -161,7 +161,7 @@
     }
    },
    "source": [
-    "Here we create a 3-dimensional tensor with the shape of (2, 3, 4) and the type of `int32`. Tensors created by ``brainpy.math`` will be stored in ``JaxArray``, for their future operations will be accelerated by just-in-time (JIT) compilation."
+    "Here we create a 3-dimensional array with the shape of (2, 3, 4) and the type of `int32`. Arrays created by ``brainpy.math`` will be stored in ``JaxArray``, for their future operations will be accelerated by just-in-time (JIT) compilation."
    ]
   },
   {
@@ -173,15 +173,15 @@
     }
    },
    "source": [
-    "A tensor has several important attributes: \n",
+    "A array has several important attributes:\n",
     "\n",
-    "- **.ndim**: the number of axes (dimensions) of the tensor.\n",
+    "- **.ndim**: the number of axes (dimensions) of the array.\n",
     "\n",
-    "- **.shape**: the dimensions of the tensor. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, the shape will be `(n,m)`. The length of the shape tuple is therefore the number of axes, `ndim`.\n",
+    "- **.shape**: the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, the shape will be `(n,m)`. The length of the shape tuple is therefore the number of axes, `ndim`.\n",
     "\n",
-    "- **.size**: the total number of elements of the tensor. This is equal to the product of the elements of shape.\n",
+    "- **.size**: the total number of elements of the array. This is equal to the product of the elements of shape.\n",
     "\n",
-    "- **.dtype**: an object describing the type of the elements in the tensor. One can create or specify dtypes using standard Python types."
+    "- **.dtype**: an object describing the type of the elements in the array. One can create or specify dtypes using standard Python types."
    ]
   },
   {
@@ -221,7 +221,7 @@
     }
    },
    "source": [
-    "Below we will give a few examples of tensor operations that are commonly used in brain dynamics programming. For more details about tensor operations, please refer to the [tensor tutorial](tensors.ipynb)."
+    "Below we will give a few examples of array operations that are commonly used in brain dynamics programming. For more details about array operations, please refer to the [array tutorial](array.ipynb)."
    ]
   },
   {
@@ -233,7 +233,7 @@
     }
    },
    "source": [
-    "### Creating a tensor"
+    "### Creating a array"
    ]
   },
   {
@@ -264,7 +264,7 @@
     }
    },
    "source": [
-    "### Tensor operations"
+    "### Array operations"
    ]
   },
   {
@@ -381,7 +381,7 @@
     }
    },
    "source": [
-    "In BrainPy, tensors can be used to store some parameters related to dynamical models. For example, if we define a group of Integrate-and-Fire (LIF) neurons and wish to assign each neuron with a different time constant $\\tau$, then we can generate a tensor containing an array of time constants."
+    "In BrainPy, arrays can be used to store some parameters related to dynamical models. For example, if we define a group of Integrate-and-Fire (LIF) neurons and wish to assign each neuron with a different time constant $\\tau$, then we can generate an array containing an array of time constants."
    ]
   },
   {
@@ -445,7 +445,7 @@
     }
    },
    "source": [
-    "We have talked about the definition, operations, and application of tensors in BrainPy. There are some situations, however, where tensors are not applicable. Due to [JIT compilation](jit_compilation.ipynb), once a tensor is given to the JIT compiler, the values inside the tensor cannot be changed. This gives rise to severe limitations, because some properties of the dynamical system, such as the membrane potential, dynamically changes over time. Therefore, we need a new data structure to store such dynamic variables, and that is ``brainpy.math.Variable``."
+    "We have talked about the definition, operations, and application of arrays in BrainPy. There are some situations, however, where arrays are not applicable. Due to [JIT compilation](jit_compilation.ipynb), once a array is given to the JIT compiler, the values inside the array cannot be changed. This gives rise to severe limitations, because some properties of the dynamical system, such as the membrane potential, dynamically changes over time. Therefore, we need a new data structure to store such dynamic variables, and that is ``brainpy.math.Variable``."
    ]
   },
   {
@@ -469,7 +469,7 @@
     }
    },
    "source": [
-    "``brainpy.math.Variable`` is a pointer referring to a tensor. The tensor is stored as its value. The data in a Variable can be changed during JIT compilation. **If a tensor is labeled as a Variable, it means that it is a dynamical variable that changes over time.**"
+    "``brainpy.math.Variable`` is a pointer referring to a array. The array is stored as its value. The data in a Variable can be changed during JIT compilation. **If a array is labeled as a Variable, it means that it is a dynamical variable that changes over time.**"
    ]
   },
   {
@@ -481,7 +481,7 @@
     }
    },
    "source": [
-    "To create or change a tensor into a variable, users just need to wrap the tensor into ``brainpy.math.Variable``:"
+    "To create or change a array into a variable, users just need to wrap the array into ``brainpy.math.Variable``:"
    ]
   },
   {
@@ -532,7 +532,7 @@
    },
    "source": [
     "```{note}\n",
-    "Tensors that are not marked as Variables will be JIT compiled as static data. In [JIT compilation](jit_compilation.ipynb), it is shown that modifications of tensors are invalid in a JIT-compilation environment.\n",
+    "Arrays that are not marked as Variables will be JIT compiled as static data. In [JIT compilation](jit_compilation.ipynb), it is shown that modifications of arrays are invalid in a JIT-compilation environment.\n",
     "```"
    ]
   },
@@ -582,7 +582,7 @@
     }
    },
    "source": [
-    "Since the data inside a Variable is a tensor, common operations on tensors can be directly grafted to Variables."
+    "Since the data inside a Variable is a array, common operations on arrays can be directly grafted to Variables."
    ]
   },
   {
@@ -606,7 +606,7 @@
     }
    },
    "source": [
-    "Though the operations are the same, there are some requirements for updating a Variable. If we directly change a Variable, The returning data will become a tensor but not a Variable."
+    "Though the operations are the same, there are some requirements for updating a Variable. If we directly change a Variable, The returning data will become a array but not a Variable."
    ]
   },
   {
@@ -901,7 +901,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorial_math/control_flows.ipynb b/docs/tutorial_math/control_flows.ipynb
index 909726b8c..f51c81841 100644
--- a/docs/tutorial_math/control_flows.ipynb
+++ b/docs/tutorial_math/control_flows.ipynb
@@ -152,7 +152,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "In the above example, the target *statement* in ``if (statement)`` syntax relies on a scalar, which is not an instance of [brainpy.math.Variable](./tensors_and_variables.ipynb). In this case, the conditional statements can be arbitrarily complex. You can write your models with normal Python codes. These models will work very well with [JIT compilation](./jit_compilation.ipynb)."
+    "In the above example, the target *statement* in ``if (statement)`` syntax relies on a scalar, which is not an instance of [brainpy.math.Variable](./arrays_and_variables.ipynb). In this case, the conditional statements can be arbitrarily complex. You can write your models with normal Python codes. These models will work very well with [JIT compilation](./jit_compilation.ipynb)."
    ],
    "metadata": {
     "collapsed": false,
@@ -252,7 +252,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "However, if the `statement` target in a ``if ... else ...`` syntax relies on instances of [brainpy.math.Variable](./tensors_and_variables.ipynb), writing Pythonic control flows will cause errors when using JIT compilation."
+    "However, if the `statement` target in a ``if ... else ...`` syntax relies on instances of [brainpy.math.Variable](./arrays_and_variables.ipynb), writing Pythonic control flows will cause errors when using JIT compilation."
    ],
    "metadata": {
     "collapsed": false,
@@ -317,7 +317,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "To perform conditional statement on [Variable](./tensors_and_variables.ipynb) instances, we need structural control flow syntax. Specifically, BrainPy provides several options (based on JAX):\n",
+    "To perform conditional statement on [Variable](./arrays_and_variables.ipynb) instances, we need structural control flow syntax. Specifically, BrainPy provides several options (based on JAX):\n",
     "\n",
     "- [brainpy.math.where](https://numpy.org/doc/stable/reference/generated/numpy.where.html): return element-wise conditional comparison results.\n",
     "- [brainpy.math.ifelse](../apis/auto/math/generated/brainpy.math.controls.ifelse.rst): Conditional statements of `if-else`, or `if-elif-else`, ... for a scalar-typed value."
diff --git a/docs/tutorial_math/index.rst b/docs/tutorial_math/index.rst
index 148fa3775..0b87a8ea6 100644
--- a/docs/tutorial_math/index.rst
+++ b/docs/tutorial_math/index.rst
@@ -5,6 +5,6 @@ Math Basics
    :maxdepth: 1
 
    overview
-   tensors_and_variables
+   arrays_and_variables
    jit_compilation
    control_flows
diff --git a/docs/tutorial_math/jit_compilation.ipynb b/docs/tutorial_math/jit_compilation.ipynb
index 785a00e01..1b21d207e 100644
--- a/docs/tutorial_math/jit_compilation.ipynb
+++ b/docs/tutorial_math/jit_compilation.ipynb
@@ -185,9 +185,9 @@
     "\n",
     "1. The class object must be a subclass of [brainpy.Base](../tutorial_math/base.ipynb).\n",
     "\n",
-    "2. Dynamically changed variables must be labeled as [brainpy.math.Variable](tensors_and_variables.ipynb).\n",
+    "2. Dynamically changed variables must be labeled as [brainpy.math.Variable](arrays_and_variables.ipynb).\n",
     "\n",
-    "3. Variable updating  must be accomplished by [in-place operations](tensors_and_variables.ipynb).\n"
+    "3. Variable updating must be accomplished by [in-place operations](arrays_and_variables.ipynb).\n"
    ]
   },
   {
diff --git a/docs/tutorial_math/overview.ipynb b/docs/tutorial_math/overview.ipynb
index 3e7817d23..5995e0a1f 100644
--- a/docs/tutorial_math/overview.ipynb
+++ b/docs/tutorial_math/overview.ipynb
@@ -209,7 +209,7 @@
     }
    },
    "source": [
-    "For more details, please see the [Tensors](./tensors.ipynb) tutorial."
+    "For more details, please see the [Arrays](./array.ipynb) tutorial."
    ]
   },
   {
@@ -358,7 +358,7 @@
     }
    },
    "source": [
-    "For more details, please see the [Tensors](./tensors.ipynb) tutorial."
+    "For more details, please see the [Arrays](./array.ipynb) tutorial."
    ]
   },
   {
diff --git a/docs/tutorial_simulation/dynamical_systems.ipynb b/docs/tutorial_simulation/dynamical_systems.ipynb
deleted file mode 100644
index d1bda66dd..000000000
--- a/docs/tutorial_simulation/dynamical_systems.ipynb
+++ /dev/null
@@ -1,808 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Building General Dynamical Systems"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](mailto:adaduo@outlook.com)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "The previous sections have shown how to build neuron models, synapse models, and network models. In fact, these brain objects all inherit the base class [brainpy.dyn.DynamicalSystem](../apis/auto/dyn/generated/brainpy.dyn.base.DynamicalSystem.rst), ``brainpy.dyn.DynamicalSystem`` is the universal language to define dynamical models in BrainPy.\n",
-    "\n",
-    "To begin with, let's make a rief summary of previous dynamic models and give the definition of a dynamical system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-25T03:02:48.939126Z",
-     "start_time": "2021-03-25T03:02:47.073698Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import brainpy as bp\n",
-    "import brainpy.math as bm\n",
-    "\n",
-    "bm.set_platform('cpu')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## What is a dynamical system?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Looking back to the neuron and synapse models defined in the previous sections, they share a common feature that **they all contain some variables that change over time**. Because of these variables, the models become 'dynamic' and behave differently at different times.\n",
-    "\n",
-    "Actually, a *dynamical system* is defined as a system with time-dependent states. These time-dependent states are displayed as variables in the previous models.\n",
-    "\n",
-    "Mathematically, the change of a state $X$ can be expressed as\n",
-    "\n",
-    "$$\n",
-    "\\dot{X} = f(X, t)\n",
-    "$$\n",
-    "\n",
-    "where $X$ is the state of the system, $t$ is the time, and $f$ is a function describing the time dependence of the state. \n",
-    "\n",
-    "Alternatively, the evolution of the system over time can be given by\n",
-    "\n",
-    "$$\n",
-    "X(t+dt) = F\\left(X(t), t, dt\\right)\n",
-    "$$\n",
-    "\n",
-    "where $dt$ is the time step and $F$ is the evolution rule to update the system's state."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Customizing your dynamical systems\n",
-    "\n",
-    "According to the mathematical expression of a dynamical system, any subclass of ``brainpy.dyn.DynamicalSystem`` must implement an updating rule in the ``update(self, t, dt)`` function.\n",
-    "\n",
-    "To define a dynamical system, the following requirements should be satisfied:\n",
-    "- Inherit from `brainpy.dyn.DynamicalSystem`.\n",
-    "- Implement the `update(self, t, dt)` function.\n",
-    "- When defining variables, they should be declared as `brainpy.math.Variable`.\n",
-    "- When updating the variables, it should be realized by [in-place operations](./tutorial_basics/tensors_and_variables.ipynb).\n",
-    "\n",
-    "Below is a simple example of a dynamical system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class FitzHughNagumoModel(bp.dyn.DynamicalSystem):\n",
-    "    def __init__(self, a=0.8, b=0.7, tau=12.5, **kwargs):\n",
-    "        super(FitzHughNagumoModel, self).__init__(**kwargs)\n",
-    "        \n",
-    "        # parameters\n",
-    "        self.a = a\n",
-    "        self.b = b\n",
-    "        self.tau = tau\n",
-    "        \n",
-    "        # variables should be packed by brainpy.math.Variable\n",
-    "        self.v = bm.Variable([0.])\n",
-    "        self.w = bm.Variable([0.])\n",
-    "        self.I = bm.Variable([0.])\n",
-    "        \n",
-    "    def update(self, _t, _dt):\n",
-    "        # _t : the current time, the system keyword \n",
-    "        # _dt : the time step, the system keyword \n",
-    "        \n",
-    "        # in-place update\n",
-    "        self.w += (self.v + self.a - self.b * self.w) / self.tau * _dt\n",
-    "        self.v += (self.v - self.v ** 3 / 3 - self.w + self.I) * _dt\n",
-    "        self.I[:] = 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Here, we have defined a dynamical system called [FitzHugh–Nagumo neuron model](https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model), whose dynamics is given by: \n",
-    "\n",
-    "$$\n",
-    "{\\dot {v}}=v-{\\frac {v^{3}}{3}}-w+I, \\\\\n",
-    "\\tau {\\dot {w}}=v+a-bw.\n",
-    "$$\n",
-    "\n",
-    "By using the [Euler method](../apis/integrators/generated/brainpy.integrators.ode.explicit_rk.Euler.rst), this system can be updated by the following rule:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "v(t+dt) &= v(t) + [v(t)-{v(t)^{3}/3}-w(t)+RI] * dt, \\\\\n",
-    "w(t + dt) &= w(t) + [v(t) + a - b w(t)] * dt.\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Advantages of using `brainpy.dyn.DynamicalSystem`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "There are several advantages of defining a dynamical system as `brainpy.dyn.DynamicalSystem`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 1. A systematic naming system. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "First, every instance of ``DynamicalSystem`` has its unique name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [],
-   "source": [
-    "fhn = FitzHughNagumoModel()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'FitzHughNagumoModel1'"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fhn.name  # name for \"fhn\" instance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Every instance has its unique name:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FitzHughNagumoModel2\n",
-      "FitzHughNagumoModel3\n",
-      "FitzHughNagumoModel4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _ in range(3):\n",
-    "    print(FitzHughNagumoModel().name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Users can also specify the name of a dynamic system:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'X'"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fhn2 = FitzHughNagumoModel(name='X')\n",
-    "\n",
-    "fhn2.name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In BrainPy, each object should have a unique name. However, we detect that <__main__.FitzHughNagumoModel object at 0x000001F75163C250> has a used name \"X\". \n",
-      "If you try to run multiple trials, you may need \n",
-      "\n",
-      ">>> brainpy.base.clear_name_cache() \n",
-      "\n",
-      "to clear all cached names. \n"
-     ]
-    }
-   ],
-   "source": [
-    "# same name will cause error\n",
-    "\n",
-    "try:\n",
-    "    FitzHughNagumoModel(name='X')\n",
-    "except bp.errors.UniqueNameError as e:\n",
-    "    print(e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Second, variables, children nodes, etc. inside an instance can be easily accessed by their *absolute* or *relative* path. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'X.I': Variable([0.], dtype=float32),\n 'X.v': Variable([0.], dtype=float32),\n 'X.w': Variable([0.], dtype=float32)}"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# All variables can be acessed by \n",
-    "# 1). the absolute path\n",
-    "\n",
-    "fhn2.vars()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'I': Variable([0.], dtype=float32),\n 'v': Variable([0.], dtype=float32),\n 'w': Variable([0.], dtype=float32)}"
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2). or, the relative path\n",
-    "\n",
-    "fhn2.vars(method='relative')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 2. Convenient operations for simulation and analysis.\n",
-    "Brainpy provides different runners for dynamics simulation and analyzers for dynamics analysis, both of which require the dynamic model to be `Brainpy.dyn.DynamicalSystem`. For example, dynamic models can be packed by a runner for simulation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "31ab07d6dd334d1385ac2300e44222d1"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5))\n",
-    "runner(duration=100)\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Please see [Runners](../tutorial_toolbox/runners.ipynb) to know more about the operations in runners."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 3. Efficient computation.\n",
-    "\n",
-    "``brainpy.dyn.DynamicalSystem`` is a subclass of [brainpy.Base](../apis/generated/brainpy.base.Base.rst), and therefore, any instance of ``brainpy.dyn.DynamicalSystem`` can be complied [just-in-time](../tutorial_basics/jit_compilation.ipynb) into efficient machine codes targeting on CPUs, GPUs, and TPUs. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "4940699c4e964051ba5296e32c8fc91a"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5), jit=True)\n",
-    "runner(duration=100)\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 4. Support composable programming.\n",
-    "Instances of ``brainpy.dyn.DynamicalSystem`` can be combined at will. The combined system is also a `brainpy.dyn.DynamicalSystem` and enjoys all the properties, methods, and interfaces provided by `brainpy.dyn.DynamicalSystem`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "For example, if the instances are wrapped into a container, i.e. `brainpy.dyn.Network`, variables and nodes can also be accessed by their absolute or relative path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "fhn_net = bp.dyn.Network(f1=fhn, f2=fhn2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'FitzHughNagumoModel1.I': Variable([0.], dtype=float32),\n 'FitzHughNagumoModel1.v': Variable([0.], dtype=float32),\n 'FitzHughNagumoModel1.w': Variable([0.], dtype=float32),\n 'X.I': Variable([0.], dtype=float32),\n 'X.v': Variable([1.492591], dtype=float32),\n 'X.w': Variable([1.9365357], dtype=float32)}"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# absolute access of variables\n",
-    "\n",
-    "fhn_net.vars()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'f1.I': Variable([0.], dtype=float32),\n 'f1.v': Variable([0.], dtype=float32),\n 'f1.w': Variable([0.], dtype=float32),\n 'f2.I': Variable([0.], dtype=float32),\n 'f2.v': Variable([1.492591], dtype=float32),\n 'f2.w': Variable([1.9365357], dtype=float32)}"
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# relative access of variables\n",
-    "\n",
-    "fhn_net.vars(method='relative')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'FitzHughNagumoModel1': <__main__.FitzHughNagumoModel at 0x1f7515a74c0>,\n 'X': <__main__.FitzHughNagumoModel at 0x1f75164bd90>,\n 'Network0': <brainpy.dyn.base.Network at 0x1f7529e70d0>}"
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# absolute access of nodes\n",
-    "\n",
-    "fhn_net.nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'': <brainpy.dyn.base.Network at 0x1f7529e70d0>,\n 'f1': <__main__.FitzHughNagumoModel at 0x1f7515a74c0>,\n 'f2': <__main__.FitzHughNagumoModel at 0x1f75164bd90>}"
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# relative access of nodes\n",
-    "\n",
-    "fhn_net.nodes(method='relative')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "2ccd3d943278438e861e5c270b08cc37"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(fhn_net,\n",
-    "                         monitors=['f1.v', 'X.v'], \n",
-    "                         inputs=[('f1.I', 1.5),   # relative access to variable \"I\" in 'fhn1'\n",
-    "                                 ('X.I', 1.0),])  # absolute access to variable \"I\" in 'fhn2'\n",
-    "runner(duration=100)\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon['f1.v'], legend='fhn1.v', show=False)\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon['X.v'], legend='fhn2.v', show=True)"
-   ]
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "jupytext": {
-   "encoding": "# -*- coding: utf-8 -*-"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
-   "autoclose": false,
-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 1,
-   "hotkeys": {
-    "equation": "Ctrl-E",
-    "itemize": "Ctrl-I"
-   },
-   "labels_anchors": false,
-   "latex_user_defs": false,
-   "report_style_numbering": false,
-   "user_envs_cfg": false
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "411px",
-    "width": "316px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "243.068px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
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-   "window_display": false
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- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/docs/tutorial_simulation/index.rst b/docs/tutorial_simulation/index.rst
index 5cfa6b661..26ba6e508 100644
--- a/docs/tutorial_simulation/index.rst
+++ b/docs/tutorial_simulation/index.rst
@@ -4,8 +4,5 @@ Model Simulation
 .. toctree::
    :maxdepth: 1
 
-   overview_of_dynamic_model
-   neuron_models
-   synapse_models
-   network_models
-   dynamical_systems
+   simulation_dsrunner
+   parallel_computing
diff --git a/docs/tutorial_simulation/network_models.ipynb b/docs/tutorial_simulation/network_models.ipynb
deleted file mode 100644
index d830cd5c4..000000000
--- a/docs/tutorial_simulation/network_models.ipynb
+++ /dev/null
@@ -1,533 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a449066c",
-   "metadata": {},
-   "source": [
-    "# Building Network Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f27e704",
-   "metadata": {},
-   "source": [
-    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](https://github.com/chaoming0625)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1daa966d",
-   "metadata": {},
-   "source": [
-    "In previous sections, it has been illustrated how to define neuron models by `brainpy.dyn.NeuGroup` and synapse models by `brainpy.dyn.TwoEndConn`. This section will introduce `brainpy.dyn.Network`, which is the base class used to build network models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa2b708a",
-   "metadata": {},
-   "source": [
-    "In essence, [brainpy.dyn.Network](../apis/auto/building/generated/brainpy.dyn.Network.rst) is a container, whose function is to compose the individual elements. It is a subclass of a more general class: [brainpy.dyn.Container](../apis/auto/building/generated/brainpy.dyn.Container.rst). \n",
-    "\n",
-    "In below, we take an excitation-inhibition (E-I) balanced network model as an example to illustrate how to compose the [LIF neurons](./neuron_models.ipynb) and [Exponential synapses](./synapse_models.ipynb) defined in previous tutorials to build a network. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "49c0646a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import brainpy as bp\n",
-    "\n",
-    "bp.math.set_platform('cpu')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e363c68a",
-   "metadata": {},
-   "source": [
-    "## Excitation-Inhibition (E-I) Balanced Network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34345d13",
-   "metadata": {},
-   "source": [
-    "The E-I balanced network was first proposed to explain the irregular firing patterns of cortical neurons and comfirmed by experimental data. The network [1] we are going to implement consists of excitatory (E) neurons and inhibitory (I) neurons, the ratio of which is about 4 : 1. The biggest difference between excitatory and inhibitory neurons is the reversal potential - the reversal potential of inhibitory neurons is much lower than that of excitatory neurons. Besides, the membrane time constant of inhibitory neurons is longer than that of excitatory neurons, which indicates that inhibitory neurons have slower dynamics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eccd498d",
-   "metadata": {},
-   "source": [
-    "[1] Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007), Simulation of networks of spiking neurons: a review of tools and strategies., J. Comput. Neurosci., 23, 3, 349–98."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b3be5a19",
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# BrianPy has some built-in conanical neuron and synapse models\n",
-    "\n",
-    "LIF = bp.dyn.neurons.LIF\n",
-    "ExpCOBA = bp.dyn.synapses.ExpCOBA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aae1bdd0",
-   "metadata": {},
-   "source": [
-    "## Two ways to define network models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3c63a6d",
-   "metadata": {},
-   "source": [
-    "There are several ways to define a Network model. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abcd15a8",
-   "metadata": {},
-   "source": [
-    "### 1. Defining a network as a class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9230ab4a",
-   "metadata": {},
-   "source": [
-    "The first way to define a network model is like follows. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e2213320",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EINet(bp.dyn.Network):\n",
-    "  def __init__(self, num_exc, num_inh, method='exp_auto', **kwargs):\n",
-    "    super(EINet, self).__init__(**kwargs)\n",
-    "\n",
-    "    # neurons\n",
-    "    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
-    "    E = LIF(num_exc, **pars, method=method)\n",
-    "    I = LIF(num_inh, **pars, method=method)\n",
-    "    E.V.value = bp.math.random.randn(num_exc) * 2 - 55.\n",
-    "    I.V.value = bp.math.random.randn(num_inh) * 2 - 55.\n",
-    "\n",
-    "    # synapses\n",
-    "    w_e = 0.6  # excitatory synaptic weight\n",
-    "    w_i = 6.7  # inhibitory synaptic weight\n",
-    "    E_pars = dict(E=0., g_max=w_e, tau=5.)\n",
-    "    I_pars = dict(E=-80., g_max=w_i, tau=10.)\n",
-    "    \n",
-    "    # Neurons connect to each other randomly with a connection probability of 2%\n",
-    "    self.E2E = ExpCOBA(E, E, bp.conn.FixedProb(prob=0.02), **E_pars, method=method)\n",
-    "    self.E2I = ExpCOBA(E, I, bp.conn.FixedProb(prob=0.02), **E_pars, method=method)\n",
-    "    self.I2E = ExpCOBA(I, E, bp.conn.FixedProb(prob=0.02), **I_pars, method=method)\n",
-    "    self.I2I = ExpCOBA(I, I, bp.conn.FixedProb(prob=0.02), **I_pars, method=method)\n",
-    "\n",
-    "    self.E = E\n",
-    "    self.I = I"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99233e81",
-   "metadata": {},
-   "source": [
-    "In an instance of ``brainpy.dyn.Network``,  all ``self.`` accessed elements can be gathered by the ``.child_ds()`` function automatically. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c1d98910",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ExpCOBA0': <brainpy.dyn.synapses.abstract_models.ExpCOBA at 0x1076db580>,\n",
-       " 'ExpCOBA1': <brainpy.dyn.synapses.abstract_models.ExpCOBA at 0x1584c3d30>,\n",
-       " 'ExpCOBA2': <brainpy.dyn.synapses.abstract_models.ExpCOBA at 0x158496a00>,\n",
-       " 'ExpCOBA3': <brainpy.dyn.synapses.abstract_models.ExpCOBA at 0x15880ce80>,\n",
-       " 'LIF0': <brainpy.dyn.neurons.IF_models.LIF at 0x1583fd100>,\n",
-       " 'LIF1': <brainpy.dyn.neurons.IF_models.LIF at 0x1583f6d30>,\n",
-       " 'ConstantDelay0': <brainpy.dyn.base.ConstantDelay at 0x1584ba1c0>,\n",
-       " 'ConstantDelay1': <brainpy.dyn.base.ConstantDelay at 0x1584c3d00>,\n",
-       " 'ConstantDelay2': <brainpy.dyn.base.ConstantDelay at 0x158496370>,\n",
-       " 'ConstantDelay3': <brainpy.dyn.base.ConstantDelay at 0x15880c340>}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "EINet(8, 2).child_ds()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97b6ce36",
-   "metadata": {},
-   "source": [
-    "Note in the above ``EINet``, we do not define the ``update()`` function. This is because any subclass of ``brainpy.dyn.Network`` has a default update function, in which it automatically gathers the elements defined in this network and sequentially runs the update function of each element. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f677301f",
-   "metadata": {},
-   "source": [
-    "If you have some special operations in your network, you can override the update function by yourself. Here is a simple example. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f45275b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ExampleToOverrideUpdate(EINet):\n",
-    "    def update(self, _t, _dt):\n",
-    "        for node in self.child_ds().values():\n",
-    "            node.update(_t, _dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "550ac98b",
-   "metadata": {},
-   "source": [
-    "Let's try to simulate our defined `EINet` model. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a74c5b2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b35fb68a92e842c1ab7ec15f2415ee1a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Used time 0.35350608825683594 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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FPYV48XWeTRkuhN3mbbkYYaF1lEihAnYhLy8vm1NPPTV4YHaawgk2NGP8LuGALrJtmT5TQV3S9vUUjPHv6Mto6vjL8eQmK7FOnzYisHXWmG1Uzm9ZryrPpYun6pqur4w6mRox/SpPQSOPXZBIoQJ2Iy2siK1bt5prr71WpWOPRRDacmPJKx4e3xioLWf//v3r4z5j88K6dPRd5Pha8K4hszrE8nZdykl73ahpQt9RhkQKFbAbqT0ldWFhwVumjViV3GRxacjzQVMH5vriWJ1ebau/huoZE2MicE2vSTttTSt8jAjxRLte44pECi1YWVkxj3vc49YHSX1R5RJrexzascUuIY66e6p06Mtd75JOW1kNGf6L7VlqLpHQVdc6A0H7GQkllbawmm94R8tADDW0bAxCCsATgQ9Zx5eBlwFbgOvJNtm5HjjFumcfcJRsk51z29LQIgXbKg0p6BjegUtH5jozQiO9qmuG7ExDQjQhsrQR27PU6EjKMl11rSMm7c4yNPzUpE9XXe3rtckvdFaeMSPwFIDjgC8ApwEH2Lgd56vy72eycTvOO+hpO04fa9lFTgzdqtD0UMQYwOrDIwq5bwyyxpBOLLla5btZPAVtrK6G79sxBlL4MeB9+fcjwLb8+zbgiLnfS9hn3XMI2NkkNxYpaL6E1geaHorY1q+2taeh51hlxZTpg7G8VzOkp9mE2B17LAJzwRhI4fXAS/Pv95b+uyf/vBy4yDp/JXBhk1zt8FHxkE4bKTQh9gMXY4mGmA+MtsXno2vdPX13jlprLhkTRnRDk2RduYfopRmqjNEuBiUF4ARgDXikaSaFKypI4YIKeRcDh4HD8/PzwYVjx+S13VrX9DXTih1OKf/XdQCzSzgsRiehLdtFnmv5u+qm1WaSp5ChrtxD9HIZC3SVHeN5GJoUngdcZ/0eVfio6NR27txpFhYW1JeybkNohZcbV9UAV5eO25bnMvDWNoDZpJ9rnjTRBwmXUZXnEPKOMYjZJf3Y6EOPGONidfLHJKvA0KTwFmCv9ftgaaD5QP79rNJA8519DDTb7yiQv6fQ54MRWuHlDsL2elw77ip5TWshuehdR0hdBgW1QjN9WLFdQ1aha/G4EHdXlGUNFffuI5zkStS+0JJVlqMldzBSAE4Evgg83Dq3FbiBbErqDcAW679LyWYdHQHOa5OvQQqrq/evklqsfTSGQUBXNHUQvp2qxpulbYTk8uD7dA5V98SKd4d0zm2koEFGXeHj1TXB9/6+PYUCMQlWS45ttIWUz6CeQsxDc/aR5lz/odDVWvWRo6GLaxq+pKY5RtR0b0gYp+36MXSoVWWprcuYQoXT4iloGG2JFBxQeAghbzSPGX245GNBX3kdU4dWQDvvscsypvxZbfMa7a6JFI4ngbW1Nd7xjncAcMsttwysTRzs3bt3w+cso6+8zs3NMZlMRiV77969HDt2jGPHjrG2tsbc3FyQHueffz433ngj559/fpCcKqytrXHs2DH2798fpa5mtc3HbHcAD4gmeYpw+eWXc/ToUbZv387ll18+tDoqWFtb4+DBg6ytrQEbG5J9fswo58EVRV7LHaKvPE3dYssBOOmkk7jssstYWloK1unqq6/mmmuu4eqrrw7SrwpLS0tcdtllnHTSSbXkFVI+de1AM40YcoaSv446F2IaDq3w0Sy9rFagznWeJpd6zKGQWAOJGnJCx5Y0ZTXBZSyva/n46KQ1E0mjLl3HsEJBGlNoxsrKillYWDCTyaT3GQ+x5NelNU2Db9odkuaMHq2Be63ydX0RzWX2iusAs+vMLFd9qmR1Hez20anqvI+hqNEmNMrUBYkUWlBUBGAWFxdVZMae8laFEPk+jTSWfl2tWW2M2UNxTaOpA3SZveKic9XMGNu76FLXtqdQltul/JoMoy5ybFLQNuaa6iC24VggkUILVlZWzIknnrj+roIGtFzSLgiRX+irueZ9U6fhokvdwxs6VbJJxy56uuYvpldToOwp1NWnq+w2r7PN6OlarlWEUuheDi256u+yNWydh2Vfq+E1VMkL3cs8BIkUWlBU0oknnmiuvfbaIFlVlo8GtAilreNyXRJDy5r36ZRdrWIXtHVkrvfavzX2LujaEZV1KTq7kJcxm9qEq9HT5j1UpVE+Vw51uaRth6XqiM0Yt0UBCz0mk4lzvTa1jeJlWd8xTI2+IJFCA1ZX73+jeUwDh13kdmkkLla4i7Xr2+Fr5CskLFCVRuF1dN28pKyHRkdcoCsp1JVJCEGFehu2jDrSbUvDdY+QNkOhro24jCd2NZiq0m8jui7Q6GMSKTSgKOBiiYtYlngomuR2aSS++mla5676uOYrVBe7Ywp5W1TTUwjNk0b9dA0zaf7f1eNuC001hRt92plv+fZVL21IpNAA2xoJXU9kKMQioro0+kivnKbL+dB0VlZWoi/rUHWP1vhIiB6+Mnyt1q4hwhD4eschMseORAotqHNTE6ox9AMxRIiuChqdb5Hm2GY7xfbSunTUfXlNXdLpyzCKhUQKDpj2Su4TQ5fVECG6Kmh0vtPuKfQhvy8jZGhjp08kUkhIiIChybEONtFozoLzHWPwDatpeApa1n8fY2h9ykik4ICxWmyxZU+DjmNH32M6bbBDUr5h0ar0XCzpqmvq7mvKk9bSM1qTMLRCixpy0uyjnkihaISaA84x3VEt2T5yYg9CdklDG2MddOzauYV6ClXpaXsKTXnSIgWtMJVWaLFNTvl/33pow2CkAJwMvA1YAW4DdgJbgOvJdl67HjjFun4fcJRs57Vz2+THIIXt27erPeDTYIXH7ARDdBwqvhuTJEPQtTOJnV7sNIYwCjTT1BqAj1UOQ5LCVcAv5N9PyEniABv3aH5V/v1MNu7RfAc97NFcWFWLi4tmYWHBLC8vj2IQc8zplNOIkeY0eQpjwGYaJJ1laI2ltGEQUgAeBtwFSOn8EWBb/n0bcMTc7yXss647BOxsSiOEFIoCt8NGMR+qWZ5B4evqz6L1OxS0xsT6IPy2NDcDqurLJXSkhaFI4QeB9wNvAG4BXgecBNxbuu6e/PNy4CLr/JXAhRVyLwYOA4fn5+e9C6Uo8KJiJpOJ6iBzGbPkKZThSwptjV77oRizNa1Rbxrx57IM+zmJ1a7GXC+xYE8GKJd17NCRMcORwtOAbwE/nP/+Y+CVDaRwRQUpXNCUhoanENtNq0pvbAjVrbi/66BmXbq+8rT1c9VXQ47GrJSucl1klD1q7QFPY9z3guiCsRthVZ5CjHKow1Ck8L3AJ6zf/wZ411jCRzbsBydWY9Kc2eAKV7kaHZKPHC19+pIXU47W7JYymuR2yY8GkdUhhqcwjeHaPj2mQUghS5f3Ak/Mv78COJgf9kDzgfz7WWwcaL6THgaajdnY4GNVjNbD2QUaHUcXuTEt6THIG5OcrjJcPYu+dag6P0T5jCGdPqMJQ5LCD+bx/48Afw2cAmwFbiCbknoDsMW6/lKyWUdHgPPa5GuSQjGuUBwa4wuujT3Ww+kj1+WeLnJdr42la6jsph3BuqIID4TOcCvr5hIWa/NUNcbV7JBIlU4uRsrq6up37Teg0TZ8QzOxwswxiLALBiOF2If2JjvFobUJRjkOW34oujbUtrBNMRjoGztfXW1fMVYzzNHmobWl5eoJhci23wgO9eiKdhX6LkxZt+KzaTDYxVMtD3xWwVWOyz4ITSEp+/lrK3eX0JbLZjptcnw8b9dQq0sb1SSORAotKDZFOfvss83CwoJZXFwMIoVyJ11XqS4NtUpuXdjG3sDDp9Mp5O/YsaP2/q4do2vYzCdmHeIpuMrW9BSKdra4uBjFU+iyCUxZnqun4OJxuL5JXdcRtk3VdJXT1VNok+Na/y5E0oUgm4jDF4kUWlA8rFu2bFGZfufaeLQ8hfL/vp6CS3gjpgsdM60+ZbukGSP9PvLUZ/3PgpwQmclTGJAU7PDRjh07eu0oxgRNSyShGamsE4ZEEykcTwIvfelLWVtb49ChQywtLTE3Nze0SoNg7969Gz4T4mEWynptbY2lpSX27t27aZ+ZWcQDhlZgDJibm+O0007j6NGjvPe97x1ancEwNzfHZDIZ1QO+trbGwYMHWVtbG1oVVbiU9djzvrS0xCWXXMLS0tLQqmwqxG4XiRTICvnYsWPs379/Kiy3GI1CQ2YMvTZzx9OUd+2y9pG3d+9eDhw40OmZGTvR+UArT65yoj8TdXGlaTi032iOub6LJsrxaI0BKNepoy4yfMqxLo1pGJCNNfjqOnNLQ7++noGmdbKGGPzXSF9rfMj1udYoJ9JAczOKaXDFPHKNwb+YszTKv10aZZM+VdMAbbRNHS2n4TM1ss+BV+20NOW5yurSvrq0D99prW0o5Bcz/apIYejBd9/0Y81+ilkeiRQqUFcBY9l1zdbPdU697/zyct6bSMhFlyqC6Tr9NOY0Qx/ZLmToMgVxiGm4XWTGstZdPJFp9RRiIaY+iRQqULZ+Ndait9G1QpusBI3G0dap2S+8hbj4daTRlSS7Xl+nV0iYpQsZltOre3GxqyWuMeddg3BDZcT0nLUwNlKIiUQKFah64DXjqV0bWFM8MTYpGLPxRTrX/RG6WOGxPYW6TjskzFJFzG0vBtZ1/r5tzNbB1/tsylefMrQQK+2hw1d9IpFCCwpPQXNMoWsn0NR5aXgNTaTjm4bv5jpllD0VXxmhYQkXYnINM3YhudihqbIMH69YKz9drolx7xByx4ggUgDOrDj3zLb7+ji0SMGY+5e62L17t4pFXliUTQNrXWUVD7SPvPKSGm0k4aJXSN5sFHlaWFiI8kBqWoAaBFaGln4ucvqwhseiR0I9QknhY8BvAAI8GHgNsNx2Xx+HBikUHdzu3bsNZCtYhnRMZYs7ZMXVqgenihRcrD8XEnANkTR5QT7ho3KetC027R2ttPXTkhfbQp82PRLqEUoKJ5Htn7ycE8Q+4AFt9/VxaJBC0cFNJpPG1UFdUTVW4TujyTVmX6SjNUOpzRJ2DXW5nK+Sp21FJqt0NqBJJH0S8RgRSgonkO2W9iHgKPCCtnv6OjRIwbYifeOtdeirwaysrJiFhQUzmUxUQ18+q63WEVmXcvXxNnzkhV47rbKGkB+CQjfNdyi0Q3YLCwuq/Yb2bMgyQknhw8DvAA8k23f5b4C3td2X3/sJ4KM5oRzOz20Brifbee164BTr+n058RwBzm2Tr+kpFKEQjYbX9wPWdVC7q9w+Yt19yukrjbHKGkJ+G1y8T9/27epx+2BlZWU9wqDVb9irNseqj1BSeFrFuRe23WfuJ4W50rkDbNyj+VX59zPZuEfzHfSwR3PBynbsP7Sx9P2AxbCkbLljcbHHEg+fdllDyG9Dl/CipuxQaHoKNvmN3VMQ4CLgt/Pf88DT2+4z9aRwBNiWf98GHDH3ewn7rOsOATub5GuvfbR161azuLg4tQN+VQOqMTvkrrKbru8zxNPFPW9LS2OjJI2wmI+M8j2h8oypL482eVW6+LwZXyV3MpmYhYWFdZ00w5DlkGvIs1DkWWuP+CaEksKfAlcAt+W/TwE+0HZffu1dwAeBm4GL83P3lq65J/+8HLjIOn8lcGGFzIuBw8Dh+fl5lQJaXV1VcwFdEcN6qZIZM3RTPtf2sDXp4qrn6mr4lFB7YL5tEkCbXhpbqvrWkX2fj4zyPaHyjKkvj67y7DoKKasqORrl3eW/LrLq8q2NUFL4YP55i3Xuw2335dc9Kv98RB4a2tVACldUkMIFTfI131OwB2t9t7PsghieQszYqYvstgdDw1Mo0ghZo6ocMmx6+JKn0M3b7eopNJ3v01MI9e6byrKLrKp8x+grQknhJuA4ixxOtQnC9QBeAfz6GMNH5YqIGYMcOnYbE13CMr7QfOdgmutCU3dfsu46DlB1rouH6JtfO406OSEGjW+abSju0x4rNCacFH4WuBr4DPB7eaf+fIf7TgIean3/R+DZZNNb7YHmA/n3s9g40HwnPQw0G7Nxj2aNSgwJR8RG7I4wdv6GLj8XxPTOCviUg0+HWPWfSxzdNdzoakS45NfFG6mT00aMXUOWLmm2obhPe1ahMYGkkN3PDmAReCnwJMd7zsg7+Q8DtwKX5ue3AjeQTUm9Adhi3XMp2ayjI8B5bWlok0LoMgshDVcbdaEA3wbqqrev2+x6jUb5xaoDX8vOp7P2yYNvh+hj3bt4Cl3aoo/BpRWqCw1ZxjQyfeFFCmTvE9Qedff1eWiRwvLystmxY4dZXl4OkjOmcIT9gGi4sloWuoucafU2fC07n87aB314MEPIqZOnVc9jeq614EsKd+UhnLuAbwNrwBfz73fV3dfnoUUKhWu4Y8cO1YrvOvjkI9PlulDr3P4/dBC+KWSglYaLDq7pdKmzrmXeJQ3NjilElpb1HVPHAtprXhXoqtsYvOMyQscU/guwx/p9HvCf2+7r49AiBfutRK2d14yJswa91nLVxvjFPZtizKGWsYZHUwXX8ExbqKSLTnUhB5982TFtjbd6Q9ph1b0xPC8Nma5159t+XfuKEO9Yo+6rEEoKN1ecqxXY5zHm/RSqLGKN2GIIKTS51108kHK+uj7AdWlpdVxluIZn2givawy8anDSJ192J+Q7E0WLcKfJU3CtO5/222XgOcRT0Kj7KoSSwiHgN4HTgdPyweBDbff1cWi/0ay5qFXXDqQpzOTbeTdZvXXXuMiqktN1KqpruKrPcIm2Ti4daNcOw9fTiD1VuAtikIhPmsW55eXlzmGmvkJ+dt1phlVDSWEL8MfALfnxx7M20NyV+V1ldg011IWZfBqXRpinTpYL4fjotxnRRzmMrazHoo9thceceBAqN0Z5BZHCmA/NN5qHsF7q0tZ2nbX100hvyPKuwlAhkD7KYTOUdYgesSY2aOUzRnmFegpPAP4MuA54d3G03dfHoUkKxoS76V3TiImxPHjTghjW2Fgs4oSEMppI4Xja8d/JZiC9jmw66sxhbW2NpaUljh07xmWXXbZ+/pJLLgFgMpmopbW0tBRF7lDpzAr27t274XOsMhMSoqOOLYqDitlHYzm0B5q3b9++vntZ8hQSEpoxxGSAWBg6/b5BYPjoFcD/RbZ43Uy+0byysmJOPfXU9UGnWULMxj7ND9I06z4W9DFtuC8MnX7faCIFl/DRi/NPOwZhyNY2mglcffXVrK6ucuqpp/Jbv/VbQ6ujgqqQmHYYaZpDVNOs+9Ao2tb5558P6ITHhg61DZ3+qFDHFtNwjHlK6tAoLJ8YKywWGLu13aTf2HV3Qew8tL1QNeZnZRbq15h4+cBz7aNn5Z8/WXXU3dfnobVHs+8LLC5yh3pBp8/0h3oA29L1fYEwND9asjTyF5J+nWHhK1O7nTTJ8637mDr5IBYB+5LCZfnnUsXx+rr7+jw0SKH8AstkMlEhhyGsqdidRJ08DS9L6wU9W1aX+ee2LPt7qF4hLyK21aeLrJB2WMh3WV4hti5V6TW1uy7lrLmemKYsY+It6udFCtNwaHkKxWbZk8nEbN++XWXAua1RxnpZqm05A22ysknVTrNr/pr0qpPlG96ouq/Ouvch2iZPwceCDXm5SqNTcfF82gha23st0vNZmqasR7kjd9Wz6ro6Uuia9zpC1irDRAotmEwmBjBnnHGGIV9C2/chcq00DSvQRW5Th9UVbZ1pkx6+ctpk+eSxi27aFrBPJxlC5LGMgKbOP1b6NkkWxo/PInFlQ6YLcVcZDHZ4zbUcXNtoOXSnVZ6DkgLZ/s63AO/Mf28Brifbee164BTr2n3AUbKd185tkx1CCnalFKujFpZHCAu7VppGvNhFruZDWSWrqxVfJSfEq/IJXzXJ83H/tQhcW37ovRryYngHdn37yA8JPdl1VmfN16Xp8ly2eYcz4SkAvwr8pUUKB9i4R/Or8u9nsnGP5juIuEez/fCvrKyY+fl5A5jFxUVvmcaMb70TzYeyqsH6kE5X4nJ5SH32waiSW3iNk8mkk6yyPJdydyFU7frTWC01RpuqI/wqr62qw+yik0s5dKkbn/Beldfj+zz5IJgUgHOAnwFeVByO9z2GbB/mZ1mkcATYln/fBhwx93sJ+6x7DwE7m+RrkcLq6up66Gj37t3eMo0Zd2euZS1WWUsxO5muoShXVMkNGSi05WmFfGJ4eqHyYnufPuloX6t1TRl1BpHm8+SCIFIA3gj8I/Ba4DX58Sdt9+X3vg14KvBMixTuLV1zT/55OXCRdf5K4MIKmRcDh4HD8/Pz3oVSFRv0tRBtaD0wfT14Puir4cZOr0ruGCz85Cl0T0f7Wq1ryqgKnQ6x10UoKdwGSNt1Ffc9B3ht/t2FFK6oIIULmtLQ3nlNo2KG9hR8O7q+9OtLXkz05eLPEupCQWP0hGNCy1gIRSgp/Pci3NPlAH4f+AzwCeALwNeAN40lfDSr6KvD0k5nmjraaeqExoJy/YaMBbXJ9sXY61XzGQklhfcA9+Sd9NXF0XZfSYbtKRxk40Dzgfz7WWwcaL6TiAPNs4q+GvZm9hQSuqPKU9BaWkar7cQ0TPoYe+uCUFLYXXW03VeSYZPCVrLB59vzzy3WdZeSzTo6ApzXJlczfDSLHVLsfIXMvghJbxrHMma1jYVgbGUSU5+xecJBpJDdzyPzMYLnAI9wuaePQ3s/hbFUmBZi58sOAfRh8fVdTzEG+zXCJQnTh7ERYKin8B+ATwJXAX8O3EXFrKAhjuQpNCO2y6rtKbR1wtPuKczaSrwJ04tQUviw7R0ApwIfbruvjyONKcRHn9b5rJJzgVnPX8L0oIkUHkA7HmCMudv6/UVwui/BAWtraxw8eJC1tbWhVanE3r17OXDgQOXmI9q6z83NMZlMmJubq/xfM70hZLXlL1T+GBFT97G2hyHkq6KOLYqDbLbQIeAl+XEt+dIUQx/aq6T2/QKJMdM9ntH3bI1YL/T5WPBVLz/aLySNLezU1/sz5f+r6kxrOWitZao1ylhrPKwvbxLf8BEgwGPJNtb5Q+CPgJ9ouqfPQ3M/heKIuVNZFaY5pND3bI1Ys4F8yKaJVLrIc+1MQgaoNYmlTY4LQRYyijz5vtRWRQo+bcS3jLu0IRe9+hx38iaF7F5ubrtmqEPTU1hcXDQLCwvrC6JNo+U+S+iTLEM9hRB5fQyuhxBLVcfXJMdFX9tTqHupzZdQfQjet4w1vU1bXh8z1EJJ4Qrg7Lbrhjhi7NHct6eQsLnRB/mFpBHa8XXVLTSNsRsTNnzDjhp5DCWFfwK+RfZS2UeAjwIfabuvj0P7PQWfXZya0MdDNCtytGXFlj3WfGvLGtOaYLPWZot7u07p1hhbCyWF06qOtvv6OLRIYXl52ezYscMsLi72vp5P10alJVNr0DbW4K82tGWPNd+aYzGaemkMCmvoox23b9LJtdy75msMnsJ81dF2Xx+H9s5ru3bt6t2ajNEgXGRqzQDxtXaaZNkyhrYO6+4bi7XpIsu3M9XUS4MU+hxf0QjluJZ7nyGvAqGk8FErbHR7Hkq6te2+Po4QUrArLGS3rVAMFWIas+Xch9y+0x8yfDVEp+Ojw5jGV7S8kqHLvQ5BpPBdN8BTgP/a9b4Yh5anMFTlxUq3rwewjzIceuBQq5y0pxrGJMuhnoehDQAbY+7QNaBKCpk8Puhzn/Yxtk12NMYHNF3kLg+XT7q+syfa0h66U9KYEmjnIUSuVviqy/Wx2mWbrJh5StiI0PDRr1rHrwN/CRxqu6+PQ4sUinhnaAip68s0TfFfe2qs78PSJc7vM2BWPu9r6ZXv64vUqmSUX6zyhV2PIQaHxkBmWZ+2ugwZl9AOXWrE7F316kPGWHQJJYX91nEp8LPAg9ru6+OIQQoLCwvBcmxS8B1ILmQdOOC/eXvfD9+QnoJm7F9zWQm7HkPkaHWKdfq0ydKMxYd6LdpyXHXUamMackJlqISPgJNcr82vfxDwfrJVVm8FLsvPbwGuzwetrwdOse7ZBxwl22Tn3LY0NMNHk8nELCwsBM3G0YxJa8TrY1g2Y3XZ+whvDC1LQ7ZW6EZTpz7kdTU8tEJomn1Cm9wuCPUUdpK9wPap/PcPAK91uE+Ah+TfHwjcBPwIcICN23G+Kv9+Jhu347yDHrfjXF3Ve1HHJa0xdqwu0LKWEhKGhKtnFDrVeqzPSygp3ES2KN4t1rmPtd1XknEi8EHgh3MvYFt+fhtwxNzvJeyz7jkE7GySq0UKKysrZseOHeshpNBVF9sQ2lCGJJVpJrTYGGPZjFGnMobQUTMcppFO3wgmhfzzFuuc0yY7wHHAh4CvWh7BvaVr7sk/Lwcuss5fScsOb1qksHv3bgOYk08+uRdSCG0oY7U+ClR5Xb6D5bHCHD5pt2GM9TJGncoYs45j7dRDEUoKbwPOyS39E8hmIL2l7b6SjJOB9wBPbiCFKypI4YIKWRcDh4HD8/PzKgV0zjnnGMCcffbZU9EAxt5Qi4fcftC7Pvh11/fRgfimMZZ6mYYxIBtj0nFMusREKCnMAX8B/DNwN/AmYGvbfRVy9ueEMrrwUbHm0eLiooq8aUOMwUFfT6Ecyy3HdLvK8clTrMFBTTlNKEgtdFptDF3H3umO2WvRRBAp+B5kezmfnH9/MPBe4DlkO7nZA80H8u9nsXGg+U56GmgecpmLMWBMD0JZF1/dtPMUezqi9ownjWm1MdrFmNpaFcZOWlpoIoXjqYGI/Hbdf4Axxryy4f/CC7hKRI4j29P5rcaYd4rIMvBWEfl54FPA83OBt4rIW7l/qe5FY8y3W9JQwYknngjALbfcwtraWuc9dKcdxf7LVfsw+2BtbY2lpSX27t3buSzLuuzdu5djx45x7NixTnXTlCcf/bTKqE7O0tISl1xyCQCTySQojbm5Oa666qr1PPpCu13EkqmJYh/tTY06tgB+reL4beCTwFfr7uvz0Fw6e+vWrVEHmcc2Xz1m2hovCtmIJS/WDlea7w/4ytPWse16TR21VvCNIXOawohNIDR8BDwU+E3gLuBVwCNc7ot9aJFCsXQ2EccVYrrNQ7rkGi/5uM4Z15QXcy/csYauCvjkv00HTR3tpUa0oCUzdhixL3iTAtnbx7+bk8ErsN4+HsOhPaZA4DIXTRiDpxDDygmVqfXSYFedYk551Z79o912mjylOt19PIXyOdd8uFr1XcplZWVlfQ92n3bhk2bf3l8XeJEC2YDwHcBvkL+ZPLZD8+W1Xbt2mXPOOae10UwzxmjlhMiyH6xQnQoZvmsVlWdNFW3IV6+Y4Y6mDsnW1/7u04nVTRoo8hXaKXYdtHepi6ZrtD3WNsQkDl9S+A7wdeArwJet4yvAl+vu6/PQ3qO5eKtZ000f00yGvuKhWtZUG0I7rSpZdauItultd3i2Fe6rV1mORhm5oM478OngqjwFO1+hz1rXzj/Eg2ySW3efVpuMEWIKHlMY66FBCkX4ohhXWFhY8A5l1HUUQ8UNh0Jf+dYk3VAr0PYUNDo821PQ8qZCoC1Hw1NoS0Nbti9hxEgvFIkUGlBU5mQyMXv27FkfX/Cp3LqOYiyeQij6HrsYCqEWpXb+p708x4yxkWZfSKTQgMJTKNY/mkwm3pWrPcDYNc3YGDvpaemzWT28zYjNWteJFFpQNIyCFDRlxm5sfTbqsYfHtPQZG9mFoDB6tLea9S2jGGUbIjPGOxHTgEQKLVhdXV33FBYXF1UabbmxxepoNBt1Vx3rro/dqQ6V7jTCNnhCyLJuJpHrwHydnCp0JSAfmV3u7SKvK4Zqs4kUHFBMRSwGnEOtzbqHSNuqjjEDx2fWhoscLYzNQxkzYnsKXafwurSlrs+Oj8wu93aR1xVDteVECi0oHpzJZGImk4nK7mvlWRaxZlssLy+bHTt2mOXl5eAGFjodsY+ZJWU9pxHTon/ogLuv7HI7jEFqoUiewkgPLVIorJ3t27err4lTdKhNMkMahtZ89jJ8PJBkwbthWsopZmjFVXbxbMbe+GqzIZFCC+xlLgpi0HpgV1fb15kJ6SRiDZT5EMxmHbTrilnyFKoQEuMvI5FCHCRSaEGxyU7olNQ6tA2MaoZbhnRry52Bhi5DDVon+EOzTFP9xEEihRYUg8tbt27t1cqNEUbQstI0Zolo5C+2pRii45jjymPTbUwGQt9EM0ZiS6TQAu39FFwbwVCdQZcOv8mL0ZgV0gZ7VlgMyzNER62Bfd9F+OrkdZkc0CTDmP5n2TTVx9B50ginjYUcByEF4LHAe4DbgFuBX87PbwGuB27PP0+x7tkHHCXbx/nctjRivbwWWuhjH0jskr+mvPRhAbmMyXSBZt2Ezs4pdPFdhK8M26tynd1Tp5NWCLBriDRGe9PKkwaZaLQ/DRlDkcI24Cn594cCHwfOBA6wcY/mV+Xfz2TjHs130NMezaurq+shpOIB1ZraOe0YQ16mOUat2cm1tUvXUFufRN/1WZr1UFqbjFBDwxWjCB8BfwP8u9wL2GbuJ44j5n4vYZ91/SFgZ5NMzf0UFhYWzOLiotm/f/8oB37HglnLT2z0SWgxw5b2PV3u9/VYfDALbbOvKMPgpACcDnwKeBhwb+m/e/LPy4GLrPNXAhdWyLoYOAwcnp+fVymgwkvQno5qzPhDSV3hk58xWX91943BG9Ec77Blarz8VdS7vZJwyPib7Z1rLUJZeEoaMwhj1UVbKK0vYhuUFICHADcDP5n/riOFKypI4YIm2drvKfzcz/3c+jz7MbmcY4JPfqqIJPSdBl9yqhuf0Bx8tDvQkHKq0sk35BRqmNhlZ4dafWHr5bK0TF2+7TIqL1XjWv5NddhUF11htwvNcRsfDEYKwAPzMNCvWudGFT5aXV01k8lkff/WorJmzcIfElWNPnQj9RByatubuAvqCM9nFz8X69QnRq/hKdj6aIRXbb1c5NXluyqs1XWjozby1SrDQuby8vIGg2iIvmYQUgAE+HPg1aXzB9k40Hwg/34WGwea76SHgeayxVJU/KxZ+DEQEiYpiDh0Fdmq+3xDRD7/+3gKIW1La1zBF5phFN8xDe3r28ZLQjptF3muumrW7VCk8Iy8s/0I8KH82ANsBW4gm5J6A7DFuudSsllHR4Dz2tIIIQXbqrA32Umv07vD92Gpum+ssrr8X6Dt4fXtFFwQ2+os5HddMrtKxhi88HLZV83gCqmfqvLylaf5IuegYwoxjxBSKDfMtMZKd/Rh3bvIKrv2mnp1+d/12nIYpunltRgWsoanUqWzpsXbh6deNcbk2w+0eacuLyi2hakSKUQmBbsSV1ZWzK5du8zu3bvVlrnQbtRjlaepl2Zcf+j8uVjDLrNlfKzqrp6KD7TDLBo6dq2rqjBfrPp2kWuHsjUMhCYkUmhBYS1ourTaLrKmvCoLqW85rp1KF6vSHrB0Ka8uD2pI/LfuHpclPHzS0+igfDC0gdD1GRla3yoZWpMC2pBIoQGrq9mg565du9aXuNDAWC17Y/ynTGrKcSWALp2yfa1Ph2+jimja7qlCE3H6kGpdvrrm3UXmtGFW8tEHEik0QGPgzBVjabRDh1ba7m2ysl068jaZLtfXpePiAVTJ8ZmJ1CVE4+NRtMlMmF0kUmhA4bJp7c3chPTwVUOr429CjNCCVoiqi/xQQneRGcN4GdIgGoMRNDZdEim0QHt55jqMxVMYG3zj9l0wLTJjy9ciu66ITaChafclZyy6JFJowTROR+2LYPpIZ2iyHDr9sSEWGbUNorp2dBpjJj551JBTd33f40CJFFqwvLxsduzYYZaXl1Xk9YG+QlGbIeQVK4+zEDLRRFs5u+oc22LvMs4Tmqfif9eNlrTqNZFCC4rxhBNPPHFqiGGMnsIYOyIXjD1sMW1p12Es8fQ2GU1l1zXU2VYPXSe6aNVrIoUWFAvhQbZ8doIfxtgRDYlZ9RS0ZY/NmOg6I8znvOv/odfXIZFCC1ZWVsz8/LwBzOLioorMITD0w6WZ/tB5SaiHNvnPgjExbXlIpNCCokJDX+YaGtPWMJvQ1a3uis0wgB5Ll1n3FHwwbXlIpNCC0A1fxoJpa5hN6DoA1xV9EOiYSHpMuiQMj0QKLZgVT8HGmC3DLjJjEV3yFIbF2PSJjbHlN5FCC1ZXV1W2GOySXuwGomkZVskKzYPvlMCu6PthHJrspgXTMA1YU5ZWftNAc0+kYEy/L7D14cqXG09IY6q6NzQPvlMCu6LvsIlreps9nBPaudXdP9a2o9WZT/WUVOD1wN3Ax6xzW4DryXZdux44xfpvH3CUbNe1c13S0NpPoep3TAwxXdC3MflOtQvBWK09zfQ2u6cQirr2PM1txwVT7SkAu4CnlEjhABv3Z35V/v1MNu7PfAct+zObQFKwG9Xqan/rmMeG9sMydot2zDNhZsXQGCO08rvZyq3AYOEj4PQSKRwBtuXftwFHzP1ewj7rukPAzjb5Wp5C0fGNufNzxZg7yRhp+JBWH6Gr1VWdjYzqZGuF86a5U9TQfQxGzxB1MCZSuLf0/z355+XARdb5K4ELa2ReDBwGDs/Pz6sU0Cx5CtOIkAfT54HqY5A75ow2zYH/MXSKvtDQfchwX+xp102YBlK4ooIULmiTrzn7aDKZmIWFheB3FYa2vIZOvyv6JOSibMq7qWnI1Bhz0eqguqQ9be3FRpXuscJKMQyXQqbrC5qadTUmUhhN+MhGwdRYeyr0Gc7QRKxZVLE6jz7LK0ZaY5ztErtM+yISbS+wC8pyYvQJXWVq1uuYSOFgaaD5QP79rNJA852xB5pt2KQwmUx6D2doIhYp1IUsQq38Jutd29qOESrQSM/3+thy6tDX9G2fMJlWfYxxwsHUewrAm4HPA/cBnwF+HtgK3EA2JfUGYIt1/aVks46OAOe5pKG5n8L27dvN4uKiWV1123u3DUORQ6x0mwY3Y1m3PuSsYU1Ni8UeE03tqC9SqDIYQspUcyzGBUMbiE0YzFOIfWiRQjFLZM+ePSryjJnuDsEVmuMBbfHhPgYEtccctNfU6tPibOr4Y4/NNMXzXXTvMs7TNCEgtLw1JxtoE0wihRasrKyYhYUFM5lM1Ap9zFaCMePXr4w+SFY7jbHKc5Hj4g3EGk8Jjed30Wt1tX7qcGj+mmR3hXZbSqTQAs3K801fu4Nuk9n1wdGyDH3zWr5viDIbgzwNzyzE2u56jY9OoXK1xnLGFEZOnkKPpGATwlCrpMawgttkdmlkths89nj9tHlAXTHLYclpIHpXmWPPSyKFBmjG/Xwx9gY0Bk/BVc4sd5rGjI/0NPUJqbs+24OLTI3QU934h0ZeEik0oBhPWFxcTG80R0YfHZpmKKDpnqE657GFuELj/1q6xPAcQ9pSaLizKj+aIbZECg0oCl9ramXCd6NowNqv8/uEwLqk3TTY6iNPo0O309WUV36r1lV2+TpNz7tL/mIQuCbhda03zTHBKiRSaMDq6qo544wzDGBOPvlk1RlICRnqOp46uD58XR4Mn+mhdaTgO+CrYc3a17ZZky6oI2xf8rHH6Lp2WHUEE2v8qU992uotRJ4PEim0YDKZJG/BAb5jC1UNuEmW60PTxUXv+iCWO36Nh7qKTDQ7US1ZhZ6TyWRd365l5xN2KROR1jswWu+LxB4Pi5FWHRIptCCNK7jBDg1ozUKqkqXl7tsIjelqhW3q5GqEW7RklevG1cNzlVvXbmwi0Aw1ankcfSK2zokUGhBiXW022NZ9zDWPQvULjUFX/a8Vs66TqzWzS+s9BttTCJXVZXA0dIxDQ4euMkPvafKkk6fQMynYhLCwsJC8hA6YRuvLhqb+GrKGjp8XqBtjiKWPK/mOpXxC5NTdo+ktuiKRQg2KylhYWDC7d+9ufa3fBV0t0BBZTdf4WB9ddauySjUtHC3rvOreQvexWOhDWrA26ix13zS7jCP4puECbc9U21OwoxWx2r2NRAo1KFtFGqTQ1si7WBgu17ZZH/b5Lrp1cfW7pOGKtrBeSDr2rKKQcQj7/9DxDO3BR9/Ow+e+kLroK3Tiq+MQRk5Z16r/Qsd5Eim0YHW1v53X+vAU6vIT0slVWVqhD3TTtW0udcjDapOCiw5VD2f5/y5y2mTVQdug0IJ2Jx5Dd9cwVaguTXJdlxx3aWeh4b1ECi1YXV01CwsLBu7fZKev2F4MFI0o5MWbuoapOSOlyeqJYS12ld3WcfvKMea7O4ixeApjQB+6xwpbNcl1JYVyujG8qUQKLbA70YIc+rCwYmF19btj3KHWV9lT0BiI1LJ6+oJmZ9Wlg0jQR6y4fZtc17Gn2J7eVJEC8Gyy3deOkm/dWXeEkEKZiTUHHl3S7BvaaWs+VNNo1Ybq3FcsXVv2mOTE0sWnQ9YKR1WFaWNgakgBOI5sS84zgBPI9m0+s+76EFLwDQW4oE5WzMY2JmhaObHyHypXa8DPhqb3oNHRxdSxTp8u9eKSJxd5Ln1Bm5yu4ai6Tr+vsaBpIoWdwCHr9z5gX931Wp6CMf3MW/fpiPocMOyKuvxoduSx8q8VTtMMfWmSQlNHF1I/WjpqGE4+HX7V/S5WuauFX8jwJZG+jMBpIoULgddZv18IXF665mLgMHB4fn5erZD68BSGlqWNPghrrJ6Ctpw+ZYXUW+z22GeYU5uAusge+rluIgXJ/h8HROT5wLnGmF/If78QeLox5herrn/a055mDh8+3KeKCRbW1tZYWlpi7969zM3NDa1OgiNSvWWIWQ5jL2MRudkY87TK/0ZGCjuBVxhjzs1/7wMwxvx+1fWJFBISEhK6o4kUHtC3Mi34APB4EXmciJwAvAC4emCdEhISEjYNjh9aARvGmG+JyEuBQ2QzkV5vjLl1YLUSEhISNg1GRQoAxphrgGuG1iMhISFhM2Js4aOEhISEhAGRSCEhISEhYR2JFBISEhIS1pFIISEhISFhHaN6T6ErRGQV+KTn7XPAmqI604CU582BlOfNgZA8n2aMObXqj6kmhRCIyOG6lzdmFSnPmwMpz5sDsfKcwkcJCQkJCetIpJCQkJCQsI7NTAp/NrQCAyDleXMg5XlzIEqeN+2YQkJCQkLCd2MzewoJCQkJCSUkUkhISEhIWMemJAURebaIHBGRoyLy8qH1iQEReayIvEdEbhORW0Xkl/PzW0TkehG5Pf88ZWhdNSEix4nILSLyzvz3rOf3ZBF5m4is5HW9cxPk+VfyNv0xEXmziDxo1vIsIq8XkbtF5GPWudo8isi+vD87IiLnhqS96UhBRI4DrgDOA84EflpEzhxWqyj4FvBrxpgnAT8CLOb5fDlwgzHm8cAN+e9Zwi8Dt1m/Zz2/fwz8nTFmB/ADZHmf2TyLyKOBXwKeZox5MtkS+y9g9vL8BuDZpXOVecyf6xcAZ+X3vDbv57yw6UgBeDpw1BhzpzHmm8BbgOcNrJM6jDGfN8Z8MP/+FbLO4tFkeb0qv+wq4N8PomAEiMhjgB8HXmednuX8PgzYBVwJYIz5pjHmXmY4zzmOBx4sIscDJwKfY8bybIz5B+BfSqfr8vg84C3GmG8YY+4CjpL1c17YjKTwaODT1u/P5OdmFiJyOvBDwE3AI40xn4eMOIBHDKiaNl4NXAJ8xzo3y/k9A1gFlvKQ2etE5CRmOM/GmM8CfwB8Cvg88CVjzHXMcJ4t1OVRtU/bjKQgFedmdl6uiDwE+P+Alxljvjy0PrEgIs8B7jbG3Dy0Lj3ieOApwJ8aY34IOMb0h00akcfRnwc8DngUcJKIXDSsVoNDtU/bjKTwGeCx1u/HkLmfMwcReSAZIfyFMebt+el/FpFt+f/bgLuH0k8Z/xo4X0Q+QRYSfJaIvInZzS9kbfkzxpib8t9vIyOJWc7zAnCXMWbVGHMf8HbgHGY7zwXq8qjap21GUvgA8HgReZyInEA2QHP1wDqpQ0SELNZ8mzHmD62/rgZenH9/MfA3fesWA8aYfcaYxxhjTier03cbYy5iRvMLYIz5AvBpEXlifupHgX9ihvNMFjb6ERE5MW/jP0o2XjbLeS5Ql8ergReIyPeIyOOAxwPv907FGLPpDmAP8HHgDuDSofWJlMdnkLmQHwE+lB97gK1kMxduzz+3DK1rhLw/E3hn/n2m8wv8IHA4r+e/Bk7ZBHm+DFgBPga8EfieWcsz8GayMZP7yDyBn2/KI3Bp3p8dAc4LSTstc5GQkJCQsI7NGD5KSEhISKhBIoWEhISEhHUkUkhISEhIWEcihYSEhISEdSRSSEhISEhYRyKFhE0JEdkqIh/Kjy+IyGfz718VkddGSvNlIvIiBTlvEZHHa+iUkFBGmpKasOkhIq8AvmqM+YOIaRwPfBB4ijHmW4GydgMXGWP+o4pyCQkWkqeQkGBBRJ5p7cXwChG5SkSuE5FPiMhPisgBEfmoiPxdvowIIvJUEfl7EblZRA4VSxGU8CzggwUhiMiNIvJHIvIP+T4IZ4vI2/O18n83v+YkEXmXiHw43zvgp3JZ7wUWcqJJSFBFIoWEhGZ8H9ly3M8D3gS8xxjz/cDXgR/PieE1wIXGmKcCrwd+r0LOvwbKi/V90xizC/gvZEsWLAJPBl4iIlvJ1sb/nDHmB0y2d8DfARhjvkO2PPIPqOY0IYFECgkJbbjWZAuvfZRsQ5e/y89/FDgdeCJZR369iHwI+E2yBcnK2Ea2zLWNYs2tjwK3mmwPjG8Ad5ItcPZRMo/gVSLyb4wxX7LuvZtsldCEBFUk9zMhoRnfgMw6F5H7zP2DcN8he36ErEPf2SLn68CDqmTnsr5hnf8OcLwx5uMi8lSyNat+X0SuM8b8Tn7Ng3KZCQmqSJ5CQkIYjgCnishOyJYrF5GzKq67DdjeRbCIPAr4mjHmTWQbyzzF+vsJwK1+Kick1CN5CgkJATDGfFNELgT+REQeTvZMvZrv7rCvJVvRswu+HzgoIt8hWy3z/wQQkUcCXzf5LlwJCZpIU1ITEnqCiLwDuMQYc3ugnF8BvmyMuVJHs4SE+5HCRwkJ/eHlZAPOobiX+zdwT0hQRfIUEhISEhLWkTyFhISEhIR1JFJISEhISFhHIoWEhISEhHUkUkhISEhIWEcihYSEhISEdfz/nLBPNxFUot4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "net = EINet(3200, 800, method='exp_auto')  # \"method\": the numerical integrator method\n",
-    "\n",
-    "runner = bp.dyn.DSRunner(net,\n",
-    "                         monitors=['E.spike', 'I.spike'],\n",
-    "                         inputs=[('E.input', 20.), ('I.input', 20.)])\n",
-    "t = runner.run(100.)\n",
-    "print(f'Used time {t} s')\n",
-    "\n",
-    "# visualization\n",
-    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'],\n",
-    "                         title='Spikes of Excitatory Neurons', show=True)\n",
-    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['I.spike'],\n",
-    "                         title='Spikes of Inhibitory Neurons', show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92b7a472",
-   "metadata": {},
-   "source": [
-    "### 2. Instantiating a network directly"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4e5848b",
-   "metadata": {},
-   "source": [
-    "Another way to instantiate a network model is directly pass the elements into the constructor of ``brainpy.Network``. It receives ``*args`` and ``**kwargs`` arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "14e659ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# neurons\n",
-    "pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
-    "E = LIF(3200, **pars)\n",
-    "I = LIF(800, **pars)\n",
-    "E.V.value = bp.math.random.randn(E.num) * 2 - 55.\n",
-    "I.V.value = bp.math.random.randn(I.num) * 2 - 55.\n",
-    "\n",
-    "# synapses\n",
-    "E_pars = dict(E=0., g_max=0.6, tau=5.)\n",
-    "I_pars = dict(E=-80., g_max=6.7, tau=10.)\n",
-    "E2E = ExpCOBA(E, E, bp.conn.FixedProb(prob=0.02), **E_pars)\n",
-    "E2I = ExpCOBA(E, I, bp.conn.FixedProb(prob=0.02), **E_pars)\n",
-    "I2E = ExpCOBA(I, E, bp.conn.FixedProb(prob=0.02), **I_pars)\n",
-    "I2I = ExpCOBA(I, I, bp.conn.FixedProb(prob=0.02), **I_pars)\n",
-    "\n",
-    "\n",
-    "# Network\n",
-    "net2 = bp.dyn.Network(E2E, E2I, I2E, I2I, exc_group=E, inh_group=I)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84449872",
-   "metadata": {},
-   "source": [
-    "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in tutorial of [Runners](../tutorial_toolbox/runners.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "36f54a4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<brainpy.dyn.neurons.IF_models.LIF at 0x159470c10>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net2.exc_group"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ad57ec70",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<brainpy.dyn.neurons.IF_models.LIF at 0x159470d30>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net2.inh_group"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa372446",
-   "metadata": {},
-   "source": [
-    "After construction, the simulation goes the same way:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "29ebd650",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "299a90c5803542039bb272b31ad67d62",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Used time 0.3590219020843506 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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M4KMAbmuuXwrgHQD+tPl7CXvmFgAPAXgQwDPZ9acBuL/57bUAQi79PsjiSU96Ujz33HNdndWLU8mzSA1i7T5vx+5yv/fZXH1wOVYYpk34TUt7NBqpStojhyvcVJms8kg5kkhScnL5mpubm/J0eF5L2jjl+VjegEemZa1Tvvfv3x9jHM9xaCu+uowp6QWWekR9ehulWAuyCADOb76fCeA9AJ4OYAHAy5vrLwfw6ub7ExpiORvADICPA9jQ/PZeAHsamYsArsul3zUMRYP7oosuOu3mK7zIhQdKPIXU/SUWXM6ao1AIKUWrzbx57upZ0W+0Z6HNAglLfhuPy1PXqZAVR0qRl7SZFnPnczG5smtIkRX1jX379iX7YBvrnuRwUk7JsfbX9JWfNljTMBSA8wB8AMA/bbyGbc31bQAejCe8ilvYM/c0BLENwDK7fj2AX86l2fdxH5s3b45HjhxpPeAtrLa10Kf8LlY3dXQt7txlUFrhkdKJ6ly+UxZnKv9c+bUd6JZ8eb0vZeLNs7dveeqHfuNhO36ttM+kyNPyJNqWTysP7+dtPAtPv1strAlZANgA4EMAvso8iC+Le77U/L0TwA3s+hsAPB/ALgD3suvXAnirkd5LANwH4L4dO3Z0qrDl5eWJV3HGGWdEAPGSSy6ZDKK+kFKifcpvq0B4B5VKJBXP58TCLWvN4i8ZBERUlhw5yWrlzTvvkVNUJMtahSTrwWNdW2XWrE/uBbSdy7E8lD4WNsh85jwE7d5SBUwgg0/OE5E8z0bSvjzoUg/JWhp92pLFJBHgYgDvAvDEBFm8XiGL5wHYrZDFW3Jp9u1ZAIjbt2/vvNfCCsHkYslt0ypRICnFyDd65fZdULo0IDnBeEnRUrJ8x3jJwNHy1tab4XmS5WxjrUplKMusWd8WgfN7Um2vyVhYWDknlDuGxNMG0pJPKdGURS3HCCdqrZyybXib0Ue7bhkGOcNO9g8uv8RT4N4VN8issF/fJLKmZDFOH0MAL8MpEobim/Ie85jHxJmZmV6UuWXp847dV6OnvAqva8877vz8fDK+ygeHtOqkAkwpIZ4XOTGcOl8pBWpPUiyyrvlAt5Qsrx/KB19qq3k1OWWoKS8ZIpFKUSo5Xr9SUVnKkCuelDUsDyfUSIbnX2sHudqIP8eVYu7sKKksSY4spywL3zMiiUOWJaXQZT+Xe1xShlHKY7J0AR03RL9bZNE1eiBx0skCwFYAFzffzwXw+wCeA+B2TE9wLzTfr8H0BPcncGKC+30YT47TBPeBXPpdyeLw4cOTznP++efHvXv39kIWHmuir0ZPKdJSS2dhYSHu27dvSsnKgcbJJKfEuZJI5b2P1TsxnthkKfMu5fBlrtrZS6RweF1oFrqVv5zSlvmUclKWbYzToReNYCSZp/ozV1pEgJqnyQ0rrR3kPgapdDkhyXpPEZLmWVj9gdefdox7iRfO24nagvcZ78nFVprS0NKIU+bnlPYsAHwLgA8C+AiABwD8ZHN9M4B3Yrx09p0ALmXPvALjVVAPgq14wnje4oHmtzuxyktnY4xThwjSh6+eWA3krO0ukB0q1cGse+WGNW5l79+/f3J+lqWAuNxcWeV6/5zLb1lvdF9qNRJ/jqxQqQxIHim0ubm5ZEzZyp/lgVGdWUo8VTZN0ZCHw61c/rxHwWjeh7a4QVtdxKEpSc0b0mSn5qj47zlPU1r/KcL1gNcN3+OSkpcyUvj8Sd/KvxQnnSzW+tOVLJaWluJjHvOYeNZZZ012ctO67FJ4G99rJbfpTCUhKXkvt+60cIUMB3T1AmKMK6xMa5BpVryW91SamoK2lIt3CWwqf1Y9UrretrW8Q7n7uvTFVFyW5jXJPOa8lFz/0u6V4TRLuec8VJm+RtiePFtyUt6MJVeSdsqbzclaDVKpZOEE76zkXezYsaPTxLZXUfdNKhwpS96y/CgfHi9g//79cTAYJN34Ejeap2kpxRLPwmtBa/XiOenUU9a+B3fOik6tzirNQ8n7NrrIIlihGJmuZ/Ub97asfiKRInsZaiqJCGgGDY0fCm1qhOzNYx99rJKFE9wtlNZy24ZIPWcp/pIwUUm5PJ6FvJYjpzbkVfK8VV7vevkcPBuj1jo0UArLACglPU2ex9MrMU40aCEwWffefkey6F7Pc9aY0M7MKhlbWshK8zg9ROD1uEtRycIJagD5WlUZp+8LXQdC13QsSOVgWWKlVqU3b578Wmf6lIJbjKcKGeRghX60cI6nrnPGg+wHqfBQLj0pq6tBpYV+2hK/16MlyLx7vQGNbNuSXCkqWRRgNBpNzo/hZNG1ITyWgXWvV2bbvFi/U4yeyNMK1VghLILngDpNrrVyheet674XPjfRBzmXtImmfLrK0u71WPq5vTS5vPK+kFugkMpviZdiwauUuxIHXzjgIbRcHlIe4MnybitZFIA6miSLNrAsMm59dVFQpeElz3MclEeav+FLMTWrypLLvQDpxmt55iEm6TmUhK1yA8yyuEuI3ZLpyR9X1NozJR5m6t4UqWvhldSb+DzlKsmTtkRZ84pKxok3L11k8/BWl9VVPA9dPcA+UMmiAKQAzz///ElneN7znteqoXjj04cvEcwNFE9e2xCC9ZxUIvPz83Fubi4OBoM4GAymlhSTl6FZlHKvhZwE91qnfE7CslZTyiVnYVqkQIpAHsHtkZMjqxILnJOxrDOZTiocKK1zzXrn7aN5bdQfUhPKWn2mQpjW+zLaErUlo+Ra7jdtTOc89FTerTEjZbSdbypFJYtCjEajeOGFF04UIx1VXtpQWkNbFlWM/c5VtBlkmqLlFj4wPlSRk4VmKQ+HwxWKwFO2lKKw4uA5q7mNtSvzn5qYtEjJSiu30suq01R9pizSXB3x3+kePsHMkVuqqqWT8qA97dO1H6fSKpVBcrzzdFxGjnysvObatm9UsigA7+D0efazn90pPm5ZiLLj9elZtMHS0lKcnZ2Nhw8fnjoegaxfbxyaNlHNzMxMFGwur5ZVNWyWgQ4GA9Wq9dYB95SsDVQW0UhlKttTUx6WUrE8Mf4cV7CUvsezsI6z8NQ9zys/HoWnxT0OzdOwSMi76Wx5eTnOzc3Fffv2mV6QR1lr9+SUrrTwU2lo5dLywOunhHw8JMOf71MHVLIoADUUf/827U7uYvFrnYUroTaWQ1dPRHY0SZJtrRm5miyXP8sC5fXTV/1blnEuJGHNzUglpB0kZ6WjKTDu0ZWcOiqfa6NkCZR3fjwK94alQcXrk6cjTxuWe1ZS/Y+TsnakR85zoHTk0Sf8CBnLc9XaVBp1Mq88DzzUzMtC5HHkyJGpI1By3pRF/DnPrQ0qWRRA8yz27t2rWnclMnOeRUlM0rI2+W+ePMpBsbS0pMauvauYKG3aYDQzM7NCOaWWBXJlxAep3GDWxpoajUaTeRf5vmWeB0+4RBu81H4lJ+xKT4zKqL1ZL2cYSIUn20oaJpY3JJUdydL2FszNza3wLPgeJV4XWr3IdpfhPn6USCokqNUPtQdf2chfjMXHHI1LOXcg25TL1+bh5AIB6XnQ9U2bNk2InWSl5i0044R/73PZdyWLQiwvL0+9Je+FL3xhjLG9Jc87Zcod9SpALk+iJI9yUEhrjwY+TWyThzUYDFRZNBisE2pjnF5hRYNMKq5U+awy5pZY5qywlOWtpaeFgbgxUGJYyPJKq9grN1VGzTCxlLe0tmW/sMiTy+XhFyv8pOVX63979uxR53lSFjeVkx90KMvL61mrW+pTi4uL5pyVtpJMhutIJslLeRaal8HDp7k67YpKFoXgnR5AvOCCC1Z4BW3kcde3rSwpT6Kt1a2FRsgqGwwGcWFhYXJU+8zMjGn95KwcvsqJExEvS44stDJ6J1+1vRvyHg+RpwY4/50rJ6ttPMqXZGty+TOpORSeD/kKWk4kOWKyVldJGRb5WmWXXhWlww+x5DKt9uLX+TycHMPSs5P7bSQhSK/i8OHDqhL3GIdafS4tLa2YB5K6SCPVPlHJohAy5i4bqhQ5ZdOHvC5IueDclabjpql+NEvbmojVwm88ZCGJ1KNouHweMtOe9RwPUrJ5ULMYtTkGqse2Z0xpk8i87qRi85KXVGg8bznil8SstTHJyJVXM1Qs40GW2UO+Mq9a3UgPI9V3efm3bt2qGiiaZ5jqx5KUeP5I1tzc3BTZlfYlLypZFIIrsY0bN04s4C67hSVWyzJoA80q10JU8jct73IwWtYZyeGDiSswr3ch82cpKY/nw70PK8+p5+R98pA46Q14oOVbu5YKE2n1pXkm3CtI7YGwds/zetDCejmjSXuG6lGuxPOOH0nklvFS4u3z8JRWD/S7dbS6RMpIkuW0PMu+UMmiEIuLi/Giiy6azFv0cWDdekYq3p8blFYIJWedaTKsyUcNNGgGg8FU/NeaAPUoFx4GoYlwz4C0ZJMsbaLfCxmakenl6l/mMdUOvM6lHO6ZWYSbUsTccyBF5/Ugtb4gCTpHlvKelPclx3npGEgpdKt/euSWXGuLShYFGI1GUzH1Sy65JHv8dtt0SqyZLmnkFKQ35KM9wy17zyDVoIUBcrIWFxfjzp07J5PuMszA81dSLut92m0I1XqLXMrq1vpDijjlb1Lhea3c0ejEmWja8TbkadCmzNQrgLnnw/uHzIt35RgPQ1mkyPuQ5/iUnNLNEYsml5Og1Xe5bmnrsba9z4NKFgWgiqeXHnWdr8ils5pei6cTWWEbj1weS00NqlQ+cpaz/J+UAk2M01vr5DwD3demXLOzsyssv9QEuhXisohYq3O6pi2bTZGtJAt5r1zGaZEnz5NGSnKyOWWB8zAWJwJLqcpQoleeVg98I6mmrD0GxGhkrwTT6pj+t8oh61jrX108hupZrBFZUMe84oorJoNiZmamd8ufd8jUTtCuaeQ6keYWe9xuzQL3ehaaZ5KKyXOyofsHg4G6IofnxZoctupaKglZT5SerLPUc1b9DYfTO9OJ7HLvHddkpc5s8hwvQvfJ3dOpvGt1yBV2yR4gq39oO9I9xGJ5p9524sSteX+5PFh9y0PW3v6zWtGIShYFoEa75pprJsqQ9ll0gWU5yEFxsqENDE/HbRO+kmlalmLKskvVo2XBUzlS3kYqDCSR2r1bEvLicui1vW0UAfWfubm5LMlbCpzXTdtNXn3IiHG6f8hwV0n9SK9LC2dpkHUid8XnwmaevFhppgw2rwfTBZUsCkANcvnll0867BVXXNFaHoF3Fi2skuvAfUIqX+1oAq3jpt4hUeIy585oirHsPQYWuck8pWLffK19rgzcsyhd5is9Ktq7Is9hKukL2k5nryxuLPDzwNooImn8WIZALk/cc7XCax45FllwhZ3zBuSkfq6tLXk5ssjlPcbVXwkVYyWLVrjssssmCvTiiy8u6qQSsvNrYZWSsEOpBSvv10I2NAg0y5ru5wNHIuWNcKtds8o1WCSqeRnW3g5ZR6mjRqyJ7VQ98uc9bcjvpYlYueKqjUKw6oFkpXbUp7y4XHo5hSktYG8YSEuD9y+vHA/B5Lxo2Wes8dolRKWhlNj6QiWLFnjMYx4zGdjaRKTX8uIdW559ZIVVNBl8IJekLwdZTrlq8olMBoPB5P0W0rpKDUySyedo5HlPElbIiOdPK1uqjqyyUZ2krEbrJFbyMjxkQfUoLXgtD32EGkgun/RN3ec1iLx90FL4bUJUVn/qqjhLPNgYbbLIkXwfRl5bWSU46WQB4LEA3gXgYwA+CuBHmuu3AvgcgA81nwPsmVsAPATgQQDPZNefBuD+5rfXAgi59Lu+g3t5eTlu375djZuWNpTs2G0a2lKKJWXyKtGcBe21wLmy00gqp3Asl98iWi7PUgBdBhmVm+YWrLCiB7JPSDJt084p5A6qs/KXaps2eeurTH3WTRvjrw0Z9ElwXfRBDmtBFtsAPLX5fgGAPwHwhIYsXqbc/wQAHwZwNoAZAB8HsKH57b0A9gAIABYBXJdLvy1ZUCPId3B7Q0Qa5O7TNhZj2w6ak6Mp8ZySzsX2eR3Sqhgtn206ueUZSXltLd8UvOEIj9xcO6zm3htP3ZyMcMdaoK9+2OY5LcLQRVbbSEMOax6GAvA7AP5ZgixuAXAL+/+ehiC2AVhm168H8Mu59Lp4FvPz8/GSSy6ZEMWVV145pThLG5bH3lNuaht07Sja83TNsoJyxKXNS/TVoTUL3MqDp46t0IGnjVP3yPK29TxWYyKzT4W5HlBiSPVBlNJzLp1b0rzHksUVqfz20Y5rShYArgLwZwAubMjiUwA+AuCNAC5p7rkTwA3smTcAeD6AXQDuZdevBfBWI52XALgPwH07duxoXVnUmBs2bJj6SxZkqeKjTuXZnVyKUgvW8zt5FqWrYqRrzL2TvstrkUFp21i7m7sqFfmbJwShDXotlJdTiiX5Imj5ayOnNE8l/cLyeK261Qjb0ydzbUW/06IB2qToma+SfZdk8dBuCWFY/ayLUbZmZAHgfADvB/Dc5v/LAGwAcAaAVwF4Y3P99QpZPA/AboUs3pJLt8sE99LS0uRIAy0U1cVl1JSAdW8p2lpSlpzSUEjfFo4lu8t91uDyeBZS2ZRY/9KiTOWF/+55LafMRyrMaXk82sGBqTBbrl/J33OTwh5S5gseuBzrNOFU/WrtkZNn5UcufaZ7JLFpZeX1L09g1tLMGUenrGcB4EyMw0kvNX6/CsADzfd1EYaKcToMQ59NmzYlV+6UytYUTKoDl8hODZi2cjSkZKcUTRuklFxqBZNHIWvtkBqYVK4c8ct8WO8psIwQnl7Os+CeYIq8cgqU/9bnnIzmXXvk8DzKMtLz1jyAlSdOjNozPGzsNQCsPkFyhsaSdCnPWlVntVPfRlmMa0AWGE9G/xqA14jr29j3HwNwpPl+DaYnuD+BExPc7wPwdJyY4D6QS7/rprzhcBif9KQnTRqc9lx0ce9INrfaNZe07WoJ7wD2dFpPB+TKpETRtCkDtQl/PwDVFVeoEl7Li19PEVPpuU0yH1xh5o71sMIuWr7bxrxl3qUiyqVfmo6HWOUzqeM72owZesZavl3Sd2Ud0bO04TS3ctAqc0n/7BtrQRbf3lTSR8CWyQL4dYyXwX4EwN2CPF6B8SqoB8FWPGE8b/FA89udWMWlsxxHjhyZNPTWrVuL1mJLWJ2cK58SK6GLx+Bx2T1p8UElB1jp2nWZx5Ti59ZzqWfhSbfE+s5BI5quCx00xeHd52HJ0epB+98jI5VeCflYaXM5bVaNadY/Nwy6EKSUqZ231dUbaDu2vDjpZLHWnz7Igq+IOuecczqxuWWtlh4aZ8nzIOWyp8JjqUFL90qy8Dxj5TFFWtZAlp5HFwWcsrZL5XEPiL/Jr62ykG1IZEnl9ipPqy+UlF2L7afu9/QJDxHI8JxVvlw5tP7E85h774RHpkZIXTwXnkfvYoRSVLJwgiudbdu2RQDxjDPOmFjkfXkWuett5XXJx9BYAZVSpPxZ3qGtdLpY6VpZhsPxrnI6rrzE5bdkpry9Uk9lbm5ukrcu81GSfEgmxcNJLvc0LFLlcriyt9pRI+jR6MSZSTt37pz8ZilCi8w5afH8pzwKflIvH4+p03Wtfme1LydCq82orNYJu/IerY6st/hJYpCELPtSn6GpShZO8MYBEM8777ypwblaccL1AI/Fot3j6ah8EPYVe+VWG7VRW8+CIENdXo9Jgg9o7gW0JUdJAvylStwblOlKpS09SK09+O+aZczlSAPKIgvebzQlx083sLxHnhfNqJGekkXyPE3Ls6d7tPdOaPmx3rWh5YOXT74jxVsffXi/FipZOLG8vDwhBnqlKoB4+PDh3hqjz4btMy+pQ/a4crGWgKaUoaZkc/WQ+50G6549e8x3OZTKHI2mVy1J5ScHb0peSvGV3iutyZQSGQ6HkxMIpHeUUjLccufeFX1SE81a/Wrf5cKOEgLlylmuqopxvOSdv143Vdf8SHfLE0q1K+Wd6oZCjLm3EcpxoHkW3rcHrhYqWTjBLQputT7xiU/s3HAyBFCiOHMyu1rSPF+aYuR5ttLkz1vKoYQYclZ8KoSQsypTMrU5AVKWQxGqs+RZ4QVtQl7zQrQySZLSXuykKRpqFwqF8Pqg3/jbDq37rX5LBMPLlJvPSNWb9f70YRNypBdFyXqUMi2yGg6Hce/evREYv0ArR4ASKQOKn+6ryctds8ZcDtWzWAOyoMai9wLQp+sEd4wrd35qFnyp/K6hHE1xEFloyj/VGfn9MuyQs5JKPA/Lqs6FOriisgiFysAVnZQrwwTa4JdHuWsyZP7279+/IrxC+ZFhUB4yorxYixe4RX7gwIGpduJLO+mafA+3VI5ancsyUV74S4M4CVnhQmspNKXD61XeJ9uCt7tW/1Sv1psWLVA68h0iFLLKGRJSFj+JWTuJ2EMAqTYqRSWLAlDF8zDUzp073aEOC+vRs0jJkuSRu5//X3p4YslyQK78tDr0vNuCDy6uNLm1aFmlmpLTrGYKiSwsjF8atX///jgYDKYUpSQ++b9U9LxM/AVSmiLmXp1WRu0Znh73LMh70CaoLc+CrwCjdHMv2Up5FvJ4e4twtH7Ar1G9XX311VOE3vaocvJSpLK2+iOHJEFe7yWwjq1pg0oWBZBhAc1y6oI+Ffxqpm1Z8PRbahdsKiSggQ+a1DHao9GJeHNqUHGlY51QKy1rrRxc6XnqQipAKhc/xZiTiJxg1WRRulL5c+WuTXzLeqL8pJbwynqRHg59ePiGP2uRsjVhm7KINSLxnMWU63uahy/r3YIW2tI2RHrDSbx/SY9OykmN2epZrBFZxDhuxIsvvnhqgPC4bhv0TRJt5OVc45Qrr4WstFVing4uB91wOJxaYqp1/hxBcciQkVZmHv7hlj0vPw+nkDw5uSoVolZOClXQ6jqSZ4V8tDCXFkrh1rrmLUgiHI1WhshS7UMERbJo75FcusvzV+IxS29bW2Un3/mR2oCo9T3ZlzTjQfYFz0GVKc+Bh+e0yXh5n/T8NDkWOXrquQSVLJyQnZc+F154Yad3E8fY/6Ff3hgrR87VTg0snh7VjzYQPPni91iWngxfpZSyVs6UZxGjb1KdW35yB7ZU6KnVQiRncXFxyqqXVqmUxcvPf+MKUbN26R6+B4XXoXfPkFSi3CvJhZQkUr9bE+Kal5VaHp0iLK0uuScgSUvrwxZ5a6FPz656ma5sF6+cPlHJwgmuLHfs2DEZDLt373YrKgspq70PeR54PQvNddaUkpa2J1+ctLyyPDFlb51Y96XKyBVxatURXbPqmcuZn5+fnFrKwxhtwwpEkuTJSE+spM9IWbIsuR3OvLxaaIvDMgRKx4y3L1mGW8mSXunplnpVlqwUQZ0MVLJwgiukG2+8cUIWu3bt6rzhS+JkdwJK0xMDXe28ebysNuTKFXZJ+iWyeB1yUuX3W0RjhZO4hW7Vh4R2T5uNgFZafEI5F5JJyeGhFMvY6tMQyd2rjYG2JKR5lW3yVFq2Nvd7UcnCCd5p5KtVafVEqSfgsWI9172/5+BVqDw9GlxddiGnFII2WGU+PV5Gqmxa+qnyWLJ4XnN1Y4VAKJxCfazNfJjMx8LCiVVXOWvfksOR8/68ZO41UFL56dOLpuupuSqvLJk3b5hXC4Vp5Uzlq696kqhk4QSv7MOHD6+w/NqEoXKd1nvd+3sOXrKQio7H67XOniM/LbQl7+HPpix7PknrtUq1etPCPTkiKfF4NPm5dNtYll0Uh+c+qxylcjzQ5KTq2FLQqf5o7eew+nSqL/DwkXXkiCZHIw65Oo57oR6jsqtuiLGSRStwzyKEUGStcVidtrSTe3/PwZsudby5ublJXH0wGKw4rE2uVpGbDnOKhg8kbqVb1ijJ42dBeQaIVm6NyLxWpMc7KfEeuwz01VIcUr63rvsOkaQsdnm+kgej0cod8DGutPxzy2nlGOBHjnBZOTLn3o70pj1LejWZbVHJogX40sOzzjqrs7wY89ZgH3Jz91gDXip1msDkXoUMp2j7C/hqIZm3lFLTlrumLCrKi3XCqkRqwHrOPZIyvO3nDcOUtKFHGaQUbFsvhlZBaYRrtamHKHPpyj5LdWqdQOtJi+dRO7PKs0xb1gvfFMfTzoWnZD/09sfVQCWLQoxGozgYDOKGDRsicOIY5i6NxpWrd/LRC4/i8lr43BXmIajcxC0hNTByCpt7FjmLqg+rq5Swc8SngdJom44myyMn1d6l5ebEbIXyZJtabVMyZ2aVgdepN2Sr9V1rNRM9511irC0G4P26pJ/KfEt4PNouqGRRCNkZd+3a1Xm9M++AqUmuNujTKpWrekomJ3PptLEqU1adNrncJo1U2KsrvEtMOSwFnMqjReSaJ5kife07GRDavhpLlnXsSskLv6y2HI3SO/m1PGgklcqr91yzGHUDSXrM8rgWb9+2DAbvBtVSVLIoxGg0ihdeeOGELPiei7ZkoXXMPkNRfaGr5bKaZeKDjKdDioDmS3IeiSwX96BSSjV3nf9Og906DiIVovCEdqRSt4wZD3nyupTE4FWYUpYMD3o9W4sIpfJMtY3WB6QR5CWA3HlLuT6iGSLWGOF9waovzWMpaZ8cWpMFgCco156Re26tP33MWezatWvKu/DGxkvQl2exGmir9Pv0cuT9cs5EWo38yBDL+pWDkM/NSIVuKeBc3XDPlM/7kBz+vHeCVuaHy+DplRozUhHLumjjWWqrjSzvRdaZVqep8klZklRlGVNhoVQ/scpv5TvX57R61VZT0VlYdCSMVo+cCLvqky5k8QCAfwMgADgXwOsALOWeW+tP14MEB4PBlGexb9++danQVxNdiSzloZQSEd0vV1ppVlbqYENutXGLX3uuREHJ63y/AycjksMtSO9EtFTkMvSWU+pWe0jlKvNjhcRyXoA8XVdLkyvT1PLU4XBovqhIytLqgStrTz+RxkhJfaYMG35N1utopL+jhJ/DlWsPLeRYii5ksQnAnQCWGuK4BcAZuefW+tPHEeX8c/jw4dbyUjgZnkWfaeSsQ34Pddw+jkKQIR3NQpd540pDC02QrMFgoCqYEqs6V+aUsrTqV95H3+UZSlZeUoQp85bKb1svgFu5mmK0frPk5d4rzmVZniW1jzVxLcua6qfcms+VX5MvZWvyeJtLA8ki6rX0LM4CcDuADwF4CMD35Z5ZD5+unsXhw4djCGFCFhdddJHZ6F1QamH3nUZpOTTlJVd2aS53CTHIwaqdqkoDKqcYrXxwK12GdEryR6B8UZw/tZvbUsAyT9YzqfOLNMUv09csYUmKliLM1QOXt2fPnkmdtFVwJMt7iKd2IKEsu2dlksdSp980L8/TByS48ZIzyFYTXcjiwwB+CsCZAB4D4HcA/JbjuccCeBeAjwH4KIAfaa5fCuAdAP60+XsJe+aWhpAeBPBMdv1pAO5vfnstgJBLv+ucBd9jQR/t+OSuSn6tPYvScnBZfGCmVniVlFEqKWkpejwbSpMUoDbgpcXmzaNlIXtW+OTqmvJJCpaXJUWIWrmkFW6Rk6UQLbIorScePukiJxWL93gWlgfaNd3UQYKp9smRz2obkCl0IYtdyrV/4XhuG4CnNt8vAPAnAJ4AYAHAy5vrLwfw6ub7ExpiOhvADICPA9jQ/PZeAHswnjdZBHBdLv2uZCHPhTr//PNbW8uE1SKGLnKtZ1PWEYE6/tatW80YML/PY8lpLwMqIbqUUpTWn2eSM0d8JSEHbccwBycLXv6S00gtAuCKqDTv/Jp3v0CqvKVyUvUvZeX6s9fK95xwzGWkNiumlnVrfa3N+V6eMnnRhSwCgBsA/GTz/w4A35p7TpHzOwD+WeM1bIsnCOXBeMKruIXdf09DENsALLPr1wP45Vx6XcNQ5ELzz86dO1tPIJUMEI8sTWG1lat1MG6hpRQK34xkxfZzHZjL0d5P3ibfmmLNzSNohJWrWw+Z7dy5c8pTTcmS4SDeDiXHisj/ORFZ5c/Bm5cSOaXPy/bQSLCtLF7/JXtBNFnab9oYSs1TtBnTXXVBjN3I4t8DeD2AjzX/XwLgfbnnhIyrAPwZgAsBfFn89qXm750AbmDX3wDg+QB2AbiXXb8WwFuNdF4C4D4A9+3YsaN1ZfGBtWnTpgggbtu2LfkinRxSA6TtoC0Z9G0tdCov79Q5BVsKXjdWOl7kQjYpBcVDOJSHrl4bheeITC1C9ZTHo5BKiEjK75oXryyP8eAlxdTcSmk6XElTH0i95U7Ksto3tV9Dy78mq6+69aALWXyg+ftBdu3DuefYvecDeD+A5zb/W2TxeoUsngdgt0IWb8ml28Wz4GRBH9pz0UYZxrh68wZepNLwkg2faJTKPDVoc+lprrjXgysNF6TKSLJS79sule3Nn0dW299SaGuJlnh1pUhZ4p58SDmes52o71G7SyPJUyYr36nyeM+MahvRaIMuZPEeABsYaWzlxJF59kyMw0kvZdfWfRhqaWkpzszMTN6XDCBeeeWVrd1dgtWx+7AGcmm1tSD5NT6ZJztvSdk8xJKTR4OZyIvO7iotKwdXLqnjLWQaOSJOEY9l3ZYqlpL6T72nooSQZH400i9V0vxZy1uVfUCTQffljsSg+qb5FRk9KCkT1QdtoqN3lFj7MDweGv0+MzMT9+3bV3xCdRt0IYsXAbgbwGcBvKpR9i9wPBcA/BqA14jrt2N6gnuh+X4Npie4P4ETE9zvA/B0nJjgPpBLvwtZ8N2n3LNou0phLawD2QlLLFytA0srbTQaTV7ek5qI02R5XW8NPEZNg1wulaR7vCEEnr58z3Zuwx4f1JrS4qENTSFopMvzwZ/RdnpLOZJktOWYqR3jsr0sxTYaTb9TJCcn9TuvI55/y6jgfVGTwdO0ltLK+uO77K15OqvNZb64LC1dTY619Hw4HE425eVk9YXWZDF+FjsBDADcBOCbnc98e1PAj2C8R+NDAA4A2AzgnRgvnX0ngEvZM6/AeBXUg2ArnjCet3ig+e1OrPLS2aWlpbh58+YVhMEHXAmktbhap0XGaK9i0o4R8Fp9Vp75oLA6Kw/tWBYaV2C5jk8KhEhqOBxOrUQZjUYrFH1uIPFniGBIJv21lL2sQx7C5EpPvpI35UHQb3LjmOYRSJKQ5EPl4segyJU7KeuZKyPt3KKtW7fGpaUltU5THor0JOh/nn+vd2l5Fjz/ucl0ftijFYLyegR0lIf1GuYU0XD53OixVketC88C4/0Q5sd6br18urwpj5TDNddcM7Uxb3Z2tpcls6thDRAs2dpAl/fkOrH0TshqTR2FolmBMj3uLcgwgsyPtIylJdjmCHhp2cpypwhTgpclt1HR8qR4GXOWea7OSAnSOx80mamwoKYgKd9SXo4gLEWrkWDpOLMIzyJrDyxvnLzq0ndoaNc1YyqXvqcOuqANWXwS41DQJwH8I4BjAP6y+f5J67n18un6Du79+/fH7du3T5TIOeecYyq9UrQdECWyNStRu4f/r4XJ+L0yfOGRnypryrrmv1ukJncyU/5Ll2RqVmmKOL0TpilrcWHB/0Y+Kx1vX8zVMye4VL1Z3gy1cyo/Wl1oXq/n/lQZtXrlxoCH2KTM1JlhubLmrqfGVIlRWdIfcigmi8kNwC+BzREAuA7Az+WeW+tPW7KQy9z4h78usQ8Fv5oeRol8TZmUWkGlnZwraGsCMHWwXMn1HKwQhkaqJcefWyE4q9yecmhWMw/nlNQjl9lm418Jkaas5NwR7R7iSBlJWj15j/ywPOLSMJN1nb+GldcjD8t5+rPHkPGiC1m8X7mmCltPn66exXA4jBs3bpwQBX3vU7H35TZ2lZ8a/CnLiZMLv9/jPnssQcsK7EKyqbJKmVo6Oc9NWt2aN+otX2o+Q5PFY9z8/9Tb3nKEImV6PCpvmUr7pzzXqa3RID0pLzlLSAWd6xsauAw+16GFDNvmsxRdyOIeAD+B8ca6KzGehL4n99xaf7rMWVDHPPPMM1d4Fqul2NcbPMs0rclIr2chLXDrVM2UcvUMklze+ORmzoLm0GRxsrAUce4IcK1+rTzxNOTE/nA4VFcslbQVn5fyKi/NIqd7U7vjc16MVM4WmVPakiS1PFjtSPdbHqem1FMGUKqutJV3qfBXH95DCl3I4lIAvwDgg83nF3AaT3ATZBjq0ksvVVd9tMVqexVd08hZ1Sn5pemmFJY1YD3PavdoJNjGU0kpklwd8fRS5eNEkAtJcEXCzxbSVj55rHMCjYMXvvCFU+ESLkd6QCmlxsmU2mJxcXEq9Gt5rKm6lQRL55XNzc2tWNgh82vVAcmSSp+v3LM8pRJvWHpvqfBk11c7e9CaLE7VT1ey0M6G6tJAUimkQg1t5edCFhIlceSUtVjqgntJx1MGXq+eOYAcCVreQMng59DCODwNT+yc0pIK2PLOrPu5LO0sL60dKP986S2Xo23QLJ3b4keipDxWK488PzSeiCTJI5JLxj0GjQxXyforWWGVSi9nEGl5WpdkAeCbAPwKgLcD+D365J5b60/XgwRnZmZ6IwtpjfBB0OVMJQ6tE+UGBHd/JaQy5ANbC59w5ZHbeSuVS24g5QY1T7sk/KX9zxWEVn5vyIQglS3Pm6aMrDwOlaWqPE8y/CXvlySohbg0j4Ce0zwUi6Rle1hhk5TsUuPBekYLMWqGjwarjancvM67Gnxe49EzHrqiC1l8GMC/AvCtGL9X4mkAnpZ7bq0/fbwpj++x2Lx5c+sGSllz/FiJLmhjcci0U5aXthNW3k8DMPfeaum6d13yZ3kDnudkXi1PQFp+OU9O8x7k3g+rzazQi9YmfGLUMhY0D8ZShFrb5ax56aVxEinZ0OhtsxLvQOtfnMw8K8WsdqZ+XEoaljEwHK487dZT3tI6yaELWaxYDXUqfLp6FtwTCCHEn/u5n+ulg8vwTl9uZRvrXF5PhVY0q9NKR4uxW662R26uLG0HCA/XaINT3qdZ4ZYStBSSRQRWGT1tkpLF7/MoR60/lljzUpGm9teUGAfedkmVXRK9JwyYa2f+v+ataZB1bHmI8jervJ57StCFLG4F8K8xPtDvUbGDm1zjc889d6JM6HtXD8DahLOaR4B4O1BO2ZfkT6Zp5aF0l2pKcVuWXUopawQmYVmCqXX2WtjIktU27OC1KC0S0+7N7XnI5TFV9tIy8WttPJRc+3oNIE8f8eSHoIUM6Xuqf5wKnsUnlc8ncs+t9afrPgvuWZxxxhmT77Ozs50awlKOfVgEFtoq+j5CQ5RmX+VOeRaWZVdKlrl6kn3ESo/Hti2ZJeX3eA8kJ6VYUitq2uSH6l0uafV4iznvloc/vTvnZVlkO6QMtFIPxlLuKePP6zla+VlttCaLU/XTxbMgq/Hyyy+fKE3uXfSt0Eusl5MBLT+eTluizDzP8IGXeyWpJaukbr0eijxmJGXp5cIYJcqAy8p5KCklRMpdnl9l1WEuP/Lk3zYyrLAV3zORkqcRlCQzT6gmJcdjoNDz3iWuuf7f1xEeJSgmCwDf0fx9rvaxnlsvnz4muM8666wpsqBO0LdC54N3PZAFB3Xm3AqnnJXksbgkNC9vdna2iFRLLLe+PJRceaVC8pKHVddaWrlwhnYIYy6kk1OYbY7DkTJkPqwNk5oca/+INBhSZeHzGKk6sQwLSmtubs5FFilQndB7MU6WbmhDFrc1f+9SPm+0nlsvn64T3PJMevqUvCPBC2n5rCfQoKDOL+dsuNIqPebDgmbBz8/PTy1nTil/Ugy5k10tYsiFFiyl7CVOubKMk4dFVFrZZFqppaoyP7xM2hyDRS5WvrSVcDkDIUfU0lDRPCGtbjQy9YYDechKtoX3dFzNO5Fl9pyHxo1IT4ixL9QwVCG4AgfGO0KtM1vaIGX5dEVpZ8pZWnzASrLUNoN5iKA0pBCjz9KiZ7VBpt1nWY/Ly8txbm4u7t27d7Kxi+7lSoSXWbP2Ncte8yw873zmZdPIjR/5wZVmzmPR6lsrl2fOQDvOIze3YxE1/a69mCgnS5Pj8TA1sqIy507H1cpg1bWch9HqyiIWzzjrgkoWhRiNRvHw4cOTTnbZZZe5Bk1KHm9w7nb3jdLOpN1vWWZWjFYbXBJeErMs2dyApnySck8pXisvdJ1kcCWlhUpyCllTotp9uf5AZaP3Uljem8yj1g9SRKYpqBLjQ3os0gvytIEmiww1K8yVkuUxxnJeUIxlK8VS1/k8TEko0VtvXVHJohCj0ShefPHFE2WxadOmTta/HLirSRZ9ehaaS7+wsDA5f2gwGKhLSDWZXuVF13IhJJk3+j4/P2/uNs7VCcmgkNf27dsn7z72WOryupQnw3i8PlMhPJLDiVCT41E2KWs4FQZLlVP+1mViVqtnb75SMlPk1WY8psKuUl6OkDWZq6kjUqhkUQjuugKI559/fufOKq25PkNPfeRJgzXByCedNcVQ4p5bg8LjIXkUiycMweXxcIOlpKzyaTul5cFzmhxLuUrFQnNH8ogWkmOF1aw6a/O8zLdFRqUeuNZ+Wr7aWNa8D2h13UYxa2EpS57X29f63nohi41wIISwF+Mjyif3xxh/zfPsqYprr70WF1xwAb7yla8AALZv346rr74aBw8ebCVvy5YtOHToEO666y4cOnQIW7ZswaZNm3DzzTdj06ZNmJ+fb53XY8eOTcn14q677sLNN98MAGb6N910E+69995JGQh33HEHHnnkETzlKU/BjTfeiGc84xk4dOjQ5Hf6zq9t2bJFTefhhx+e+mvJ0MrJ65Xa5vrrr8emTZumnj9+/HhWFmH37t245pprcN55562QpeWNZB0/fhxHjx7FgQMHpu7n8m666aaptOm+gwcPrqhD4EQbvf3tb8e3fdu34dWvfjV++qd/GnfccYean+PHj0+1KU+L5B06dGiqHY4dO4bjx49jOBzipptuyvahY8eOYTQaYXZ2FkePHsVdd901Je/gwYN497vfjTvuuMMl684775z8f9ttt2E4HGJhYWGqLg4ePIi77757qk6ojDlQ+ebn53HeeefhWc961kQm/Q5gUn75rNVPaAxcc801OHbs2NTvN91006Tf8PqV7SvTeeUrXzmRvXnzZrXvrRk0BuEfAL8O4I8A/CKA1zWf1+aeW+tPV89CroaiMEKXs+SlldTXru3SOQqCJ/zjec92W2jx/FT+rHKSBWZNaJeExCxLP2XNpqxymU5piEZ6Jjz8ZoWArDrTwokylOcBt9K1FyuVyKO2s+pPk5kLm6ae5Wl6rP5cWTxl7XLPas9PaECHHdwfAxBy9623T5/vs9i4cWP8lV/5ldYbj2QogTprHxPl1rW2MmXIpkv8OZevXOhDU7SpMJa1SkqTk5oPGQ5XHuWRGsypcKKlvFPLKq08URolytiaZ5HKl8rsmX+i+3Pk7Jk/4USfC4em5nRSIRvLAKJ5H21JLC9rqg97iNuz8bFkfmm10YUs/iuAbbn71tunK1ksLy/Hs88+e6I4t2/fPvEwaEB5G1EqiC4KuGvnyVnZmvJrO2BysevcwPQOKu1E3FQ6uTqkttXeQdF2MHPFYZGLR1aOnLyw3tbHDYVcvnIKnD9nKXPv2WCpuhmN8i8FSuWFGy197ZhOjfG2ZN/lnhJ0IYt3AfgSxq9XvZs+jufeCOCLAB5g124F8DkAH2o+B9hvtwB4CMCDAJ7Jrj8NwP3Nb6/1ejl9T3Cfd955U/97JwJj1DuOd2eqJqttx5D5kJaPZ7OZBosULCXeVvFaA9BK3yIWWsVlKdxcWIvLzq1iIsh9KlofKtnUqE34es860uQQiQ0Ggzg3N7fi/Q8e70Sms7S0NNnVLRU03c+NrhQRelYece9E5kd6DzwdmRcKrZUecqnlSfN2Pd5Izrvk6Nv76EIW+7SP47k5AE9VyOJlyr1PwPi9GWcDmAHwcQAbmt/eC2APgABgEcB1ubRjD2QxGo3i7t27J+RAR3+03XpvWbcpa6hvSKWait3ye/l9WoctUeL8Xo9yT+Wfh0+8g0hb405pU9kkeeZ26WpkKOuJiIcUMRkK/Fktv5a3xZUsfddesGTJ5G1Jv3Nyt/JhKXd5fWFhYeqd0lb/56QnxwS/n+QPBoMVClzzWmS5c/9TGXl7plY75SANDq9xmcuXRgopY6ENWpPF+FlcBuA5zecbPM80z13lJItbANzC/r+nIYhtAJbZ9esB/LIn7a5kIY8of/KTn9zZ9SfQIOvj/BgptyRcZFl7MlRGHZ3u16xJb8iIBkMqbOQpX24AWemTtSg3d3FFJUkspcRTRMKfoXT5XJhUIKn6shQEJ30vAfN2579TfWrnb1neEMniITu6V+53ybXNaDQyiVQSIlfgnr6X+197rotnIfsHJ1PPc1rday9P48/1FUJrTRYA/h8AnwbwJgC/hvER5c/PPRdtsvgUgI9gHKa6pLl+J4Ab2H1vAPB8ALsA3MuuXwvgrYn0XgLgPgD37dixo1OFcQvroosu6tXNswZeX3K9+cwNMm2AcYs5RTa5ND1WUGpA87OI+iBxKo/2KlJvqEnK0jywlCfklZO6z1OvmiUeoy9UI79rstoYEQRrbFDeFhcXVbL39L0+7/PKKbnfQ2AWNC++LbqQxYe5NwFgK4AP556LOllcBmADgDMAvArNgYQAXq+QxfMA7FbI4i2etLt6FouLi3Hjxo0TwujzRNjSjnYy5UrCScmUiiJHVqX5s+RxF7/N4Evd6znIzSNHU2K5AW15TpbXpBGJp/1yBkHOi+Rp5AhQa0P5vJa+JGgpR/YBj6Gk1am3HiVSHl8Jcadker2h3G+l6EIW94v/z5DXEs9OkYX1G9ZhGIqHEgDEK664ongyWkOJZb0WsFbJ5I7TTlmihFQ4R1Nm1oS7FvrQ5OSsXv4/v1emo8XJrbzL+uKW8vz8vDqvIPOwsGAf3pdT5lLJWl4E3S/3uPAQEl+AwcshlSsfK1J55s5TsohfzhdIOSkys9pI68tWKNPrWViH/FE6XTx9OV5KIwdt0YUsbm+U9w80n0UAr849F3XPYhv7/mMAjjTfr8H0BPcncGKC+30Ano4TE9wHPGn3MWdxySWXTBFGHw3FO+zJaPhSyA5pHactrR+pbLnikwNLO15bszy1U1C5suJpaEpRU0RcjjffGpHIuuIWthbK4nMV/JReqYipfjRrlys3qbRJhtwLpC0p5dflvBkp5cFgoF6Xcwn0m7ayT0tfGhVS6dM98mVXvHw8TGbtDdH6Mz9JmC9/56HMnDWf8/68+bI8mpzXx70W7fk+0IosGgX9WIxfeHQHgJ8H8H+nnmHPvhnAFwB8DcBnAdyI8W7w+zGes7hbkMcrMF4F9SDYiieM5y0eaH67Eydp6aw8dXTz5s2TA+XaQnNR19rDkB0y51lYv0tlyyc3ubUqFY7lWVg7qLklnFqamZMjCYBONt2zZ8+KdtYsf1l33KIkJcEHsiQuSTSaArIIwdrQJ1eZUZp79uxRFS/lVSNAqeT5damgrLLwdEiO5hHIurI8UPLK5ubmJm3GT1rQDC8rP7KPWm2csu69daJB88a0/ppq474MWIlWZDF+Du/P3bMeP13JYu/evVMdq49XqkrFalnWJxPWYLDCTpqlqFleXBnTh56lwZWzvDQS5WREVjQRhjdUxK+ThSaPd9EIIRWa4HVD5UspREsWr39NseZCI9wip+flCqLUHh8rnJVSZJpCpvaVezY4aXr29fC6I2+HDDmuMGdnZ81QF2/rwWAQZ2ZmJvtsKK9y5Z/Hmpd9PeWhyPrSvA9OPtrY4H2DH9vexYDV0IUsXg9gd+6+9fbp87gPYHxE+Z49ezp7FrxzyXCEvK+Nt1H6rOZZWMeaaJaiNchl2IJWsEhvI0eUludDcrdu3TqlOCxZOeVMFqtUbnKA5pS1pnS4UrLCD1o+U/da5ZHehab4LU9Aqxe5RyalALmnLIlOu4+3mWZIyfbhXqBG9Dwd6TnKdHjZ6V55zpU2Xi2y5MSlEY5Wplx+pCzLq19PnsUfA/gHjMNAH0ETRso9t9aftmTBOzPFc0MInV2+nIXO4e0E2uDt2oHoeRo4sjOnQgb8O3X4ubm5qTfN8bynlKbMD1lbcpOTN5wn60V6FinlLV3/EnKLcVpxWUq0RJ5WHgIPzaVkco9PIwVLCVuQ+cmdUGB5sNwj43lJnVmltT3fMa8ZMZIUUptKZV+TcmQerUUDmrcq28uSJb1VKUf7vy26kMWV2if33Fp/2pIFb1h5mOBgMGjdELLDcAUi4W10TWF07TBy4Eg3POUF8e9cKfLwTKkHJa026e7Le3OWr0ZynmctheWtU6pD6lMybGLBIgWNbC2llyo/JwWp3KSFbMnS8pMzWixFb3lWmqK05nZiLH9HCt+syfu7ZUhwA0IaVLK/W31rNBpNvV6V55tW4FHojYivxCBqiy5ksUP75J5b608fngVZxBs3boxHjhyZ+r2NwkgNij5ktkXOQqEOzI9v8IRkhsMTZw1RXWqWm8eDIkWbeg96yWCxlKe1B6LLQJSKLXfoHUfJmUipMqTIUSrWXN+06kILqXgNHo2Y6LdUeEruitfK5xlrXJ5M00suvA08JxRIL563Kc0z7d+/391f1oNnQauX7gfwp01I6qO559b603XOglvG1GjLy8uTRvQMdELKXY6x39ijN7yTSl/KoLqYm5ubIgxPnvlOa1l26+gNykNuUJd4FjlYVjlvO6tec+lSnvnEvrcPpfoG76OUR8/xGiXhC02ZezwLL0gW9xy0Ok95IJI0rXriCt1qQ77CSrtfjmUum+fZ44Vqsqm89Nri+fl5U2/0RQ4SrclixQPjwwFdG+PW8tPXBDetgtq5c+eEKErJQlot1AmoQ/W1fFaGbLwWthyQXAmR8pETt6k8806cIgRZLymvg2SWHr3hKXdKaXJlaZGJtZJG1oemWHIWr6WYNQKziNTyojwegCfvUmZJ3ZfuvdHk54hK5j0XHksZBXL+imRyAyDnpWr1q5U7R4J9GpkcvZHFWBY+0Oa5k/np27Ogz+zsbOtjxWkyS8rsq7GlBevJI+9wXDEOh/o5STmlUjLYUx6XZcF5Y/Kp/PH85MJq2qoui/CkUtJkabuied1b3p3VZrKM2r4Sa68Jz0POCKC8y2Wrmsxcu0hjxOoXKcKT9ZMjE49XZXmXWltLopfeiJz3y3lu3EsiGdJzzJWlL3QJQ72UfV4G4D8DuCf33Fp/+piz2LFjx6SxLrnkktYutuyIpDAGg0HROuncIPJ0npRbnbL2NDnWYM0N7BLklBmllQvtaHI4IVLbyJNS5XOaMrQ8M5JDv8vBb7Wd9BD5iqFhJsSR6nP0jJZuioxlfmZnZyeeYskqNNlWXE5p/+V1yetXI4GULG35rvaq2JRMzZPgq5y8RCbri1Zk8k2iq+VNcHQhiyH7vALAiwCck3turT99rIbas2fPpEOeddZZxeEizXLUzqWxFFFKXuqaR4ZlIXutXG6Zl1hPEqUegSWblL61YkQqGWmpUdvKdzB4SE6rL22nrbazO1cm7t3xOuek6FFivNxaH0h5eXS/Fob1kKnmxeRCj5ZRw703Ik5twUNKllY/mmeR8pZ4O2pzRdp+Fw8JauE4mUcPSXdBa7KY3Ahs8t67Hj5dPYulpaUpzyIVAsnJ0jqUNZC98lLXctdzrr9l7chOOxgMJtZhab4JFpF4XWw5eHJ1mZPL2ymnCFMyeb4kkaUma+X/8ru2gz61+zjnueTqSXqc1g5+Tx9O9TGZpsyf1RaW8aORlKePWc9p1y3jKkb/62IJmgwiJe0FSqvpYXTxLPZgvDHvz5r/nwTgF3PPrfWn65yFPP7h4osvVidpvdCsT4tI+kAXa17eRysz5PJXbj21Rc7qKx0M3rJ5ZfHB2nZgSoUql1WWlFWSo7Vpq8Rj075bYSnL2+LXcxsdc6C0+ZEglkxPe1sb8XLErRkOMrRIdZYqr0yrZL8S/423r7V0tw90IYv3YHyY4AfZNfXY8fX06UoWfDPMhg0bOrN4ihC6WgklHkdOBu/I1DmJJGQYpZTkSu4vIbQ+B4vlVaVWtnjDDJx4vMdo5PLFl3LzurAsfS0tHtrhFjyFeWTd5qx8voiDh8u8dUf5ofkjKgfPp4RnPwp/jrcrrycr7dFoNDUOJCxjkHtJ2koqre96PCGZ/1wfLUEnsmj+fpBdc738aC0/fa+G2rFjRyeFZLmZlqXRRXYb61obPFQHMzMzK5RJG2LrwxOx8t2XS96mLj11YinYLvmScXErnVxIhivT1L2yv0pC4nnK7UeSq8y0ccG9G268cJl0L5Gw9krY1HNy7FF+iBj4ZDffmS0h+wn3jkiOXBTAdYxWfo/xYHn9XdCFLH4LwF4AHwBwFsYroo7knlvrT1eyGI1G8YILLpgKQ/Vt/fcVd7SszhK52uDRFFKqI+c6ubT+2ljU2u88/NGWdL3EnfLiSjbutfH8pOxUeMXqZ1q4w3tib4pIPaEWDmuzpicco5VtMBhMDpXkZbW8Iwvcs5BzTSV7fCwPhbebPFqkTd/l6XQxODm6kMUWAP8JwF8A+CKA3wCwOffcWn+6kkWMMR45ciSeccYZkw7Y56tVY+w3vr5acktklZJU7n5OVh6ZnvSt8njznrqvxMvxlF2Sg3zGIoGcHJmH3HESHiL11l+OoFKhJg3SQ+FLhHl7aMo0Rfyj0WiFl5FrK05KkvR4G2getuURyrazPBhvfXnQmixO1U8XsqBGuvrqq6dCUVqs8nRFG8JpYy2n7uehsZzMlFLUZFpWtiedlGflyYMmxwrxpEJiqZBRKswj89Bmj4pH8aZkaQQ3GqVPY07Jk/tjpLzc8tpUvXgP8JNejeWVpTxCHtrjcq13ja+GwVlMFgB+MvF5pfXcevl0IQvZ+PSRMd0SrEajrqZn4t2huxp5axM+SFm2VqjKY923DbmV3i9j3JRfLUSRs4hj9IV55LO5M4j4HIK0gq2yeglOWtyade4hV0vxa55Mzsv0lI/+l/3Vo9Rz1zjZ5TzIPtGGLH5c+fwkgE8D+Kr13Hr5dPUsBoPBZBUUcGK3qec0SQ25DtgGpWGfUrnWZiVCquN2yRsna+8KD09epMWaIwPPO0e8+ZPhAiucoFmxsi65LKsM1uqglDWdCvlphMvJw8pvqp404rAm6619F15jpmT8eWWVEkFOjpWXrvMapegUhgJwAYCfAPBJAK8G8A2e59by05Us+PK/DRs2xMXFxUmH3blzZyvPYjjsvmZfylwtz0JTZHLOJhUvtfLmybNVV128Ou/rYKWiSpXPsxmOwMnAKpsVn5ZKgtd7KSlbMkmOda6Ylo7HiMiRkPaMvKaRUq79JDxhuZJ88bbytkHOO9I8KA7pia0WcbQiCwCXAviZhiRuBXBJ6v719OlCFjxezC3crpvnSq3R9QKLFFLWsIVcuMijwEsHpUeWZi3m5h+kMrNkafdq5JvLk1YWTz41WNZxLpzTlvy7KDZPGXN9g36nuQ3vBltLrlTcnvdhW94Nl+U1wFYjUkFoE4a6HeNXqf4bAOdb963XT59kcfbZZ8cXvehFxQNS4mTEG9sgl6+Uosh5SxoBWAM/Zzl5ydrr3pdYpimUyNK8nK55KvUuUrLXYx/1eCeWxyR/l/MuqTqTpwRr8uh6bg8R9XvtLCsuK0UWMm/SU+qr7dqQxdcB/C2ArwD4G/b5CoC/sZ5bL5+uYaj5+fl4xRVXrPAw2oaP2lqAOZl9dA5PDNZKVwtRaRYQrzePxcwtJ/p4XxrkrZe29ddVfltSaKvgPZ5WW/R1VI0n/OI5ej9HnKn3q8i88N32uTzm6oFHFVLRBUtPWJ4x9yzaGA0aOs1ZtPkAeCPG+zIeYNcuBfAOjN+49w4e1gJwC4CHADwI4Jns+tMwfkvfQwBeCyB40u+6z4Iqnn9mZmaK32WhyesrDJXrHG2VUE4uJwhNgec8hJLQhQzblIa9UvBacRIeskvdJ5/x7iXhCqdNyEmbPE4dwOdBbh6vi3ckvQWPsZBLz1OHMmS1vLxstlPpGPOcceUxsDRPas08i64fAHMYv1WPk8UCgJc3318O4NXN9ycA+DCAswHMNOGvDc1v78X4MMMAYBHAdZ70+9hncdFFF00U1aWXXtrJu9BCNl0b1zswSvObc+m5Atdipn1a9tzS8p4sW1oOL1nk5Hk9NE0xePeSlJ58bD1nySo1QJaXl5MrBL19UKsr+Szf99AWJSEtLS+plV9diFGT4yGCvrwJjpNOFuM0cZUgiwcBbGu+bwPwYDzhVdzC7runIYhtAJbZ9evhfKVrH/ssdu/ePeVZXHzxxXEwGHRibivU0odFkEqrS/40a8YKqZWk2YXMPCE96fpbnoB3SWJfnhyX4wnjcLltiVirM+taqWWeeqbtb9rvbT3B0nRLnsl5BF5Z2jWrvJoB1afuWC9k8WXx+5eav3cCuIFdfwOA5wPYBeBedv1aAG9NpPcSAPcBuG/Hjh2tK4saY25uLgKYOvKjz0PwZKilD2+jK7gysJZXpgZDKpzQhVjahngkuaVe7pMjAe+5R7n8WwrGIsE2pKopde+1XDm6bti0ypWaUC5Np9QQSMnwPpvq523mKy2yoHrrgzg1rHeyeL1CFs8DsFshi7d40u7y8qPhcDg5D2bjxo0RQDznnHOmXm/YVm7OGi9VDH2TSyqsYCm83Hdv2MjCaKTvkygZgJqX4VUosk36Cj1QOWTbl1juFiylnrqW8nBT3k1Jn03J4cdtt7HOOXJepQceI4IbIdbyWd6uJeN6OByqr17WxkOfWC9kse7DULxh+afNRjxLbsrqLlUMJRZ2qVJN3U8EsGfPnqkjzDVZ3jCb5Z5zJcLJgk+u5k5M1YhB1l3OA9I8C4s4Uu1KikAeZ0LXtaXIOatVOxpFy4OmgKQnrR2YqZVTq49cOVOe59LS0uSd03K1koegNE+SH/PhHQceT1L2S/5d6orRKP1WSS1fVD4y3HhEwzKU+jIc1wtZ3I7pCe6F5vs1mJ7g/gROTHC/D8DTcWKC+4An7a6ehbZ0tguLcxebd8Iulg/lN9dB2lp+9L/WMYksUvWTUp7efFI6c3NzU3mge+lYahke9JBeqYWshQUsrzAlS9adZ0WSJU8aN6k2pnvpXRP8vQw5OVo5NetWqwPeP3Jlo7qRhCnbMyWHjylusHg8KK2uU/2SVkiSZ0F1K+svtQ9Dm7jnBCqX+lp9oa85nZNOFgDeDOALAL4G4LMAbgSwGcA7MV46+04Al7L7X4HxKqgHwVY8YTxv8UDz2504SUtnY4xxz549vZGFNohya677QheLw/KIlpeX49zcXNy9e3fcu3dvdkmx1wOSxGQNgJSbTr97N7/lrufkEfhkteUlLSycWAI6NzfnPv7FIt6UZyGfJSOF3gK5b9++FUaB5/BGWQ7uicg64B6LVW+83a2TXrklbxGGtoeC7qF+dOBA+p33o9HK3dhaW3LCt4wH/n/qXRjUB+bm5sz+x8eB1U9PWbJY60/XpbMLCwvxRS960aRD7N69u5NC1wb7yToYrAssz8IiEQttd1+nlG5q0MeYnyD0hHnkM571+VZ+uJUrwyUlfSCVjtbPZD1xxdvWo41Rn9/S8uZpe1k3HsL2eACpetHS4P0655V65g08RpJ1bhXPp4cITukw1Fp+uoShaCCdc845vXgVXPZ6JweJlBVeoug8yjaVniZLe4eBN++pEEbKOvfmPUVy3rCXhdQbBzUPlivgPg0VHjKSlrQnhMbh7U8pC7uP8UX58Hh8pX3Ck7bVP06m7qhk4QQ10ObNmydEsXHjxri4uBhj7CekIzvgeiQRGWrQyNKjEPl1rzWbqw+vZ1GCvqxtgldBtmn3lEWdI6y2BKXBG/bwlnM166wU3j7o6fulz+QIPZVOH/VTycIJqmyK69JndnY2xlg+2LjF5N3568lfn9aUJpvyxN80JleHaMo6FeLxvmGtSzihiyXXtS5lW7cJMXny4c13qXIqUXKe50rLVyqvbb7b3OfxCnPkqXl6Uo7HaEmNjz6MgUoWhRiNRnHbtm0Tsnjyk59sKqgUeAzUcidLDmNLWZWpsnjyrLn4fMUW/aUJy8FgoCoeriRznT9FLlp957yzEm/DO7BGo/Gk59zcXBwMBiYByLZO5TVlQLQxSCR5eEMpufR4+52sM80s5PqCt69ImZ4zn6T3phkCVohMGku8j8r7UsvBed4sQ6R6FmtAFjHGeNlll015F23YWjas1oFX27PwyrcsI9nZacMiX3pppafFtVPlyeWd7perSzSrrcTyTNUrJwGrL1A789VElnwuT1NUuZ3MMv98BREnCfKIvXI04iLZ9D761AS0Vq+8THyVlOf9DxLUl2gZtfTecuSh5ZN7zrydePg1NYZ5f+RL47XluzxfmrKn+2j5berAzD48CAuVLFqAHyT4nOc8p5dJQSsM0DZk0TbNkvvk9ZwnxJcwdt1pailcuW5dy3vOC+EypUciFcJgMIgzMzPx8OHDajt5QxDSMpWkx8vG5aXKxxUePTczMzMh9VS+NBLjhCCXdfJyWn1Fs45le+Xe/8DlEQmTR2t5b57+q3nMvH5knVI5tbbjMkjBz87OTuootSdE6y8km5bsE0FbHp/XKCpFJYtCjEaj+NznPnfSmTZu3FgU4rBkroal0Hdn4TK1jYMpSzTGlZujSjbGpcDrKLUiyPI+NDnUptyKk+SthSpkvq3Bb4Ui6DnZ7tK6lEqMKyAZyiKPhCvAHFlzz08Lk1j1LMeCvH7mmWdOkYGU4z1AkSt0Iq25ubmJt+TpP5LgeN+Unldqr4b0KrgsPsdJHp5l/HESlu0ivVjLI9ee6cvLqGThhHRDZUflg7NUOacatYvC79pZUlYrraPnVqIccJblLutJu16S95RXxq1r/tKanGehKXluYXPZ2gF8pIBycw85JZtSVnQfL4/lyWhER5Z5yiPKWcK5NChv8/Pzk131W7dudZ+mK//nMim8o71ljteZltZoNJp4WEQKZEhoZ6B5SX80Gq0IraX2gGjGl2ZEEeGTd6GRvFZvfUYmKlk4IS07+ZGubwlyFnlbdPUstPLIcELOs/DkQSOZlMIoybtss507d7res+whIG1gW8qfgysTHt5IDWqrLWR9p8JeUnnwOrLIxZoM19pHkjBvV8pXboJWEp6sS+lJyXR4n5NvtOPpaKEmLku2BxmJfHe7Nd6lMcDrXJI+D02lvG3L0015o6k8tkElCyd4B9u0adMUUVAHaquccwOzbX67Eo9FYnSkR1/HedAAWlxcNC1BjxwZgqHvNNCt83ly+eL54UZDagWURTbcO6VnPatuPMo5ly73FugeOZHrKQevB6nQORlYlrcFkjEYDKZ2LlMalhzL8xgMBhPjQMu7RtBWG3EDwfL+tPxQWrRRlOTK86hyhiLVDW8ny0ux6qYrKlkUgB9jwD9d3tAVo/8ogTYy23o5VthE5pevfJKD1rOslMtLhYdSIROCVIYyX6WemzZZLsNPJXXMlRS3aD11bslKHVmuWeoyLGGRjhXCkXXJZXvCe6lrXCFKkrDkWda1POtJ65v79++fWPiyb0jvhpNLKoyq1S/3cvbv35+915IrjckUWfUZfiJUsigAdZrzzjsvAieO/dD2FZRgNRq3rUWRCnfwTkwrUDhR8me54s69B0M7QVPLU045a6GCGMv2q3DI57g1bZFXqt5zpCWVg+cQQK4wrfBNLsSl5ZmHGjVi0SzinBeihUd43jSlqylma45GjiOeH36fLJ8WBqK5i6WlJdNzkcYTD21Jha71cZknyyjznoor69YKR7ZFJYsCcHd0w4YNkwYpDW+k5PflMraV782DZu1rg5PO6k9ZvDmX3OsZyMFHz/LJ+C5l18IL0hPMeQSW9Shlej0XTYF7rP1cuVOHAVontKYsXc2z0JSrTDulmKXCTYVl5HHffO6A+jG33PkkuuW5UJ/nxCb3iXADg7eNbCeeX76KjY8xr2fM9VQlizUiixhXvnOAvIsu4SOpPLuSjoXUYGoDXhfS6knJlgNIhjRKwhm53yktWomTqtuS+rHCN6XP8DQ1ZZHzUnidaXXvqTNvfVty2/Zfy6MmRTc7O2saDp6yap6upjxlfxyNTkxEp058jXGlgSLbk8onSU3Lr1aPXF7OEOFo602nUMmiEKPRKJ5//vkTJbl9+/bOjUKdw1rp0JfHYYVVcp1Ps8Lm5+cny/j27t2bfTcBQfNI+G+aZUX3WQMxFf/1KF4qo9wdbdWP5eloyi/njZRY4/y6pnys+k6V3/KQNEWmKXYuIxfuypWNftNCnLnntPLy/ObSzP1mnelleQdaf82NbU5avG/n9IDWxiXE4kUlixbgx32cccYZRfFEDZrV09aiSKHUs6DfydLTJvdJSeRipJol7LX06X5tMFix4pQlqUHbOWwpWqs9eJ4sz8FSuFbZSYlLS1c7hl1T8DKkJecIcu+HpvR52YhUSc7y8vKKzYIaNMvZstS1vUtWvVtLkSk/pcaWNER4nmVf9Hh/FrkORZiQG1IlKyMpb/yZvgxMDossNqJiCseOHcNdd92FQ4cO4eyzz55c/5Zv+RZs2bIFDz/8MG677TYAwPz8fJHsm266CZs2bcKhQ4cmaR0/fhzD4XByDcDU9zag5+nvli1bVuSVl/Ouu+7CzTffjPn5eZx11ll45Stfibe97W14+OGHJ/efd955uP766/HmN795UhYNJGs4HGI4HOLhhx/G7t27k2U6dOgQjh8/jj/8wz/E0aNHsXv3bmzatGnqNwC4/vrrsXv37qmyWeDl27Jly+T6HXfcMfWX18/tt9+Om2++GcC4bSnt48eP49ixYxM5PE+yrim9TZs24eabb8amTZuSdX/o0CG8+93vxtGjR3HXXXdNvj/yyCNYWFjA8ePHcfToUTzjGc+YyKE6pvRe/OIX4+jRozhw4MCkXEePHsW99947qcfbb78dAHD33XdP5efgwYN44xvfOEmfyvaud70L9957L+69914AmMh56KGHkvV98OBBvPSlL8XRo0cxHA6xsLCAa6+9Ftdeey2Wl5cndUs466yzcNttt+F973sfjh49OsnT29/+doxGo6l6J7kA8KY3vQkAMBqNJnmkejl+/Dg2bdqEgwcP4u677578lX3hrrvumoxlSp/6LfXFRx55BPfeey+OHz+Om266CXfeeSeAcf8nWdr44n2C2nDnzp04ePDg5DlqkwMHDuDgwYO4/fbbp/Io+zCNx8c//vH4ru/6rsn1Uj3UGhqDnA6frpvy5ufn4zXXXLPCuu7iWVhp9elCtknba52UhAeku52KR8tnuUWaSsMKGcjyeaHlq20bpepKyuThP88rPbU65ruc6cM9i1w9yfAUP8GA5hS8cqQ8eVaUFjbkR7NonhuvJ+4dyV3UmlebW6rNPQs52Sz3gGj58vQF6VnIfS9aP5PXSr3otkANQ/lAnU++fxsYH87Wp7u3Gi7kaqZtKZZUGsPhynXjFol48yYVkzZwPaG31LyDV04bWCGZNps1pYKkT0kbaeWTp83m8pSaM+HLU3OhvVQYT8urpkBlXnLzWDw9TpLaHIQ3vGjVDyeG3JyWFm48GfqikoUT1PmuuOKKFWRBE3FrqeRPBlKWf+lSPa4IZafPeRAWrPOTSqBZiV7LLTewS/sHpWsdSe316EjZ0cSx3K2dUuhWngaDwYoFASVyYpwmd+twR1p44HlfCO8vuTaTCl7Lu5yn0eouVW7NQ0nVieYNtWnzVDm7oJKFE9JCo8+FF144dRBZH9ZbG5wMSzcVopKDM2f9pAazNvg8A6+P0JA2uLxkkQsZlOZPW6GVS88q33A4NI8mJznW5kkuh8Jh0tLmcrw78eXheSnPQqaV6w9eL5Tqgi8AoGteZW+Vm+uMkgUdqd3nss1Lylk6LiQqWTihDTj+oQFUEjKghqSVLZpF7CUBTZF3OZhwNFq52WtxcdEMG+SW5Uor2ZM3HpbiK55yFnJJeXNeUYllpil3nifv2ncthJQiVYuQNTm0akl6dNqcgsw7Xw1HdaKF6yzlzVfNefuqRjByHHJjrcTilsQ3Ozsbd+3aVRwtoHuWlpamzkzjcr2HV1I7UN7IgyMyp+XrVO85g4HKyY9vb4t1RRYAPgXgfgAfoowBuBTAOwD8afP3Enb/LQAeAvAggGd60uhjn8ULX/jCqU5Px39wxVriWcilqZZl6pElPZy2FoVm5fBdtSmvQ+aHKwrvKab8GqVPio4riq7WkrQwrd9LyN+63zOwFxam33BXEt6zCJqHjKyjsLX652RNbU/vefCQJ5cpPfPZ2dlWikvz8GmXtaduNYKUxgivbytakKovzWNJ9XfNg5HlpE2lBw4cSHr4WhpcVpfxsh7JYou4tgDg5c33lwN4dfP9CQA+DOBsADMAPg5gQy6NrmSxvLwcN2/eHIHxkR/01jx6E1abAWDF2tvGG72ehaejUWcmy8myjFOy+IBrE0KS5UmtdvLUh7XChZc1pURT6aReC5qrbysUUmp8UPnkabtU/0NlcYElj9/LVy15lBUnG5KxZ8+eFfVTUkZSfHRAJcnK9XnZv+h/KpM0aHgf0RS+ZhwNh8M4GAzUlzCl9upwkuFtND8/Pynj4uLiFOF7xjev/zZ6ROJUIIsHAWxrvm8D8GA84VXcwu67B8CeXBpdz4bSNqZ53mfcBiVW7VrJ90wq55RBihS9isRLVtpfSwm0CWu1qVOuqDZv3hwXFxeL09VkHT58eGKRyrOWvB4wlUc78E+GQXJhMOtdIiV1lmsTS5bsY5ycNW84Vy7ZX3NlsMiFh9bkacZy7sIiPKuvtum/Kaw3svgkgA8AeD+AlzTXvizu+VLz904AN7DrbwDwfEPuSwDcB+C+HTt2tK4sapyZmZn4xCc+MV588cXxxhtvnGrQPtF3Y6+GfHl6Z5s6SA00ryIp9Uwsy8xSGDl08eZk+EIL87WRRR6Fto8hReraERNWH/coWGm1SzlWWb2er7f+udGgeZEEOfck7+MWu6wnTZZlTFGdUsiLl0E7XNOaI+J13+WMuhzWG1lc3vz9hibENJcgi9crZPG8XBpdPQvu3gOIO3bsaH1olxbq6dLQbRVMF7lUduvFRZpFV2IBeRXJahBrbiljG2LR7uMhDB4CycmzrH6q71TYMCVPs7hzbZirD1KM2jJgS57XiOBkJOd25BiTm+C0dPlBhlpZJIFaXtpoNIozMzOTsJnMk3zBUqo/5/oDL9tqEca6IoupDAC3AnjZegtDDYfDuHfv3glZbN++vVXoIUZ9EjkV1/fK09xqgrRmcuW1rErvIOSWrnW0NaGEdK1ySPe+C4FIZSDzLRWWFkpro2g9/9O11DsULIWcy4OsN1nXvNxam2l1YdUPlyct/lIPUCMj2W5LS0tTbznU+uJgMJjISylyORkt5fB87dq1azIPwYlNXrN0iKznXH8o1UUerBuyALAJwAXs+x8BeBaA2zE9wb3QfL8G0xPcn8AqT3DLcAGauHBbq5Y37vz8vHmGf4m8nDVSQhbS3bYOdZODXSqT/fv3T1YwpWLl2mF+FqxySHLqMnB4Gh6PKEUoVj5ShKyVS/6uKWFJYlK+bNdcv7XCJdKaTeXVUmSj0XhRAH8njHzeY1WT0pWnH8s24uRqlZ/Ky8kndaBhinzJsJQnP8jFAjmPQHqRVn9ZDQ+bsJ7I4upG+X8YwEcBvKK5vhnAOzFeOvtOAJeyZ16B8SqoBwFc50mnr5cfcbLoAurofHKrq0VsWYj8N49sqYg0JaRZqprVp52SKpHzLLzWccqzKC2/da+HPLQ6lDKsCWdZVkuGlm6u/jUCKS0rQdtborWjNCjkdf5byopO1TtX8pYx5NkRzuube/yeupKg5/mZUvJ02FTb8jxxovMeB98n1g1ZnKxPH/sszj777EmjnX/++Z0bjQYMf9l9F6QssVLLwyIIC5biylnOpXnxpt9GhiVLU8Jd4sMpGVzJ5xYPeNqb4uPa8SqatZxCyoiw8uMhd+kZpepF8964grfapLQ/csNLC621GQ9t5PBnve/i7huVLAqxtLQ0RRY5l9YDy0W37i3toBwlSoHL8u5tSIVLLG/Hi5TyttL3POMtiyTOrmGunNexsDC9YTOnAFMeF7e6NSVs1ZPWXtwb0IwIKz8lStEKWWleY5s2Jcu8pD+m2t+bB218cO/DWz/8vlKDri0qWThBHZU25NFn06ZNyRiiR+7CwnjDW+ocIEKJdd1XGIYv5fOETDT5JEO+plJLzztYeIjAmgzNkVcbL4n+z53d5EmHzwdo91oTyNZ98uhsadHmXpAj61bzajwkR9d5+fj3HFKGAC+HVm+pOpdK3vJiUnXDvSk5T5fzNDW5VC9tThe28tXWgEmhkoUT3JrinwsuuKDYutHkcmWc6nCedKTMth2H5JDCsCbXPB2U7062FI1XmXCl4Xk3gVZfXQcVVzptQ0Qx5ldceWXy+k2FWXKKxapbb7+WMjWy0E5u9UASmaw3bYGFJoP3P82L8XiNHjla3i0DbjicPv/KazTJsdR2Gb8HlSycGI1WTm57lHsOfJ8CXzXUxbpIdcxSmdrRzDlLLmchW4M5RRa50EZpWXPWZ85TojLMzc2Zx3vk0pH14i2LJnNxcTFu3bo1Li4uTu7RYuNSwWneB//d60Hw61YavD9ZnpQHy8vLcW5uLu7bt29Sb1ZISKYhQzZcSct7UnOIWpqpusqdjaaRtNdo4KSpzU31hUoWBRiNxsv8+DstLrzwQrdrrUF6AW1XOayW+5kLk7TJS07xa/L78AS8iomnJdMlObThLRVW8+RJs97blFVuJNMsXa5cNNlaW/NQnxUSssIq2pgo8aSsTZFaOXKkLldaUftp9WF5CZ4wlFYWnl8iJjnGiWz4qsgSz0KGslZDF1SyaAG+ZprWUbclC25NdgkbtbXSSuR6lVhbxdyXzC7pyLQsy7T0jKVUnuS8gJdAOeT7JaQyI7kpz0JT5JalmyKTNh6i/C3G6X03mkdgKVzpTVlzFJZxZs1FyTzkvGm6Lo8It/qi1mYe8HKXHrJZgkoWLfDkJz85AoiXX355r42zWgq/L7RRYtqzfcksSY+TctfQXB9yvLJSCkrK4ife5upSs55lW3AFZMni77lIWfmetpVKlHsWuaNX+PMpj4DXuTVurc2hbfqnzFOKsNvWmyTn1dIjlSxagM572bhxY/L9waVYzckpidWw1LuGkdrUo6VwLes2FT7xppVSwH0PUs9cDyH3eyqM0laedxLc07ap/k/P79+/P/lOFG0OwgIP3UjPwspHjrA1z4Zfk/Wg9cfSaIPs621Cxx5UsmiBI0eOxBBCBMZHSvfhXYxGo6njPlYbbQlOKufUMQjac6n66WK5SevWIgsZPpFkl2pHq3yWzNIya94QV8ZtJr4J0gNo2x6aIvPG17VyagRm9R+5YtDaa+Ht29RevAy5eknJ5vI8hM3/5/1VrvArXY7LSVDr521RyaIF5KtVaTNNHxOdW7dujUtLS71YBDkrqDScooUu+FvdrFhv27JYyluGEqg9tBVbVvm0cICc8LTCMlIWDUg5eWn1CV5PfPWKHNj0Pz83yaqnFOEsLJzY3Ef9K9cvPOTVhih4uaiuPFaw5jXw+uWeUi5sxsNZw+H0ng3PklmrX1meilUPmjySkSuH1c6yL7fxoC1UsijEaHTi2GH6UOdo2yiaddxHaKvv0A8PBdCA5UsCLcuqbVly8vgkpTWhmgq3aMqGl037PWVR0sDMTcTKM37ouxzYnIS81q4V5vAeVOmRVXp+mZRjkUUK2n2Wgkz1MzkfwduIK+pcubT+kDNQtDJIOZwUU+G0VNukjIYupFHJohDUKBs2bIjA+P3bS0tLnWRqoZ3U6zlL5XpcWE8oTQu3cEvNsqzadtScvNQR1gRtAlbLV8rq47/ndsVLOZpSWVpaijMzM3HPnj1xaWlphYfjSZOnrU1sa3VjydP6gPye89JSHljOCs55E6Q8c6SSmmvg+5m4Z+HdnyFlpTxo6SHSde39Fdo7R7iRlAvr8rbRvAkrT21QyaIQy8vLcceOHdnGLIGlYPqKNXqQS5Pn0RqUXlLwkliOvLgsS5HRAN23b18v+fJawimZvK6lHI1c6JoWUuCKJbVXI1U26alJryLnLfHnLW/QWzdavlLhFE6W1p6X0UifD5TWuLYHQyuD5p1whSyX46a8Vi2cR+XYu3fviv6fMkY0YiBZg8Ggehaln65kIVk/hNDZs7Bc0Zyi7BO5NDXlQQPYslQ9YRMLOaUsB4Y1uHmYLAdP2KeULHheef1YO5ktD2c4XHnaKM+PnNfIKRXNm7EmjPl3TYFLqzbVjzRZVtgn1ye5Z0BtrPVHSkfuyJZ1RLI4sWhtIo0lPh6sPSnWbm9txVOqj+WMAFlnJf0/h0oWhZBkwTtpzn21YD3TRtZqQVMelqLOkUEfFnyOsAglXhC3DL3uv6dttPooDbFYilULIXmVdKqNUvXGCUFatTJkJfMlSatkI6rsg/SsPHJF1pU3pLS4uBh37twZFxcXTaKV+ZB1lSJ7rQ60uk4dUJkbO3Lc9Lkcv5JFIUajUdy1a9dksG3cuDHeeOONqsvulWdZ5anwQ6nctvek7kspiBJPQ0sn9YzH4sylbylwLxGk2tmTPpeRmqC3ZJbmScrKea7WxrRcnrTwCicmb93kypciT8sLS5Uz5Z2mvDTt/1S+S+S1CUO38Xy9qGRRiOXl5XjmmWf25lmkOp604Es6jkdxeJVL2/s0D6SLsvPWh1QkKWJI5aft5qycJyEVZR+7wb1l8vbPnEVaYkDwkItmdbcxECwLPnWMh/YMt+JlGM4i1FLCy5VRk5cjcwul+qcElSwKIfdYXHDBBb2tWJKdhE+4rZVnMRqN4mAwiLOzsyvmZnKDhntG1rp8r7KQ8lIDWb7TuXQA0f3U1nQ4n7futMHOw5daeKQkj22f4/nQLM+2Vn4uj6lnNG9Bs+ytVUtanqzVb1bZU3ltY2SkZFsekff5tvnoA5UsnKDGoYMDzzjjjAiMl9D2dTyH7MyeUMBqgw8aygeRmLb6hFuW0hLU3Hyv1yJlWyEDmd82g4lk8P00soy5WLssF7UtJzGuOLzel5Sd8ppSG8c0svB6RDyNHLHTPTQ3o8lJeR5cFs1NpFY9Sa+Gy6OyW5snebjXKrdWrpQHoHnb3o1/PG+l/W81UMnCCWo0WqZ3zjnnTBTJ7Oxsp1ACdQq+G3phQV+DXSKvS164tyBPzaSOSnsF6L0CMaatO02JtbGSadDTpKaW35I9KlqZqYyzs7Pm6aOp1TWWxyjLTrH1+fn57K5oq/4sL0aL26cUnDZRyz0iTTFZ3iU3FnIyNIUqy8e9RR72TUESo1YmfhIsD29ahKsp8pzil0RZSrT83uXl5al6qJ7FOiQLq5PkJsc8kJYN77RtUGKte5/l1/lGN2nFD4f5M/ktV9zronMlwElcHriXClflymxZb1TGubm5FRa6p+6sshC5eXdXp65zC1oqpZRVqnksVh1qCjYnI+dZyFCN9L40guZ5kW2cCjtJhSvzJ9O1iFLmXTMYcmQpkZov4hPymnfoqZe2qGTRAsvLy/Giiy6KAOIVV1yRdKE9sKyzto3cp2eRyuNwOIyDwWDKwpfuvJWGtdvUQ7ragJQEVTJIvWWOMb2CqUSOVheaJdkmnykZKUIu8fIsMknJ0MjOImopIzW+LBJNlWFpaSnOzs7GwWAw1U+kF1I6rnm9ePeeeMsTo++97x45bXDKkwWAZwF4EMBDAF6eu78PsqDOdeaZZ8YjR450lsfRtzWQk9smPSuUMRqtDFnlZMhQSdv8WEdeWINUS8dTR5oSySnrXJlS96fSa2Og5Mot69Ijx6uUrHJZXkdJmaidtTZPkT4PLWvkbxFhroykI7i36zlSx8pvSX/1/l6KU5osAGwA8HEAVwM4C8CHATwh9UwfZDEYDKbmK/pEztLqU27qukeW9pxcV++R03YiWsrRQiHeZ1LP5fKZS79UmfLQjhYO8YRILNm5/PO2teYGLC+zRNlbabb1AFP7OqxwYu594DxffBFCaX1Lb7SNtc/rS+snJwOnOlnsAXAP+/8WALeknun7taqDwaCzvBjTbm8f7mTfnoW0PknO0tKS201uY7lpkDHeEgtQs9S0Sc1c7LqLZ0HgIQyPZ5FaACHTTPUhaeVTaNFadVRCtBakl+EhGw+ZWzvGc2E3K4/c+82FV3Nl5cfRazJS+dHIp21+2uJUJ4vnA/hV9v+/AHCnct9LANwH4L4dO3Z0rjRaf3/11Vf31lCewXyyOoUHba3nlJw+ZPSB1fLucihNI1Vu+ZtHtveZNiGRPtDG4OkjX32VLSen1AM92frgVCeLFyhk8brUM31NcPd13gphPRJCCm2t55ScPmT0gVOlLfpWkqdKuU9XrPf6t8gijH9b3wgh7AFwa4zxmc3/twBAjPHfWc/s2rUr3nfffScphxUVFRWnB0II748x7pLXz1iLzLTA+wA8LoQwE0I4C8D3Abh7jfNUUVFR8ajBxrXOgAcxxn8IIdwE4B6MV0a9Mcb40TXOVkVFRcWjBqcEWQBAjPEogKNrnY+KioqKRyNOlTBURUVFRcUaopJFRUVFRUUWlSwqKioqKrKoZFFRUVFRkcUpsc+iDUIIIwCfbvn4FgDHeszOqYBHW5kfbeUFapkfLeha5itjjFvlxdOWLLoghHCftinldMajrcyPtvICtcyPFqxWmWsYqqKioqIii0oWFRUVFRVZVLLQ8StrnYE1wKOtzI+28gK1zI8WrEqZ65xFRUVFRUUW1bOoqKioqMiikkVFRUVFRRaVLBhCCM8KITwYQngohPDytc7PaiCE8NgQwrtCCB8LIXw0hPAjzfVLQwjvCCH8afP3krXOa98IIWwIIXwwhPDW5v/TuswhhItDCL8VQlhu2nvPo6DMP9b06wdCCG8OIZxzupU5hPDGEMIXQwgPsGtmGUMItzQ67cEQwjPbplvJokEIYQOA1wO4DsATAFwfQnjC2uZqVfAPAH48xvjNAJ4OYNCU8+UA3hljfByAdzb/n274EQAfY/+f7mX+BQBvizHuBPAkjMt+2pY5hHAFgB8GsCvG+ESMX2fwfTj9yvwfATxLXFPL2Izt7wNwTfPMLza6rhiVLE7gWwE8FGP8RIzxEQBHAHzPGuepd8QYvxBj/EDz/SsYK5ArMC7rm5rb3gTgn69JBlcJIYTtAJ4N4FfZ5dO2zCGECwHMAXgDAMQYH4kxfhmncZkbbARwbghhI4DzAHwep1mZY4z/C8BfictWGb8HwJEY49/HGD8J4CGMdV0xKlmcwBUAPsP+/2xz7bRFCOEqAE8B8B4Al8UYvwCMCQXAN6xh1lYDrwFwM4Cvs2unc5mvBjACcFcTevvVEMImnMZljjF+DsDPAvgzAF8A8NcxxrfjNC4zg1XG3vRaJYsTCMq103ZdcQjhfAD/DcCPxhj/Zq3zs5oIITwHwBdjjO9f67ycRGwE8FQA/z7G+BQAx3Hqh1+SaOL03wNgBsDlADaFEG5Y21ytOXrTa5UsTuCzAB7L/t+OsQt72iGEcCbGRPGfYoy/3Vz+ixDCtub3bQC+uFb5WwV8G4CDIYRPYRxe/I4Qwm/g9C7zZwF8Nsb4nub/38KYPE7nMu8H8MkY4yjG+DUAvw1gL07vMhOsMvam1ypZnMD7ADwuhDATQjgL40mhu9c4T70jhBAwjmN/LMZ4B/vpbgAvbr6/GMDvnOy8rRZijLfEGLfHGK/CuF1/L8Z4A07vMv85gM+EEB7fXPpOAH+M07jMGIefnh5COK/p59+J8Zzc6VxmglXGuwF8Xwjh7BDCDIDHAXhvmwTqDm6GEMIBjGPbGwC8Mcb4qrXNUf8IIXw7gN8HcD9OxO//LcbzFr8JYAfGg+4FMUY5iXbKI4TwDAAvizE+J4SwGadxmUMIT8Z4Qv8sAJ8AcAhjA/F0LvNtAL4X41V/HwTwgwDOx2lU5hDCmwE8A+OjyP8CwBDA/4BRxhDCKwAcxrhOfjTGuNgq3UoWFRUVFRU51DBURUVFRUUWlSwqKioqKrKoZFFRUVFRkUUli4qKioqKLCpZVFRUVFRkUcmiooIhhLA5hPCh5vPnIYTPNd+/GkL4xVVK80dDCN/fg5wjIYTH9ZGnigqJunS2osJACOFWAF+NMf7sKqaxEcAHADw1xvgPHWXtA3BDjPGHeslcRQVD9SwqKhwIITyDvQfj1hDCm0IIbw8hfCqE8NwQwkII4f4Qwtua41QQQnhaCOF/hhDeH0K4h45jEPgOAB8gogghvDuE8PMhhP/VvINidwjht5v3FPxMc8+mEMLvhhA+3Ly34XsbWb8PYH9DQBUVvaKSRUVFO3wjxkeefw+A3wDwrhjjPwHwtwCe3RDG6wA8P8b4NABvBKCdCPBtAOQBh4/EGOcA/BLGxzYMADwRwA80u86fBeDzMcYnNe9teBsAxBi/jvER1E/qtaQVFahkUVHRFovNYXX3Y3w8zNua6/cDuArA4zFW8O8IIXwIwE9gfIibxDaMjxLnoDPJ7gfw0eYdJH+P8ZEdj22u7w8hvDqEcG2M8a/Zs1/E+MTViopeUd3Viop2+HtgbM2HEL4WT0z+fR3jcRUwVvR7MnL+FsA5muxG1t+z618HsDHG+CchhKcBOADg34UQ3h5j/KnmnnMamRUVvaJ6FhUVq4MHAWwNIewBxsfChxCuUe77GIDZEsEhhMsBPBxj/A2MX/bzVPbzNwH4aLssV1TYqJ5FRcUqIMb4SAjh+QBeG0K4COOx9hqsVOSLAH69UPw/AXB7COHrAL4G4F8BQAjhMgB/S29Mq6joE3XpbEXFGiOE8N8B3Bxj/NOOcn4MwN/EGN/QT84qKk6ghqEqKtYeL8d4orsrvgzgTT3IqahYgepZVFRUVFRkUT2LioqKioosKllUVFRUVGRRyaKioqKiIotKFhUVFRUVWVSyqKioqKjI4v8AdzP5fIJ1OSkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(net2,\n",
-    "                         monitors=['exc_group.spike', 'inh_group.spike'],\n",
-    "                         inputs=[('exc_group.input', 20.), ('inh_group.input', 20.)])\n",
-    "t = runner.run(100.)\n",
-    "print(f'Used time {t} s')\n",
-    "\n",
-    "# visualization\n",
-    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['exc_group.spike'],\n",
-    "                         title='Spikes of Excitatory Neurons', show=True)\n",
-    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['inh_group.spike'],\n",
-    "                         title='Spikes of Inhibitory Neurons', show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee0ef0f9",
-   "metadata": {},
-   "source": [
-    "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in the toolboxes, where the use of [runners](../tutorial_toolbox/runners.ipynb), [monitors](../tutorial_toolbox/monitors.ipynb), and [inputs](../tutorial_toolbox/inputs.ipynb) will be expatiated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d31c4afc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
-   "autoclose": false,
-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 1,
-   "hotkeys": {
-    "equation": "Ctrl-E",
-    "itemize": "Ctrl-I"
-   },
-   "labels_anchors": false,
-   "latex_user_defs": false,
-   "report_style_numbering": false,
-   "user_envs_cfg": false
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorial_simulation/neuron_models.ipynb b/docs/tutorial_simulation/neuron_models.ipynb
deleted file mode 100644
index 9f82aba1f..000000000
--- a/docs/tutorial_simulation/neuron_models.ipynb
+++ /dev/null
@@ -1,565 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "118e3b1d",
-   "metadata": {},
-   "source": [
-    "# Building Neuron Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c68cbca",
-   "metadata": {},
-   "source": [
-    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](https://github.com/chaoming0625)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f783d7fb",
-   "metadata": {},
-   "source": [
-    "The previous section shows all available models users can utilize by simply instantiating the abstract model. In following sections we will dive into details to illustrate how to build a neuron model with ``brainpy.dyn.NeuGroup``. Neurons are the most basic components in neural dynamics simulation. In BrainPy, `brainpy.dyn.NeuGroup` is used for neuron modeling. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "aac4b858",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import brainpy as bp\n",
-    "import brainpy.math as bm\n",
-    "\n",
-    "bm.set_platform('cpu')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d38f2b7",
-   "metadata": {},
-   "source": [
-    "## ``brainpy.dyn.NeuGroup``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6444c5ce",
-   "metadata": {},
-   "source": [
-    "Generally, any neuron model can evolve continuously or discontinuously. \n",
-    "Discontinuous evolution may be triggered by events, such as the reset of membrane potential. \n",
-    "Moreover, it is common in a neural system that a dynamical system has different states, such as the excitable or refractory\n",
-    "state in a [leaky integrate-and-fire (LIF) model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html). \n",
-    "In this section, we will use two examples to illustrate how to capture these complexity in neuron modeling."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9520e950",
-   "metadata": {},
-   "source": [
-    "Defining a neuron model in BrainPy is simple. You just need to inherit from ``brainpy.dyn.NeuGroup``, and satisfy the following two requirements:\n",
-    "\n",
-    "- Providing the `size` of the neural group in the constructor when initialize a new neural group class. `size` can be a integer referring to the number of neurons or a tuple/list of integers referring to the geometry of the neural group in different dimensions. Acoording to the provided group ``size``, NeuroGroup will automatically calculate the total number ``num`` of neurons in this group.\n",
-    "\n",
-    "- Creating an `update(_t, dt)` function. Update function provides the rule how the neuron states are evolved from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2993080",
-   "metadata": {},
-   "source": [
-    "In the following part, a [Hodgkin-Huxley](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.HH.html) (HH) model is used as an example for illustration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3095ec6f",
-   "metadata": {},
-   "source": [
-    "## [Hodgkin–Huxley Model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.HH.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5170763",
-   "metadata": {},
-   "source": [
-    "The Hodgkin-Huxley (HH) model is a continuous-time dynamical system. It is one of the most successful mathematical models of a complex biological process that has ever been formulated. Changes of the membrane potential influence the conductances of different channels, elaborately modeling the neural activities in biological systems. Mathematically, the model is given by:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "    C_m \\frac {dV} {dt} &= -(\\bar{g}_{Na} m^3 h (V -E_{Na})\n",
-    "    + \\bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t) \\quad\\quad(1) \\\\\n",
-    "    \\frac {dx} {dt} &= \\alpha_x (1-x)  - \\beta_x, \\quad x\\in {\\rm{\\{m, h, n\\}}} \\quad\\quad(2) \\\\\n",
-    "    &\\alpha_m(V) = \\frac {0.1(V+40)}{1-\\exp(\\frac{-(V + 40)} {10})} \\quad\\quad(3) \\\\\n",
-    "    &\\beta_m(V) = 4.0 \\exp(\\frac{-(V + 65)} {18}) \\quad\\quad(4) \\\\\n",
-    "    &\\alpha_h(V) = 0.07 \\exp(\\frac{-(V+65)}{20}) \\quad\\quad(5) \\\\\n",
-    "    &\\beta_h(V) = \\frac 1 {1 + \\exp(\\frac{-(V + 35)} {10})} \\quad\\quad(6) \\\\\n",
-    "    &\\alpha_n(V) = \\frac {0.01(V+55)}{1-\\exp(-(V+55)/10)} \\quad\\quad(7) \\\\\n",
-    "    &\\beta_n(V) = 0.125 \\exp(\\frac{-(V + 65)} {80}) \\quad\\quad(8) \\\\\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "where $V$ is the membrane potential, $C_m$ is the membrane capacitance per unit area, $E_K$ and $E_{Na}$ are the potassium and sodium reversal potentials, respectively, $E_l$ is the leak reversal potential, $\\bar{g}_K$ and $\\bar{g}_{Na}$ are the potassium and sodium conductances per unit area, respectively, and $\\bar{g}_l$ is the leak conductance per unit area. Because the potassium and sodium channels are voltage-sensitive, according to the biological experiments, $m$, $n$ and $h$ are used to simulate the activation of the channels. Speficially, $n$ measures the activatio of potassium channels, and $m$  and $h$ measures the activation and inactivation of sodium channels, respectively. $\\alpha_{x}$ and $\\beta_{x}$ are rate constants for the ion channel x and depend exclusively on the membrane potential."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84f438ae",
-   "metadata": {},
-   "source": [
-    "To implement the HH model, variables should be specified. According to the above equations, the following five state variables change with respect to time:\n",
-    "- `V`: the membrane potential\n",
-    "- `m`: the activation of sodium channels\n",
-    "- `h`: the inactivation of sodium channels\n",
-    "- `n`: the activation of potassium channels\n",
-    "- `input`: the external/synaptic input\n",
-    "\n",
-    "Besides, the spiking state and the last spiking time can also be recorded for statistic analysis:\n",
-    "- ``spike``: whether a spike is produced\n",
-    "- ``t_last_spike``: the last spiking time\n",
-    "\n",
-    "Based on these state variables, the HH model can be implemented as below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3ea88e6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class HH(bp.dyn.NeuGroup):\n",
-    "  def __init__(self, size, ENa=50., gNa=120., EK=-77., gK=36., EL=-54.387, gL=0.03,\n",
-    "               V_th=20., C=1.0, **kwargs):\n",
-    "    # providing the group \"size\" information\n",
-    "    super(HH, self).__init__(size=size, **kwargs)\n",
-    "\n",
-    "    # initialize parameters\n",
-    "    self.ENa = ENa\n",
-    "    self.EK = EK\n",
-    "    self.EL = EL\n",
-    "    self.gNa = gNa\n",
-    "    self.gK = gK\n",
-    "    self.gL = gL\n",
-    "    self.C = C\n",
-    "    self.V_th = V_th\n",
-    "\n",
-    "    # initialize variables\n",
-    "    self.V = bm.Variable(bm.random.randn(self.num) - 70.)\n",
-    "    self.m = bm.Variable(0.5 * bm.ones(self.num))\n",
-    "    self.h = bm.Variable(0.6 * bm.ones(self.num))\n",
-    "    self.n = bm.Variable(0.32 * bm.ones(self.num))\n",
-    "    self.input = bm.Variable(bm.zeros(self.num))\n",
-    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
-    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
-    "\n",
-    "    # integral functions\n",
-    "    self.int_V = bp.odeint(f=self.dV, method='exp_auto')\n",
-    "    self.int_m = bp.odeint(f=self.dm, method='exp_auto')\n",
-    "    self.int_h = bp.odeint(f=self.dh, method='exp_auto')\n",
-    "    self.int_n = bp.odeint(f=self.dn, method='exp_auto')\n",
-    "\n",
-    "  def dV(self, V, t, m, h, n, Iext):\n",
-    "    I_Na = (self.gNa * m ** 3.0 * h) * (V - self.ENa)\n",
-    "    I_K = (self.gK * n ** 4.0) * (V - self.EK)\n",
-    "    I_leak = self.gL * (V - self.EL)\n",
-    "    dVdt = (- I_Na - I_K - I_leak + Iext) / self.C\n",
-    "    return dVdt\n",
-    "\n",
-    "  def dm(self, m, t, V):\n",
-    "    alpha = 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))\n",
-    "    beta = 4.0 * bm.exp(-(V + 65) / 18)\n",
-    "    dmdt = alpha * (1 - m) - beta * m\n",
-    "    return dmdt\n",
-    "  \n",
-    "  def dh(self, h, t, V):\n",
-    "    alpha = 0.07 * bm.exp(-(V + 65) / 20.)\n",
-    "    beta = 1 / (1 + bm.exp(-(V + 35) / 10))\n",
-    "    dhdt = alpha * (1 - h) - beta * h\n",
-    "    return dhdt\n",
-    "\n",
-    "  def dn(self, n, t, V):\n",
-    "    alpha = 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))\n",
-    "    beta = 0.125 * bm.exp(-(V + 65) / 80)\n",
-    "    dndt = alpha * (1 - n) - beta * n\n",
-    "    return dndt\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    # compute V, m, h, n\n",
-    "    V = self.int_V(self.V, _t, self.m, self.h, self.n, self.input, dt=_dt)\n",
-    "    self.h.value = self.int_h(self.h, _t, self.V, dt=_dt)\n",
-    "    self.m.value = self.int_m(self.m, _t, self.V, dt=_dt)\n",
-    "    self.n.value = self.int_n(self.n, _t, self.V, dt=_dt)\n",
-    "\n",
-    "    # update the spiking state and the last spiking time\n",
-    "    self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)\n",
-    "    self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike)\n",
-    "\n",
-    "    # update V\n",
-    "    self.V.value = V\n",
-    "\n",
-    "    # reset the external input\n",
-    "    self.input[:] = 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d523fb3",
-   "metadata": {},
-   "source": [
-    "When defining the HH model, equation (1) is accomplished by [brainpy.odeint](../apis/integrators/generated/brainpy.integrators.odeint.rst) as an [ODEIntegrator](../apis/integrators/generated/brainpy.integrators.ODEIntegrator.rst). The details are contained in the [Numerical Solvers for ODEs](../tutorial_intg/ode_numerical_solvers.ipynb) tutorial.\n",
-    "\n",
-    "The variables, which will be updated during dynamics simulation, should be packed as `brainpy.math.Variable` and thus can be processed by JIT compliers to accelerate simulation. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "215292d2",
-   "metadata": {},
-   "source": [
-    "In the following part, a [leaky integrate-and-fire](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html) (LIF) model is introduced as another example for illustration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04d7d580",
-   "metadata": {},
-   "source": [
-    "## [Leaky Integrate-and-Fire Model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f45c7805",
-   "metadata": {},
-   "source": [
-    "The LIF model is the classical neuron model which contains a continuous process and a discontinous spike reset operation. \n",
-    "Formally, it is given by:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\tau_m \\frac{dV}{dt} = - (V(t) - V_{rest}) + I(t)  \\quad\\quad (1) \\\\\n",
-    "\\text{if} \\, V(t) \\gt V_{th}, V(t) =V_{rest} \\,\n",
-    "\\text{after} \\, \\tau_{ref} \\,  \\text{ms}        \\quad\\quad (2)\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "where $V$ is the membrane potential, $V_{rest}$ is the rest membrane potential, $V_{th}$ is the spike threshold, $\\tau_m$ is the time constant, $\\tau_{ref}$ is the refractory time period, and $I$ is the time-variant synaptic inputs. \n",
-    "\n",
-    "The above two equations model the continuous change and the spiking of neurons, respectively. Moreover, it has multiple states: ``subthreshold`` state, and  ``spiking`` or ``refractory`` state.  The membrane potential $V$ is integrated according to equation (1) when it is below $V_{th}$. Once $V$ reaches the threshold $V_{th}$, according to equation (2), $V$ is reaet to $V_{rest}$, and the neuron enters the refractory period where the membrane potential $V$ will remain constant in the following $\\tau_{ref}$ ms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f3f7d32",
-   "metadata": {},
-   "source": [
-    "The neuronal variables, like the membrane potential and external input, can be captured by the following two variables:\n",
-    "\n",
-    "- ``V``: the membrane potential\n",
-    "- ``input``: the external/synaptic input"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76fa0aa2",
-   "metadata": {},
-   "source": [
-    "In order to define the different states of a LIF neuron, we define additional variables:\n",
-    "\n",
-    "- ``spike``: whether a spike is produced\n",
-    "- ``refractory``: whether the neuron is in the refractory period\n",
-    "- ``t_last_spike``: the last spiking time\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50fbecbe",
-   "metadata": {},
-   "source": [
-    "Based on these state variables, the LIF model can be implemented as below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4961244a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class LIF(bp.dyn.\n",
-    "          NeuGroup):\n",
-    "  def __init__(self, size, V_rest=0., V_reset=-5., V_th=20., R=1., tau=10., t_ref=5., **kwargs):\n",
-    "    super(LIF, self).__init__(size=size, **kwargs)\n",
-    "\n",
-    "    # initialize parameters\n",
-    "    self.V_rest = V_rest\n",
-    "    self.V_reset = V_reset\n",
-    "    self.V_th = V_th\n",
-    "    self.R = R\n",
-    "    self.tau = tau\n",
-    "    self.t_ref = t_ref\n",
-    "\n",
-    "    # initialize variables\n",
-    "    self.V = bm.Variable(bm.random.randn(self.num) + V_reset)\n",
-    "    self.input = bm.Variable(bm.zeros(self.num))\n",
-    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
-    "    self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
-    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
-    "\n",
-    "    # integral function\n",
-    "    self.integral = bp.odeint(f=self.derivative, method='exp_auto')\n",
-    "\n",
-    "  def derivative(self, V, t, Iext):\n",
-    "    dvdt = (-V + self.V_rest + self.R * Iext) / self.tau\n",
-    "    return dvdt\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    # Whether the neurons are in the refractory period\n",
-    "    refractory = (_t - self.t_last_spike) <= self.t_ref\n",
-    "    \n",
-    "    # compute the membrane potential\n",
-    "    V = self.integral(self.V, _t, self.input, dt=_dt)\n",
-    "    \n",
-    "    # computed membrane potential is valid only when the neuron is not in the refractory period \n",
-    "    V = bm.where(refractory, self.V, V)\n",
-    "    \n",
-    "    # update the spiking state\n",
-    "    spike = self.V_th <= V\n",
-    "    self.spike.value = spike\n",
-    "    \n",
-    "    # update the last spiking time\n",
-    "    self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike)\n",
-    "    \n",
-    "    # update the membrane potential and reset spiked neurons\n",
-    "    self.V.value = bm.where(spike, self.V_reset, V)\n",
-    "    \n",
-    "    # update the refractory state\n",
-    "    self.refractory.value = bm.logical_or(refractory, spike)\n",
-    "    \n",
-    "    # reset the external input\n",
-    "    self.input[:] = 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9b54438c",
-   "metadata": {},
-   "source": [
-    "In above, the discontinous resetting is implemented with ``brainpy.math.where`` operation. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b80959f",
-   "metadata": {},
-   "source": [
-    "## Instantiation and running"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05818ebb",
-   "metadata": {},
-   "source": [
-    "Here, let's try to instantiate a ``HH`` neuron group:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7afcd4ff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neu = HH(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6be8d3d",
-   "metadata": {},
-   "source": [
-    "in which a neural group containing 10 HH neurons is generated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9d2604b",
-   "metadata": {},
-   "source": [
-    "The details of the model simulation will be expanded in the [Runners](../tutorial_toolbox/runners.ipynb) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.DSRunner` and `brainpy.ReportRunner`. The variables to be monitored and the input crrents to be applied in the simulation can be provided when initializing the runner. The details are accessible in [Monitors](../tutorial_toolbox/monitors.ipynb) and [Inputs](../tutorial_toolbox/inputs.ipynb). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9a291f2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "runner = bp.dyn.DSRunner(\n",
-    "    neu, \n",
-    "    monitors=['V'], \n",
-    "    inputs=('input', 22.)  # constant external inputs of 22 mA to all neurons\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00385de1",
-   "metadata": {},
-   "source": [
-    "Then the simulation can be performed with a given time period, and the simulation result can be visualized:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f102b056",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "732ae8e9ff8c44cab255e67c1ccc1de8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner.run(200)  # the running time is 200 ms\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93208ac2",
-   "metadata": {},
-   "source": [
-    "A LIF neural group can be instantiated and applied in simulation in a similar way:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "929d85e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8d86a285e764a9288e5fbf26cac018d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "group = LIF(10)\n",
-    "\n",
-    "runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 22.), jit=True)\n",
-    "runner.run(200)\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
-   "autoclose": false,
-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 1,
-   "hotkeys": {
-    "equation": "Ctrl-E",
-    "itemize": "Ctrl-I"
-   },
-   "labels_anchors": false,
-   "latex_user_defs": false,
-   "report_style_numbering": false,
-   "user_envs_cfg": false
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorial_simulation/overview_of_dynamic_model.ipynb b/docs/tutorial_simulation/overview_of_dynamic_model.ipynb
deleted file mode 100644
index 93104ff93..000000000
--- a/docs/tutorial_simulation/overview_of_dynamic_model.ipynb
+++ /dev/null
@@ -1,900 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Dynamical System Specification"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    " @[Tianqiu Zhang](mailto:tianqiuakita@gmail.com) @[Chaoming Wang](mailto:adaduo@outlook.com)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "BrainPy enables modularity programming and easy model debugging. To build a complex brain dynamics model, you just need to group its building blocks. In this section, we are going to talk about what building blocks we have provided, and how to use these building blocks.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import brainpy as bp\n",
-    "import brainpy.math as bm\n",
-    "\n",
-    "bm.set_platform('cpu')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Models in ``brainpy.dyn``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "``brainpy.dyn`` has provided many convenient neuron, synapse, and other models for users. The following figure is a glimpse of the provided models.\n",
-    "\n",
-    "<img src=\"../_static/dyn_models.svg\" width=\"750 px\">\n",
-    "\n",
-    "The arrows in the graph represent the inheritance relations between different models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "New models will be continuously updated in the page of [API documentation](../apis/dyn.rst)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Initializing a neuron model"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "All neuron models implemented in brainpy are subclasses of ``brainpy.dyn.NeuGroup``. The initialization of a neuron model just needs to provide the geometry size of neurons in a population group."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "outputs": [],
-   "source": [
-    "hh = bp.dyn.HH(size=1)  # only 1 neuron\n",
-    "\n",
-    "hh = bp.dyn.HH(size=10)  # 10 neurons in a group\n",
-    "\n",
-    "hh = bp.dyn.HH(size=(10, 10))  # a grid of (10, 10) neurons in a group\n",
-    "\n",
-    "hh = bp.dyn.HH(size=(5, 4, 2))  # a column of (5, 4, 2) neurons in a group"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Generally speaking, there are two types of arguments can be set by users:\n",
-    "\n",
-    "- **parameters**: the model parameters, like `gNa` refers to the maximum conductance of sodium channel in the ``brainpy.dyn.HH`` model.\n",
-    "- **variables**: the model variables, like `V` refers to the membrane potential of a neuron model.\n",
-    "\n",
-    "In default, model *parameters* are homogeneous, which are just scalar values."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "120.0"
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hh = bp.dyn.HH(5)  # there are five neurons in this group\n",
-    "\n",
-    "hh.gNa"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "However, neuron models support heterogeneous parameters when performing computations in a neuron group. One can initialize *heterogeneous parameters* by several ways.\n",
-    "\n",
-    "**1\\. Tensor**\n",
-    "\n",
-    "Users can directly provide a tensor as the parameter."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([114.53795, 127.13995, 119.036  , 110.91665, 117.91266], dtype=float32)"
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hh = bp.dyn.HH(5, gNa=bm.random.uniform(110, 130, size=5))\n",
-    "\n",
-    "hh.gNa"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "**2\\. Initializer**\n",
-    "\n",
-    "BrainPy provides wonderful supports on [initializations](../tutorial_toolbox/synaptic_weights.ipynb). One can provide an initializer to the parameter to instruct the model initialize heterogeneous parameters."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([50., 50., 50., 50., 50.], dtype=float32)"
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hh = bp.dyn.HH(5, ENa=bp.init.OneInit(50.))\n",
-    "\n",
-    "hh.ENa"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "**3\\. Callable function**\n",
-    "\n",
-    "You can also directly provide a callable function which receive a ``shape`` argument."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([52.201824, 52.322166, 44.033783, 47.943596, 54.985268], dtype=float32)"
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hh = bp.dyn.HH(5, ENa=lambda shape: bm.random.uniform(40, 60, shape))\n",
-    "\n",
-    "hh.ENa"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Here, let's see how the heterogeneous parameters influence our model simulation."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "outputs": [],
-   "source": [
-    "# we create 3 neurons in a group. Each neuron has a unique \"gNa\"\n",
-    "\n",
-    "model = bp.dyn.HH(3, gNa=bp.init.Uniform(min_val=100, max_val=140))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "4c2540451765416785d74c0665dd6531"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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yM5gTATDV528uFkIsEkIs2r59+6C/2/bkUSMmydP8LumNhFNZQJH8sgJkLGUeFCACqAe6I7sGimThfFgKisjONIm1t8R47aBy3B6bR4CyoPwitSj54Iy5jOSrBF9rt2usaxxvb2rtGnPlbFJkPaUNXJNZrXaNYYAScJIXQrQDdwBfllLWvBNJSnm9lHKelHLelClTBv39EhDS7IUZl6DJfOU/aDHYyiIZVUEU7RpDCLSoZdcEiADqgS5Nkk+UkryD/HSpkzfybpXXAkreDirbY8t6BF+dK5ackXNtGjIP0IokHyBP3nVPUr3uumtlVon8CpZce1Nr16gewqMW2Dajr/3mhaAreSFEFJPg/09Keaf18lYhxHTr/enAtkZ8lx8kpvcJEDdAY3SRvL3sN5VFUSkAZGJW5kWLkrxhlAZeS0g+ao1Tyzj82hZS8i67xjvwqgiFuJUlVbbEdwZeA3SNS62oakRYSImNpNAMzbUz1AU78Bpra9p4DemenOuxawzDreRrCtoaOijDk+zYiOwaAfwJWCal/IXjrbuBc62fzwX+OdTvqgQ7uwYghiDP6OoM5SQLYSt5i+SzUYvkA6Ty6oEu8SQLp10DmEt8l5IPDuH5wQ4q28t+LyI0pIGCQiJiNrwpszIM3RFcD841LthsJbEU3+NLJm1fy8blyTdXyVe6bn5wJgwA5IwarOOAK/njgU8CJwshXrL+ezfwM+BUIcQK4FTr92GDBBSb5KWCJurLbQ06zCwN29stLnsB0lErT74FSM8L5o5Xc8MQlNs19sOS0TIOlde8oFw9MFcpzgCetyevCIWE6kPy0jDTCSFQ11gvWYFVVfJSRyAK4/S1bJwZVFq6KUXZ3DGw+loAOvcDQI3NvA192Dz5yFA/QEr5FFjsWo5Thvr5NZ+HkIWTiKHQR+3Lq1aAS1kIt12TjdiboYJDAPXAKLE0yrJrIg7l57RrjDzoGqhDvo2HDc7dvOCtdg3DQFXUgpIv2yhk6BCJgxIJ1MRWbkVV9+RVURynr1/tnMgBtCxEEw0551pRqKc0CJL3XIFWQ8CVfCDgVPJxVPJitJG809KwtsrbD4uimjdICwQivWAv+0sfqELhLqcicqo8CPyYDWsFVi1PvrKSt1ReJBkoJW/HUgorsBrsGlfsoRYlD025xqWB17pI3pqk7C51NQVfWyG7ppmQUhZq1wBEhYomjLp2qQUdLmUhDAQKCSUGQEZIiCZbWsmb3q572e/MxgCLFJx+LQR+zDYRxiP+ZGGm/yrEIz6BV0MzJ/FoIlBKXqszA6WwYvGbzAoHOlZr0JRrrA8h8FpmM9aSRil1EAENvAYBeV2aSl7YgdcoOUVW3WbdStCdm6GwST4KQFYaEEkEXtX6oeDtRuwAXnntGrAUUdlSPthjLgSVYxU8eUNHURR/r9oIqJIvIcJaPHlFVJjMbBTKGjRPyRslgdfBKHmXzVj1C4fPkx8VJK8bEimK2TVxJUpOVF8+thKcBZOEkObDYpF8WkiT5AOuav1gSFAUQURViKmKZ548YO4Gde54hcCP2Q4qV/J2K3rVUgIykEpeNyQRRRCPKAiBb5VNG6WpolWVfGFvQBOUfJ3poe6/tewaK+utpnLDoSdfGXnDcKVQxoVJ8gN1Vo8LMmxlEY/YefIKCcUqDIU0CSDgqtYP9qYaMGu8ZPxSKLVssftVqyj5klWK17K/1Kt2lTawCU9RrdVacCY1Oz1UeHT18jxe6uZkVtWusS25Jip5iRVLGZxdExGR6uN0fWGo5CtC001P3rZr4mqcvCLoy46e0gY2WSiKQFWkh13Tup68bUWBmUaZ9tkM5c6Tb41SDvZu3oiqEFWFJxGa967io+St44VixV2CM6nphoFqzc5mSd7KRFhQ8lXtGkdZA2jKNTasekrxSP158rrUURW1uGKpFnh1rtaGAaOE5A13do31P7c3E/xStLXCDk4CqCoIFOLCJPkMRksred3a+g8WWWjedk1Gt/LklUixvnrAx2wHXgESEe/a5LpRIbumoOQjwVPyEiL22GpQ8gVbqtbAaxM9eVt42JlR9dSuKVzPSJX9ADacq7VhwOggeduTt0gwoZoE0J1uzaqMXtAdZBFRJEiHXWMHXgOuav0gJQUln3C0ANQN3eVVmymUjkwTCPyY7VxyKO9ha6PUk3d5uPbKRVEDp+QN5+Qcq82uqSmFUuqAMMcLTVPyimMCq3czVF12jXO1NgwYHSRvZ9dgXxTzf25vuqt5J9VgGJKCb62qAIK4MEk+Iw3zgQiQyqsHupUeCm6y8CQFo7WUvL0HAOx4Q5158gUCCKIn77DZov7tDW3UvBnK0MxrbJN8E8ZcOra6yhoYGopSgy1lI1Ty1WEHXpXCRTGX9/0DHU08q8bCqSxUxZzSIkgiUpJBDxwB1AMnETrbrRnSIKJECv+ZgVcrQFVQ8sEmeemYnBMR716oNvlFlAiKUNykYAeaC0o+ONdYM5xqt3ovVM3Qat8MZQeaoWkk71bydRQoq9WWsuGcyIcBo4LkdUNa+SbmRUlZUflMtuaKx4GHU1moikRKAVInLiVZqQduKV8PpNPScKgmmxQAEmrCfFicuz8h8GN22mzJmHefVzsgKYRZ18VFfs6lfMD2QhhS1u3JR5RI9RRKaViWXPOusSGLz1s8Ul8zbzuLqOpkVviyUMlXRV43LE/eHE6bRfIDo4zkbWWhKIBUwNBJGJKMbHElL91K3lm7RrXUTVyNm151qScf8DE7x+bn7dokD5CIJNw2hpMAAqbkSwPmtZY1EEIQV+MV7JpgKHnX5DyI7BpVMVdnVe0aO5soVPL+0HS3km9LjAUgk+1t4lk1Fk5lUVTyBgkpyRp64AigHtj51uBeGtuKCCzyswuUOT35gCt5V3ZNVCVdwZMHczJzKb9C4DUSSCWvWlZULUTonMzKxuk6UCtOatCksga4M77qzK4p3Lf2CrQSQiVfHXtNSiEVSFkbF9qSZn5ttkV7nnrBqSwUxWp2aGjEpSQjtcARQD2we7yC2efVGXhVlZKHpaDy4oBoCSVfzCVXPIOTzhVLIpJwKz+nXxtNmgSoB6NXgqZLV558rZuhoAr52bs/1WjTCu+Zmw/Nn+v15J3jjKvxGpR8mF1TFeNTMTQgaZWcHRM3lXw239fEs2osnMpCERJpCDD0IslHk6DniqqghVAaeHWmUBaUXyRerCevqCBES8QhjNKgsocn71TyZeRXuuMVAjOZ66WxlBrLGoDjenoe6Nj92aQVqlFqIdZp17jst1DJNwY5AVHM/0nJxDjzNW30KHmnslAcgdeENMgamqVsCbyy9YIuS7zdvI6Uskz5FapQ2t5lC8QhaiFCKaXbxnApeYdf20T7wgtGmW9dWe2WKtyqSh6atkItza6ppw6WHWCGGu2aMLumOqSU5IGoNRPGY6aSz+vBUDyNgNOuEcLAkKaSTxTsmmARQD0wHJlDiZjd8Noo9+Q1hycPLRGHKI03eCnC0nF6e/LBVPJOtZvTDTTdn+gNw0BRiiuWyimU1jVu0iY/ZwwsEVU8G7D7wZkVVnHFUviyUMlXhS51pBDErFouibip5LVRRPLO5aMiJIahIA2duCFNJR8NFgHUA+dGL2cHJbv+ODgCdXZQDloiDiFdxddUslUCrxXtmoApeVcGil3Iq0J5b1e2VKRado1FTU0q11E6tnqUfM2xBxthdk115HSzEFnUXiLZdo0RjIehEXApecWsualpeSu7Jt/SSr40FQ/MglCa1MofFmmUKPlgk3wpWXipXd1wBJgrBV6DpuQNR558Dd2hKk5mTrjsmmZ58pSVbKi1CZEu9YJdUzGLqPBloZKviryRByBqKXklmiJmSDQ5eqpQOpW8EAZIhWw+7wi8BosA6oFzaezsDlUaqCvmyVu3bSRY9dW94Io3xLxbAFZMLXQp+WDV6ykNmEPl5hqe+x48D3Rack1U8o4VmCHN5kS1/a1HwkAlhNk11ZHXTDKPqWY7PKIpEtJAkzXUcW4ROJWFEGazw3zOVPIZPeeo5dJ6YzZKgpNgkoVu6OUBrFJPPuCBV1e8wYcIK6dQ2mUNglevx5Aedk0FknftB4hUCLyWWnJNahqilNyTtWbYuK5nXXnyw9OQfpSQvFlS2FbyqFEScnQpeaeyALMKZc5S8lkj3zK1XLygG8UKok6yKEtF07JuAmgFu8ZjAiu1NCp6uHbgVSiBU/LO2jX2KqUSEZaRn58gscsaQNMmctfqss5m3pqhuWNJVQOvjuD6MGB0kHzeInlbyQtBXIJOvoln1Vg4lYUQ5v7enJY3yxoYeaTaGtv8vWC4NkPZRGgUOuxA0caQsrVSKA1Hdo0fWbhSC63lfcH/deXJB0zJG+7aNVDZky8jv4rZNQ5LrsllDert81q6Mqs5hTJU8v7IWzd91CoIBBCXAo1g7AxsBJzKwlTyRbtGIslHrBsk4MrWC16qKe2h5AEzk6jlAq/mz36t5EoDkhJJzrBWoa4dr8FS8rrPdfNDWY2eSoHXJl9jQ3onA9SCurNrDMc1HgYMO8kLIc4QQiwXQqwUQnxjOL4jly+xa7BIXrTe7k8/OJUFtpLPm2UNADL2DRJwZesF3ZDFhi/R4rLflXVil2117oZsASWve/jWpWThTBV1NUiBks5QQVPylPnW1Tz50s1QnhkrAdjw5kzrrSVzyAlnOY6ylZnnl9nXuAUDr0IIFfgt8C5gDvBxIcScRn+PbdfE7BQzIIaCxugheaeyAMNU8lYKJUDGfivgytYLpUW8ADI5vSyvGiBj5Ju+5b0euMoa+PjWpQXKwFGGN8DZNZphDEnJg0+54QCUNXCW9k5E6mvm7cyusVdmdgagJ5xF6IYBw63kjwZWSilXSSlzwO3AWY3+kjljZvHE2g28dex+hddiKGhCxzBqS3sKOpw3ndX6mbwVeAXI2u8FXNl6wZB4qt3SPHnAbJDi2g0Z7D6+ztTXeMRb7TrJL2mp9YKSd+XJB0vJ6waoaknqa67GzVCVaso7A69N2vBW2toQavfkS2NJUKWmfIuXNdgDWO/4fYP1WgFCiIuFEIuEEIu2b98+qC+JSIOJhkHM3hEIxFHRFEm2wg68VoLTrpGWkte0PAmrc1AGazJrQSWvS4k9R7ny5J3b4G0bw7mUjybByAe6KFtpXXLwDrz6koJzKW9X3gyIkndv/a+u5L1WLJ6ZJ6VKvgmF9+qNN7j/tjyWVDHDpsVTKIXHay5pLaW8Xko5T0o5b8qUKYP7FmvHK3Z2DRATEfLCqKt6XJBheoQOuwaFfF4r2DVZ+61WVPIeS+N0Tvckv6w03J48BHpic163ZIUUyjIbw04vdKo8IQJVysGzrEGtKZQV7RpnnnxzCu8NObtGKVmxVNq/0uI7XjcAMx2/zwA2NfxbdMvvUh2BVxEhr8BAbnRk2BiOGigG5o7XvFa0a0yvOhpowvODMzipKIJ4RCnPk7cDr7IkTx4CPbF5Z9dUCLzatlRByZf4tdHmbA7ygnPHa1QVqIqouaxBRRujtKwBjPiYh5JdoxlaeSypJrtmeOh4eNYHRSwEZgsh9gY2AmcD/9Xwb9ltDlzyHIwtOkFxJUZOyLo6ugQZbrtGBxR0XS8qeT3bEjtASyGlRMriZigo1gpx1QCxHpY0JbVrINATW2kZZaCsO1TpTlDwyq5x1nIJxnh1QxJR7b0bomrdda/JzFPhuuya5pTrcAVe68yuKd30BVWaeQ9z4HVYSV5KqQkhvgA8CKjADVLK1xr+RdEkTD3I9VJMiZEDBrKjg+SdysKQBiDQHIHXjJZpiVoupbDj4qqT5K2qf3rES8l7qLwAT2xOKyoe8V72e5FCQfmVVihsUi0XLzhLDUP1Zt6ek5mnkndM5E1Q8lJKVxkR20KsNb5XWnAOavXkh8euGW4lj5TyPuC+4f6eUiTVGIYh6M0FlwDqgVNZmF61iua0a/SMRQCtVbtGt1hedaxUbUWoq3qZh5txVaEMvifvZ0W5j/EYZ5mSt3eABidt1HBYUWCmiHq1NywcX6vCdRaha4KSLxUetVhRTnhlhVVU8uGO18HB/p/bnWn97lClysIO7Oi6RtJw2DWRZGBUXq0wrEmquAfA7qmpe6fcScNNeBBYJW9bUYqHFeVExTz5UgIIkJLXXGm91dvkeY6zml3TBCVfKjxqsaKc8LpvK3ryrb7jtVmwFVFvJth51LWgVFnYN5GuaW67JkBBuVpRIPkSIszkDVetk2KefOso+cJ1U9xEWJNdU1DydoGy5tZXL4VRIMIihXiNzYnSGj1QQ+C1oORHbsz2PSlcVlT5CswPTrvGHmdt2TUtuOO1mUhGUwD0Z7qbfCZDR6my0KVORDWVvCvw2oJKvjA2D0XolXKXQXr4tcEcsz02B8dbvnV505Cy/QBlefLB8uR1WW6zVfPka7dr9PJr3ASSV5Xa4w1OlNaugeYGXkctybfFTJIfGAUkX2ppGIZBRJjZNa2v5M1/S+0aO0++zK7B8FB5zSc9L/hZUaXebsWdoKWB1ybVVy9FYQJTSq0o/+CkczKrfTPUyK/W/IRHPTteS6+nb4MUaPkdr01DKtYGQDrb+iRfetPZSt7QNFQgokRM5deCSt7wULumXePOkxdCmEWtwF3WAAJBel7wIguvptBOUhBCmE2u/VIoo8G4xr5EWGvgtdJmKKk7un81QcnbfVrK4kQ1ZtfI8uyayko+DLwOCu3xdgCy2d4mn8nQoZcsHw1pEFEi6Lq5zEva5UxbUMmXjg0gEVEKVSgjjhs/rsZJIx1NnoNVy6UUXmMrbQptSAOJLJAflLSMK20oERQl7ze2essaeHnyzVby9ticwqOOZt6u9n/1BF5bdMdr05CKjwEgm299ki+q3aKSjyoqhkUAcbtlXAuU3i2FX+C1tJ48WDXIhcOTj4585kU9KL1uUE6EurVUd47T1VCjdCkfECVfDLwWxxavQPLS6ntgT2YRJUJERLwDkrK5nrzuMbaER1aUF+xJ2y7HEVEiRBSfcdoIe7wODomYSfK5fF+Tz2ToKPjWdlkDaZgkr5s3h12buyU3Q9lLY6/NUI4sBTCDWBnw2AwVzDF7kkWJt2tYnrtznMlIskgKZTteA6LkPcZWya7xnMz8+ry6itCNvJL3jKV47G/wgtc4qzYOCQOvg0PSsmtyWuunUJY+ULrUiaoRpJ4HoRZvohYsa+CXpZHVDJeHC9ZkJnCovGBVZSyF7rsHoOjt6sYglLyeLc6OTYInyccUX7VbmMxKrqe3XeNRhK4ZSr4srbc2JQ/uSbtiq0MI7ZrBIhEbC4Cmtz7JO5WFfRNFVQVpeZfxSNyM3rekkve2a8C9cxAgocbJCFF8GAJWlbEUNg+7ycJNhF7k5x14LQk2N3kyL/rWbiWvGZK8Xj4B+SpcP7vGPk5RR7zwnmfmUI0plPak7bqekQpNyyHc8TpYxOMmyecDSgD1wKks7IclqkYwDL1cyUu9WJWzBeCVk2wW8vIgv0icjCLciifAweZivKH4WiLiVoT29SwjhbLOUKXB5iaTvAcRVqop76nkI35KXncT3givUO1OfaqoP7vG63rWrOTDFMr6kEyMAyAvRw/JK4ooKIWYGgFDRypqUSkEROXVA90nOFkgeeeyV4mZHbCcD0OAqjKWwj+XXC/0/Kwp8OoabzB2+XqvUoqtG0tRl1ftrCcPI75CLdpsxddq3QxVUPKldk0TC5SNWpKPRNtRpUQ3Wofw/GA4lsZFuyaCiuldFkgh4NkmXvAMcsVUEDb5O0khZtk1TpUXYLvGw9JIRFWkLFY09LRrIiV2jWvlEgwlr1ksH1FLJ2cfJW94e9X+dk3Jaq0Jnnyp8MhpRuE9P2jSDKL6rsy8YBdkc3xfIzFqSV7EUsSlRJe5Zp/KkOEMctmKKG6RvHTaNQUlH0zS84JfqWGERSLCkSevxMgIpUTlBaOWixf8smsAstbSvxB4VXwUriyxLoKi5L1SXyuQvB/5ldkYhXSrkmvc9LIG5vUp3chW/reDCLyWTmoNxqgleaJJElKi0fok7xV4jamqSfIoxcBrCyp5r/ouTrumVMlny5R8MPLGveC1SiklQl+v2qnkRfCUvB1bLc0lB+/mGvY4q9o1pZu/YMTjLn7ZNVC9ccigAq+lMYgGY/SSfCRBwpAYo4DkNeumiziUfCwSQbGUvCtPHgJLel7w9q0VhChXRAklannyjts2GmQlb/5bml0DRZL3DLyqCXeBMqc5HBAl7x9LqTPwWjpZedVxGeFyHZVWYNV8eb/rWbWswTD58TCaSV4I4hJ0WifTxA/Om85+WIp2jVLcPFPYOBJM0vOCXyaDV3ZNXImRVkS5fRHQSc2TLCLuPq+eAUmn8ivzp4Oh5O1VSsRjlZL1yELxCzCXK3mPIGSTlLwz3lDsz1s5w6ZqIN0LoV0zeMQR6LR+I29XCqX1EMQjEVShY4hi4FWqLajkPTIZnJ58qZLXhEB3BqgCnEKpG+VEaFsaAzl/JR9X42hSI2/ky5fyAVHymudmqPoCr54KNwBKXqu0Sqlm11jn76y5VN2u0UIlP1jEpYIu9MKGm1ZFgeTVol2TiJpK3kApVLrL2TdKQEnPC57L/pgK2BObk+TNB8f1uAS48qadgaI6FGFbzByDTRY2+TkDr0mrXENWy5YTQECUvN+GIfAmQs/JzI4lOeFVkXGEJ/LiKqV4TVIVJjAnqu5g9vzC0K4ZNGIo6IpOpkpEPOhwKkL7JkpF46gY6CjFSnd2bYCAkp4XPDMZIioID1JQooDV59VGNLi7fL0CeKmCkjdXmH5KHqzKhdIIZp68T3ooeBNhYZwOMktFUmS0TGHPgPnBHnbNCGfXaHr5PWlft/5cZWfAtlNdWWFWnrxrnE6UZlA1GKOa5OMiQl4YNZcIDSo0B1nYqWjJaBTFUvIFUsC6KVtIyfuXNfDIk7cenAwOkm+BFMqIh6Vh2zV+NV3AaqhRGngNmJL3SjP0qvGiWVkzTvJLRpJIpFvlehXrisSb0zTERfLuFZgf7OezNMYikab95oXSDKoGY/STvGIUHqhWhbOsq63kk9EYUXQ0IsXGBPZN2UJKXvfIt45Hitk1Lm9T8SD5AG+G8iIL266p5MkX7Bo9G9gdrxUzUDyeN688eXucLsvGJsImljXwqpVfUPLZKkreZ9MXVKgpXzqRNxijmuQTIkpOkTW37QoqNI/NUKlYtGDXFJo/2+ohoPaFFwyPfGshBPGo+bvL27QIIluq5PVccZkfIFQKTpbaNaUeLlikYGjlhAcBUvLF16KqQlQV3naNUR6Q9CZ5DyUftUpX+NkdDYZu7+b1IPnBpFC6YixeKL3GDcaQSF4IcbUQ4nUhxCtCiLuEEOMd731TCLFSCLFcCHH6kM90EEhYpWlrbcAbVDhVU8GuiUSJopUoeety5vqbcp6DgeFRahggYdrvruV9IfDq8uSDQXpeqKQIK9k1hWbeBbvGoeSbUJXRC14rMPCv8eLlySeta5d2jsWerNVo8bVC4b2R2fPi7cmb915/tkaS91LyfvdowFMoHwIOkVIeBrwBfBNACDEHOBs4GDgDuE6IYRyFDxJKnKwi6M+2dq580dtVCoooqkaIKhLN6clLzSSAFiJ5myxECVnEIx5KHktNSceDFuBdvrpenqURVRViqlK0a7zKGtiTtpYtD7xCIPoGGEb52MC/4bWXJ5+KpAAYcPZ8sCuoujKKzH7NI3Vf+5U1EALSVQKvXjteCysW3WdiDvKOVynlv6WU9qifA2ZYP58F3C6lzEopVwMrgaOH8l2Dgf0/tzvb2jXlC3m7ilspxIVOXqpu5RdLQb51xmt4ZKAAxKL2g+Z4WISt5B0kEuBdvs7r5kQyphbsmkqB17Se9vZrA9A3oNLYKqZQKtU8eduucSj5mDkZjNR9rXkEzIUQpKJq1fheYTKrZks50UIplBcA91s/7wGsd7y3wXqtDEKIi4UQi4QQi7Zv397A0yn+z+1Nt3YLQGferlMRRRWDvMOTN+vXtEGuhUje3vGqlJK8+a8rFc1S9RlaRMn7qN22WJEs/Ap3ga3kPZbyI1yV0QtePV7Bv7lGRYVbNfBqK/mRua+99gAApOIR+mvMrnHZjBHH8+kF2WSSF0I8LIRY4vHfWY5jvg1owP/ZL3l8lGfUREp5vZRynpRy3pQpUwYzBl+kYubN0ZfuaOjnjjScATyb5FVFJSZ0clItLHvTWtpS8i1k19ibgUqVfMQmSCfJW4FXpycfYCXv5cmDt5IvLdwFVnaNnnf70wDR5q/WNJ8JzPTk/csaVA+82p68g+QLSn5k7muv1Fcw4ynV7Brn81n4O/v59Ft9OXvaDgOqGkFSyndWel8IcS7wXuAUWcz23wDMdBw2A9g02JMcLNpjZp/XgXTnSH91Q6EX0rKEK3ofFQb9hlIMYGlpkwBaSMnbZBFVq5N8wpoIMs5844KSDyDJe2RpALTFIzXlyae1tJV54UXyzbZrvMfm18zbzhGvquR1LyVvkfwI3ddeWVFgjq2qkh9tdo0Q4gzgcuBMKaXzCtwNnC2EiAsh9gZmAwuG8l2DQVt8DADpTKuTvPmvc8drVIkSxVTyrpso1tZ0lVcPvDIZAKyUa3etE0uTZLw8+QCSvN/Ykg5v1yvw6sqT1/NuVQvmNW7yRG6PLaKWr1Iak0Lp9ORNsTZS97XXHgAwJ+fB1K6xx+mbJx/wHa+/AcYADwkhXhJC/B5ASvka8FdgKfAA8HkpZeX/O8OAManxAGSy3SP91Q1FwdJwpFCqikpEGGQNUeLJp1oqu8Zu+hwtyaGMeil563b1VPIBTKH0ytIAW8lXD7yaefJ5HyXf3GvsZ9f4evI1B149lLxt14zQfe1H8qmYWrWsgd/OXqii5Jtp11SClHK/Cu/9GPjxUD5/qGi3mnnn8r3NPI0hw0vJq0Ilik7WUM1MGzVeVPI9G5t4tvXB74GKRiRo7oclKiAiJekWsWu8sjTA9uTdgVenJ68qKlElamZLeXnyseZbcoWxqeV58rWmUMbVOIpQGHAqdM8CZSObXeMXME/FVLb3VqgmiY9dE61G8lpxRToMGNU7XpMFku9p8pkMDZ6evKKiopM1zIcsGUkWSb6FPPm8D1lEVMurd6pYQyNpyBby5H2yNKIqA9amGpsUoiVqvVCG12s3ZLT5lpym+3jyMcWT5L2UvBCieN8WDrSurSvwOrJ58ppPWm8qFqmeQinLST6mxBAI92TmhJ4DNTaEM66M0U3yifEA5LTWsS+84CpQ5lBEEXSyhnkJCw9LAJby9UC37ZoS1WSTvPNhwdBJSoO0c+ejnV4XwDH7ZWk47Rov5QeOrkl6zkfJN9muKXjyHnaNV568RwolUE7ynmUNRlbJF9JD1XK7ZqDW7BpRZTJzQtdCkh8sksmJAOT11lG2XvCqJ68qKqrUyRgCKaVDyTd/KV8PNJ8HKqJYHpV0vK7nSUhJ2nCSvKXkAzhmPyvKZddUUPIZ3bZrSgggUCmU3nnypWV1/SYzf5J3ljUYWU/eb2yp2OA2Q4E5Tt/Aq54rD643EKOb5BMTAMgbwVvK1wOvzlCqUFHRyMsImbzhUPJtZs54AAt2eaGQQlmi5FXVsN53KD89R9Io8eTtpXwAM4r8UvHaYiqaIclpRiG1sJQUEpGEVbvGI4Uy1mYSg968rmd+dk08qmJIyOnuXHmvTV9QieSd9XoUsxDdiAVevfduJC27plITImf2m+tvKyl5w2MibyBGNcnHE+MRUqK3OMk7M1CcSkGROjoKAznNreQhkKTnBZssSolQtewaTXMq+RxJKd12jaKCGm+6feEFfyVfrE3up+RTkZR5Pb1SKKMjuznIC5VyyQEyOTfJF8SJUk7yrto1Nsl7WVQjdE/nde+9G21WcblKTYic2W9OJKNJ/81QukcGVQMxqklexNpJSkleBi+9rh44bzpb+UWVKIo0q1AO5HQSkUTRk4dA2hdeyOveS2PFsms0vdSuMUgbJRkOseZvDvJCTjPHEFPLyxqA2WXIeT2dKEzaXimUseZfY80wiCiirLCcX59Xm/yqKlx7Ai9LGx25hAK/sRVryvuTfGFlJqrYUk54xV0aiFFN8kRiJA2JLkemROlwIa8Xb7oCKYgIipEnh+kTurJrIJCBSC/ohkQR5RkoqkXyLq6w7JpMafPnAGSbeKEaEQ44lLyvV61rHmUNmm9RaYYsU/Hg6PNaQvI5i7yrk7xlxUXi7g8ewXIdeV2WZXtBbd2hdENHIMqVvFqJ5EO7ZkhISNBobZLXjOJNVyB5FASSvIzQ77RrWk3JG0ZZhgYUlXwu71bySSlJl258CkC2iReqkcVATvMl+cLKzMiXp1CO8OYgL2i6LNvABv7dofJG3iQ/L0/euQqzG2t41esZoXs6pxmeYyv0Asj7x0I0QysjeKim5D32QjQQuwDJC3TR2vXknTddYTloZdnkiJL2VPKtQfK6Lol6KEJF2Eq+xJM3JOnSLIUAZJt4wY8s2hxKPm/kUYXq2gwFDvLzK1AGTR2zbnhPYH52Td7IE1Wi5asaP7umVNmOYLkOzfAh+Xj1xiGaoZWtVqBKdo0RkvyQEEdBE+UpXa0E502X1/NElAjCIvs8Efqzmhmoy6dHPN1sqPBb9gvFQEqBq9+LkSeJKFdEAS3KphlGmR8P7haAmqGVqXhwkJ/UvbNroKnX2LYQS5H0U/J6npiHJeG/GcojbXSExpvXZFnQFRwtACvYNZrUyvx4qBZ4zYWB16EgYZF8aUpXK8F509mKCM1UPDkipPOmks8ZOfSoXbAreKTnBT/VJBQDpOJWhHqepFDIaBn3pB3Q8sp5zVvttsWLzbwL17MEJvlZ1zCoSt6j+bRfL9TK40wXr6eeNQmvRPGPpJLPG4bn2OwJrFL9mpyeI+qhyn3tGkM3u3+FnvzgESdCXjEK28hbEc6bLm9YikgvkrwdeAVI26owgMrWC5rureRBA2muUgrQcyRR0KVesK2AwCr5vM8EZpOFTfJ+Sl6TOnkor2tSUPLNG3Pe57qVNiq34WdjpKIpJNIs4QCmki8NusKIbvLL65JYxMNmi1cPvOb0XG0rFhteZRwajNFP8iJCXhgMtHAzb+dNV1BENsnLKP1ZrVjO1FYgAVS2Xsjp3kQoySNlpETJ50hYgTvXAxPQ8sp5n+BkIYCX1Sp6uAADiignvQDkyed1w5sIY8VViut4I++rcMFxPbWstz8dbRux8Wq6UdGuqajkjRwxpZzkE2oCTWrk9ZL4oF8MooEY9SSfUGLkFFm1o0uQoTn8z7zuJvk8Zo3rQqU7W10FUNl6IacZxD3IwkADqbqDXHqepBfJBzTwmte8yaJg1+QrKHn7egrFIwjZ/AyqnFYt3lDuyXsqeWdXM7Byxput5L3tmlo8eb9x+jbz9otBNBC7BMlnlcoR8aDDqQiLnry5vNWVKP0Ou2bA7rzYKkreJwPFkHmQEffkbNk1UKrkg2nX+MUb4hEFIWAgW9mTB2vSLrVrApAnn/NR8s5VihOVbCkoJXkPwou2mVko2vCnQ+d1SdRzbN6rFNffGj4BZnvSLg2+2unAYanhwSOhxskK3N5uiyHvWD4W7RpTASiROOlc0a5JSyuvOoCk5wU/ssjLPIKS0q5ahoS1FHYrebteT7CC6zldeu4BEELQZtVByek54h4edOF6CgGREtJQIyYRNjG7Jqd5X7eoqhBTlTJ7tOpk5iJ5D7tmBPu85nXDM61XVQTxiDLowCt41JQPSX7oSEaS6ELQlwvetvda4UqhtL1NK1ClROP05/SS7lDB9Ki94Oft5vQcClF3T818hqTqRfJ2TflgjVnTDWIedg0Um3ln9SxxpRLJK94E0GSLys+uAWtsJaKqGvkVaq1rWe/A6whu8vPb6AV2M+8KgVcfT96f5K2As9eYG4RRT/IpiwC6+lu3cYgzFS+v5808XMuTVyMxtydfKDfc17TzrQd+ZGGTvMuu0TKk7NZ4zl2vAd0A5uftgrkhylbyXsv7gletCG/7osnNYbK64WlpQHFsTtSn5H3GCyNyjXO64Zn6CqZlU7F2jd9+ANWP5K3fQyU/eKQs/3JgYGeTz2TwyGo68YhVAU/PmMv7vElyIpYsVKEEZ5/XYBGeH/yW/TkjhypKlLyWIelcsdgI6AawnOZPhHbZ2qye9U25A8gIj+waaHpzmHw1JV9C8lk9W1htuo4tJfl8urgyc2IEr7GZDODdczUVU0lXKGvgq+T9WgCGSn7oaI+PAaA/3dHkMxk8so4MlJyeMx8WS9GIaJL+nO7OUhjBsqxDRdaH5M0VS9S9NNYyJCIeJB/Q8spZzSDhQ/J2l6Gcnis07naiYuAVmh5szuneWVHg7nxlI6NnvMdZSn75tP94YUSucUbTiUf9r1slJV/NlsqU1l0KPfmhoz1hknw6293kMxk8sppRKPxUeFism0ONpwq1a8DhyQdM1frBL/CaM3JElKg7yJXPeHub0eZvDvKC87qVwu4yVE3Je6ZQQtPjLn4rMDA3e5UpeS1bcTIr1JTXMkXV7sQIXuNs3n8CS1bz5PVc5X0PWsn5h0p+6BhjKflstqu5JzIEZPN64abLalnLrjFJTvWya1pIyec0g7jHsj+rZ4kopUo+SzJaklcNwVXyjutWCrsXalavTH5pIbxJr8mVNysFXr3a5BVsxhJ42zWVlPzwj7ny5BypWIUyq2cLY3IizK4ZRiTjYwHI5Fs38JrRjMLyMaNnLLvGvFki8XbSOZ2oEkUV6qjx5DNahriSKEmhTJMo3TwDgajl4gXndStFKqYykK9u1wwookhwTjQ7u8ZnBQZmtcZSu8ZvMkuoCYSz6Fw+bbb6K8UIlnKoODlX6fOacViKrr9r9ewaIcTXhRBSCDHZ8do3hRArhRDLhRCnN+J7BoNkYjwA+Xxvs05hyDBvOlNZFJb3lgKIJpIM5HV3R/jYyG0BHyr8yCKjZYipCTdZaFmi0RQRJeL2NgMaeM3mdRJ+Abx4pKDkvZb3qqISE6pp13gFIpudXZPX/Unex67xCrwKIYr9bMHMNvFS8oUNYE1W8tHKdk1aT3uO0zOWBMV71p7EhgFDroojhJgJnAqsc7w2BzgbOBjYHXhYCLG/lHLEt52mLJLPacEigHrgDLwWHpZ8HygR4vF44YFKRpLmw9JkAqgH6Zxe2ArvREbPMDWSLJKFlOYDEUuVF3sKql1TSclbRCi1TCH4WIqkUM3Aa9SDAJqYXSOlJJ3XC7tbS2EGXnXX8Vk962nXQEnxrnzGZ1IbmTx5w5AVg8peVpQNzTBrEXkp+agSJaJE/Eney5JrEBqh5K8BLgOcBdvPAm6XUmallKuBlcDRDfiuupGMjwcgrweLAGqFphtohiQRVd0PS94MUKWiEXKagW7IYlPkgNZyKYVhSLKaUajKWHhdGqS1NMmIadcYhjSXtVKHWFt5K7UABl4L161iKl6enJHz9HABEraS99sB2qTx5nQDQ+KrdkuDk3kjj0R6Klxw9rM1TCXv9f9jhCw5uyS5XwplMhbxVfJ2JU2/61kQYU7Y4xlGJT8kkhdCnAlslFK+XPLWHsB6x+8brNe8PuNiIcQiIcSi7du3D+V0PJFMTABAM4JDAPUgq9k3nULOyBUflnw/RJPFWiHOFoB2VcaAbfMvhd31vpTkCw9LNFk8rrCsbTcbMHgq+eCs1grXzUfJJ2MRpDA3tNnpr2XHoJCOeNRWh6aWcsjkzO8svW422mIqOd0gbxGmfa28PHlwkJ+9gc9KlnBBUc3g5DBv8stY5RgSFWIpOd1A8+hPYY+z6mTmRK7PHJdHy8BGoapdI4R4GJjm8da3gW8Bp3n9mcdrnq2ZpJTXA9cDzJs3r+HtmxJJi+SlT+utgKN406n0WySWiqYg2wvxMa6qf4W+oCmLNJztAAMIWxGV2jW22klZy97+rE5Ksx7uWFu5IookABEoJW9ft0qbaoRikryv8kOQ9nv4nRZVvH1oJ1sn7PLPXjab+XqxkNe4pFIoWdDmZTvhIL+slRyRGOv9xSOQUJDJm+RdKfUVzAqiY0uyi+x70suuAXMyLyf5gWF/RquSvJTynV6vCyEOBfYGXrb6Ns4AXhBCHI2p3Gc6Dp8BbBry2Q4CanwMccNAk9lmfP2QYft/ZjaG42HJ9kGs3aHk9aJd48xECDLJOyYwJwokb6dL5nTQigGqMkUkhLV6CU59Ivu62WWFS5GMqSCqkLyEtF8ziWgASL4aEeY0xiWjhdzwqiSfsUg+7kPyI9A3wN6X4RdvsCtRpnM6YxNuG80epx/Jeyv5fu+YSwMxaLtGSvmqlHKqlHKWlHIWJrEfKaXcAtwNnC2EiAsh9gZmAwsacsb1Qo2SlBJdDn+J0uGAfdO1xSMFJd8WbTOXefEx3nZNIdsk2PVrMj5k0WtlQo21yGsgr7nsmoSa8O7zGiC7pnDdfMmiqORTPkG3pGFUUPLN6/Nqr8Cqql3rONcK1AOFFWhNSn54x2t3kLObn5SidGxO9FnP29iY9/kXxulErm/Yhdiw5MlLKV8D/gosBR4APt+MzBobCQm6yFc/MICwt1CnYmpBKaQiqYKSTzqURTJiNQsOaLZJKZyrFCd6cybJj7ce9v6sXiSAWLs5ztLmCwGrKV+4bj5Kvha7JqVrpH0KnDVzb0A1u6ZQd936f+C6bz1QruTHeX/xCGzyKyj5uH9QGcrbG0LxvvUjec/Aa6YbLEt5uNAwkrcU/Q7H7z+WUu4rpTxASnl/o75nMEhIgUZrknzasex32TW5XoiX2zWFsgYQKNLzQl/WXhq7ibDHIvSJCfNhT+d0SHeab6YmmoHXUmsmYBlFA1WUfDIaAdUcgy8p6HnS3sUQm6rkezPms9TuM4G1lRChawXqgdo9+eFPDS5et8pK3ivDpidnnv+YmEfgGIp2qhPpztYh+SAjIRU0oZmpeC0Gp0fYlzeXg8lI0rw5EuNdTaGL2TWtoeR70iZZjEu6vU3brpmYNEm+P6fBgFVgLjnR364J0Gao4gqsgpJXzeszLuatXJNajrR3vkJTN4B1+1w3G0lHcBKqk3zhemas+lK+nvzwW3L2dWvzUfKV7JpaSL7svg1JvjGIo6ArurspdIug36F2u60iaxOiY8wHom1yUVnki568tAM/AQpEeqFAFqkSkreWvZNT4wFbydskP8Fc9uoly96A1espXrdKnrx5fcYlPEheSpL5DGk/l7OJE3lPxhzb2KSPko+77ZquTBcA462NiaUopMRWU/IjsMmv32d1aSMZtTOH/O2a9ph3INyf5McP8mxrwy5B8gmhkhdGxbZdQYVNhOOTUTozpmUxXrce/NQkV9/JVDSFRJK1N88EiPS80JO2yCLhbddMSZUo+fg4UCPFyUw6VO4ILOXrQS1qV6gDCBTaox6kkOsjaRhkMdAND6JvoiVnr8BKs0tsFFeX5vXtynahCpUxUX+FmzfyaOkuEKr/7s8RqLzZVeW6VVLyvblekpGkZ5kK8CD5fMYcT6jkh444EfKKUbHmRFDROZBHCBibjNKV7aIt2kYsa9XhSU0qLI1d5YZtH7cFlLwiyr3dnlwPbdE2xiTNzTMFJZ8yH4ZUNIUhDXKGI2NqBJby9aBrIFe4bl5IxSIIdYC40oYiPB7DdBdJaeZsl61aoKkbwHrSeeIRpebsmq5sF+Pi4xBem7ooBmQzmS5TxfscNxIdzzoHcsRUpeIKDPxJ3s+qAQo1egrixFrhhCTfACSUKDkhKxb7Dyo6+3OMS0ZRFUFXtovx8fFgd7lKTSoLvAJk7IyM0qVhwNCdzjM2GS17+HtyPYyJjSHliDc4vctCP1vnJBZNBWpS6xzIM966bl4wPfk0ccUnxz3TRdKKIZUt8WFEe56Wwr5ufijYNQ6SnxD3J7LC9RzYDqlJ/l88ApuhuvrzjE+V35M2khUCr725Xt8gOphKXiILO7qdyQTDiV2C5JNKjJxCxbZdQUXnQI4JKbNpRGe20yJ5K4mpbTJRVSGmKi6SL1BCgEjPCz2ZvOey2H5YIqpCLKIU7Zqk+TAUJjOnwg1YUbYOx3XzQjyiINQBosKH5Ad2kpQVSL7Q87QJSt7nutmIRxSEoNCftzPT6evHg6M7VN8WGDPd/4tj7WDkQR++TLnOKtfNaY+WopqSLys3PFCMMw0ndgmST6hxMsL7wgQdXQOmsgDoznSbD4tDyYNdEEorKiKsuhoBJ/nudN7T17WX92AV8srp5sRmj9ery040GTi7ZkKbP1kIIVAjaSL4bITp3kjScNd+cUGNmf51s5R8wmcnLubY2mKRQn/eakq+QH4D22Hs7v5fHBv+jCLn8+YFVRHEIopn45CdmZ2mCPNBqrQXQt9W643JPn/RGOwSJJ9UE+QVQW+m9Xa9lir5CfEJ0G8peYv07PKntiIaMKyO9wEPvHanvRXh1v6t7JbaDbBK8mZy0LMJxpk17jxrc0fbwNBAC8Y17uzPM6ECWQCISD+q9FHyPRsrK/lCKYdmBF61ikoe3M01qir5AsnvhDFeZbIsjMAGsGpKHhzCowRbB7Yyrc3//MuUfPcG89/xM33+ojHYJUi+zSK/7kywt/l7wVYWUko6Mh1MSEyArnUwZvdCCdpkTGUgX9rnNRl4Jd81kC9Ln9QNnW0D2woPSyoeIZbeZhL4+D0Bn6bIAatE2TmQY3wFstAMDdRuItLHj+3eQNJazXiSPDRtb0BHf+WxQbFRuWZotXvyUqti1wz/BrDOgTwT2ipPYF5NUQbyA/TmegvixAvlJL/ezBjzSqFtIHYJkk9ZBNCX7mruiQwCtrLoyHSQ1tLs3rY7dK6BCbMKx9jKwk3ywcobL4WUkk1daaaPdRdz6sh0oEmtqORjKqkBq7ZdCcl7tgAMgC8vpaRzIMfECnbN9oHtIAwU3Yf8ejaSTE0BKN/da6MJewN0Q7K1J8O0cZV7kqZiZuOQLf1b0KXOHu2elcYBhyevKJWV/DCTvJSS7nT1CcyrmfeWgS0A7NbmT/JlK9Cu9cOu4mEXIXl7c8KAvaGmRdCdzjOQ05k2NsHGvo0A5sPSuQYm7FU4LhWN0J/VWkrJdw7kyWoG08e767Zs6TcfloKSj6mMy9okb47Zk+QLgcjmk3xPWiOTN5jS7t+3c1O/NSbNh+S7N5BsN/8f+Cv5kQ827+jLohmS3auSvKnkN/WZ49xjTAWSt1dmQsCEvf0/dJjtmh19OfK6ZNrYWiYwtye/td/01+tW8uNmDOGMa8OuQfJWNcNMiyn5DZ3mzTxjQrJI8skppj/tUPLJmEq6zK4JVkphKTZ1medWShYFRVRQ8hEm5DYCovBAeNo1AWrmvd66bjMnehceAwrkZ+TGl7+p5WDnSlIT9wUqkHwT9gbY1236OP+xQTFO5BInfsfaAUmhwOT9/T90mJt5O5+3SvBq5l0QJ6kaPXlDh45Vrud4uLBLkHyb1Wkmne9q7onUiQ2d5gM1Y0Kq8LDMyKYBCRYBQPGBckXvo8lAEJ4fNnebBF2q5Fd1rQJgr7GWao+pzMq/CZP2LfT+9FbywbFrimTh37dzQ98GQJDLevixO1eAoZGYejBQzZMf2fFuKVy36ko+ndPZ0LcBRSgVA5IFT75tYvE6emGYS2ivt563mRMr91tNWaLKiVXdq4gpMaa3+8cUXPdtx2rz+dztkCGedXXsEiSftHJXc3ZtjBZBkeSTrOpaxZTkFFLbXjff3H1u4TjbI3R5fgG3azZ3eyv5lV0r2aN9j0Lt8WRUZR99NUw7rHBMRU8+AIFX+7rNrEDyKzpXkBRTyeQ8dlZuXQpA0hqzv5If+eyajTUq+URUJZPXebPrTWaOmem71R8cbR7bp1T+8mGu12NPznuMr22V4sSKrhXsPW5vIop/aqnrvt36qvnitJDkGwKb5LP51squWd8xQCqmMj4VZVnHMg6adBBsfgliY2DiPoXjbP9TEYpZ0S8f/MDrym19jIlHmDLG7Vuv6FzB7AmzC79PFH3sLrfB9CLJ25OZO08+OEp+XccAY+KRsswhJ5Z3LGecuqf33o2NiyCSIDZlDopQApVd8+b2PsanolXTQ5NRU+0u27mMORPnVDw2lu1HkZKBSkFXMDdDwbCNeX3HABPbYr7dvGykPJp5r+xcyX4T9qv4dy6S3/QSKBGYctCQzrkW7Bokb6Wi5fXWIvnXt/Qwe7cxZPQMq7pXcdDEg2Dts7DHXHA0k7AzGcBRBCmSCLSSf31LL/tPG+PaPj6QH2BNzxr2n1D0ZQ/MvGT+sOdxhdciSoSoEi1JoQxO4PX1Lb3M3s2/JV9fro/1veuZFN27sCvUhTVPwcy3IqJx78qFNpqQXbN8Sy8H7DbGd9u/jURUZUDrYVP/JlOcVIBYv4CklKSrKflhjrss29zL7KnVWynaosrGjvQOtg5s5YAJB1T8O1ucZLQMrH4SZhwF0cq2VyOwi5F88wmgVkgpWbqph4N3H8uSHUswpMHBqd1h22uw7ymuY5NRlaxmYBiyWIY3wIFXKSXLt/Sy/27uLeAvbnsRXeq8Zbe3FF7br3cBvTKJ3ONI17Fl5NfE+upOGIZ93fxzn1/Y9gISyR6Jg0jndXc1zd6tsHUJ7H0i4FOe1sYIZ9dIKXljax8HTPPfum8jGVPJR8z4yiGTq1gSKx8iISHtV2LYxjCu1nRD8vqWytfNRmngdfHWxQCu+9YLhZV2utNcke/99iGdc63YJUg+Ye2200pbxgUYGzrT9GQ05kwfy7ObnkUVKkd1WGl3s091HWsXTcpoerGPZIADr+s70nSn88yZ7iaLBVsWEBERjphyhPmCnmd2x+M8YRxO1nDfqmX9MgPSKGVdxwB9WY2Dd/cnrOc2P0dMibFn20EYEnK6UXxz6T/Nfw98L+DTTciGreSdk8QwYtWOfvqy5j1ZDcmoiky+QTKS5PAph/sfaOiw7F6SkQQZWaUmjaJAJDksgddV2/vI5I2K182GU1QBLNi8gGQkWXXFAtakvX0ZSAMOeNeQz7sW7BIkn7JIPi9bh+QXrDZz+ufuOZ4nNzzJ4VMOo/2l280A5G4Hu44trUQ5oA0EOvD63Gqz9s7RexcrDkopeWTdI7xl2luKDZ+X308y38Vd+vFlHmgqkipJoQxGy8PidfPOf5dS8ui6Rzlq2lGMiVvZUPbYpIQXbjYzLqYeCFRT8ilAjth1fn6VObaj965eNTEegciYpRw59ShiaoXNRcvvh74tJBMT/Dd9OTFMwebnHM9bNdj18jOajiENHl3/KCfscULF4HLhbyNJ0jteN1NFp1eY/BqIXYLko/GxRKREl9lmn0rNeHrlDia2xYgmtrG8czmnjTvAjMi/5dyyY+263q5m3tGRVXn14Lk3dzIhFXX5n6/tfI21PWs5ba/TzBekhKd+QV9qBo8bR5SlrJWRnxoJRL2e/6zcwZQxcfb38eRf3v4yG/s2csbeZxTL1tpje/NR8xofc0nh+Mqe/MjGIZ550xzb3pN9iqo5sE17DSXazckzzvA/SEp49rcwbk+SbVO96+aXYpgatj+9Ygd7jE/WNDanqHp+8/PsSO/g1L1OrfJXJpLSIJ3phLd+1r9ufoOxS5A80RRJQ6LTGiSf1w0ef2M7J+w3mduX30ZUiXLG649D21Q47Oyy4wvKIu9s5m2lgZV2h28ycprBw8u28o4Dp6I4aq3fsvQW2qJtvHvvd5svLLsHNr3Iyv0vQqc8L7nMroGmW1SZvM7jy7dx4uwpvoHJW5bewpjoGN655zsL1y2d00HX4OHvw9g94NCPFI6vruQZkTiEObbtnHLg1KpBV4AlPQ8i9QRvmXyC/0HL74d1z8BxXyARSbpXZn6ItjU8TXYgp/Hkiu2cuL//dXPCKapuXXYrExMTOXnPk6t/kaGT7NvBQDQBR3xiqKddM3YNko/ESUgDnWBUKKyGp1buoKM/x9sOjPKPlf/g/RMOYdK6BXDSNyBerhBL7ZrCjlcInGXz5Bvb6clovOuQ4qaRdT3r+Peaf/OB/T5glqAY6ID7L4Nph7Jt3w8D5U0aPMmvyS0AH162ld6Mxgfmeu/uXN29mofXPcxHDvgI7bH2YsPrnA7P/ga2vAKn/xgiRXsjWYn8RjAO8djr2+jLapxxSJU0R8z00KU9T5HrPAbd8ElHHOiA+/6fmUI47wKza1LNSr6xJP/ga1sYyOm+160U9nVbtnMZT254ko8d8DHiqn8JiwIWXE8i20dmwqwRyaqxsWuQvBAkJegMX7OBRuLmZ9YwqS3Gwp5bAMn5rz0Oex0PbznP8/iCIvRS8gELvt70zBqmjU1w0gFmupyUkqsWXkVMjXHBIReYivbvF5g188+8lkTcfHgy1ewaaHoLwL8sXM+0sQmO3be8u5GUkp8t+BmpSIpPzvkkULxu0XVPwyNXwEFnwpz3u/7ObhnniRGMQ9z4zBr2GJ/kbbMrpzlKKbl60dUk1TZyO0/0brlp6HDXZ8x66u//LahREmqFcToxDM1hbl+wnhkTkszbq7bmHaaoMvjD0muYEJ/AOXPOqf5H656Df3+HZGoy6UT1NM1GYsgkL4T4ohBiuRDiNSHEVY7XvymEWGm9d/pQv2eoSEiBJjR3uloAsXxLL48t385Jc7dz/5r7uKBfY6YShw/8LyjefScTMS+SD56SX7Kxm6dW7uCTx+5FVDVvvbtW3sUTG57gkiMuYUpiItz9BVj1GLznF7D73HLf2oK3kh/5bf42XlzXyX9W7ODc42Z5tvz72xt/45lNz/CFuV9gctJsEpGMqRwsVrPvoxebm9vO+m2ZT1ubkh/eie3FdZ0sWN3Bucft5dvO0MZflv+F5zc/zwdmXQxGquy6YRhw95dgxb/hXT+DPcy0w4qTmRMNtmsWrung+dUdnHfcLJd9WAmJqEp04tO83vUylx55acWWfwBseRVuOxvG70lyj6P87bdhwpBIXgjxDuAs4DAp5cHAz63X5wBnAwcDZwDXCSG8GWqEkEBBVzSymlH94Cbip/cvY0x7N892X8tBuuCinTvg47dVLElq2zVpq3FIEJW8lJKf3LeMCako5xxj1qV5bedr/PT5n/LW6W/lnP3Phn9+Hl6+Dd7xbTjSrXZLFaGnJ9+kJhqGIfnp/a8zIRXlU8fuVfb+q9tf5coFV3L8Hsfz8QM/Xnh90s7F3Br7KfnoWPjknWYT6xIk1ARpv9TfEVDy9nWb3B7jv95aPjYnFm1ZVBjnaTPPAkom53wa/nkJvHQrvP1yOOrThbcSaj12TWPGaxiSn1pj+0SVsTmxtv9V4lMf4LAJJ/DB2R+sfPCml2D++8xrdc6dJONjW4vkgc8BP5PSTFuRUm6zXj8LuF1KmZVSrgZWAkcP8buGhLhU0BWjbNkfJNz36maeWLmKibP+ALk+/md7F/Fz7oQZ8yr+XbIkuyZv5NFsXzcgSv5vizfwzJs7+cqp+zMuGWVtz1ouefgSJiYm8rO5X0O99QMWwX8H3n5Z4e8SUX8lX6b8mtRE4y+L1rNgdQeXn3Fg2Zb4lZ0r+dwjn2NKago/OeEnKEIxs0qe+x17/+tsumQbTx1/Q6FWfimq7niFYVXyf16wjoVrOvnaaQfQXmG7/4rOFXz18a8yY8wMrjrxKtriZjphxp6cO9fCDaeb1/ikb8FJ33T9veek7YUGluu49fm1vLCui2++66DCirEalncs55evfgsjN5EPzPxK5UDtS7eZY462wXn3wsS9K1/PYcJQSX5/4G1CiOeFEE8IIY6yXt8DWO84boP1WhmEEBcLIRYJIRZt3759iKfjj4RQ0YRRvnwMCDZ1pbn8rqeZsc9vSOe387uuLDM/cRfsdWzVv3V68oVKlLa1EwAlv2ZHPz+4+zXeuvdEPvHWvViyYwnnP3A+Ukr+9+DPMXn++2HTi/DBP8Db/5/rbwsbvXzsGpf9Fhv5Xb4rtvbyo3uXcsw+E/nYUe7V1vKO5Vz80MXElBh/OO0PTExMNCehOz4ND3yD7KxTODP3Y3ZG/fuaJiIJNEMjb3jEk4a5Xs/KbX386N5lvG32ZD42z38lubxjORc+eCFRJcpvT/ktY2NjXfckKx+B698OHWvg47fDSZeX2VK2XVPVTo21NWQiX7a5h5/e9zpvmz2ZDx5ZW8B14ZaFXPjvC817b92FCMOnAJ2WhXu/Av/4rFm64OLHYKJZJ7+i/TZMqEryQoiHhRBLPP47C4gAE4BjgP8H/FWYU5vX9OZ59aSU10sp50kp502ZUqV2xRCQQCWvGN6BoCajL6tx/s13Mnb3n5BWOvmVnMThFz4BMypvk7aRjLmVPECmQPLNVfLdA3kumL+QaEThfz56OA+te5DzHjiPmBrlT9NPY9bfLjTJ+dMPw2EfLft7P7smGUkikWR1R1psdGQDr93pPBffsphkLMI1HzvCpeoeW/cYn7z/kwgE1596PTPHzISdb8If3wmv3Qmn/DeZD91ML6mKDebt65nVPNJ/hzFPvrM/x4XzF5KKqVz94cN9/eoXtr7Ahf++kHgkzo1n3MieY63uXVEVgcE+y34Ht37IbFd58WO+uzzt65kzqmTA2SQ/hNhaZ3+Oi29ZxNhkhP/5yOE1pU3eueJOLv73xUxMTOSaE/8XqY1nwEswdq2HG98Fi26A474En/wHtE8tvJ2MJMnqWXRj5Hiocrk1QEr5Tr/3hBCfA+6U5vS7QAhhAJMxlbtz6p8BbBriuQ4JCRElJ7KBU/KabvCZW69l55gbGGtoXD/xbRz8nt+40uiqwWlp7GaXG7Zv3CaSfDqn85lbF7G+Y4BbLjiae9bN57qXrmPu5MO4pl8w6bGrze3777/Ot89lURG6YynOxiF24aeRDLz2ZzUuuGkhGzoH+PNFxxRK70opufG1G/nl4l9y8KSD+dXJv2Jqaiq8/i+467Nm5cFz7oB9TyZh3YuV7km71npGz9BOSVbGMOXJ92c1Lrp5EZu7M9x20TG+rf7uXHEnP3zuh8xon8F177zOnMjs8zb6uD56DUesWGzm/b/vV8VJyQOFcWqZyumI0RRIHfQcRGpIWyxBbybPeTctZGt3lts/cwxTq3SB0g2daxZfw/yl8zlu9+O4+u1Xo8gU8EbRirLx5mNmZpieh4/eAnPOLPu8wn2rZ2hTqm+8agSqknwV/AM4GXhcCLE/EAN2AHcDfxZC/ALYHZgNLBjidw0JCTVGFhkoTz6n6Xzm5q/wqvooB+Y0rp37NXY76qK6PyeqKkRV4e4OZYuTJtk16ZzOp29eyPOrO7j6Iwdx54aruH/N/Zy5x0l8b/nzxHasgHd+H47/csWdf/GIudj08uTBLNs6nvHmiyMUeM3kdS66eREvruvkt/91JEfNMrf55/QcP3j2B9z95t2cMesMfnj8D0koUXj4B/CUmS3ER28u+O/xiIIiyq0oJ8r6gjoRTQKioWPuz2qcf9NCXljXybUfP5K3eKQVaobGLxb/gluW3sJxux/HVSdexbi4Y5LeupQxf/kEJylreXLfr3PiB79TdXenc5yuzyqFs89rnSSfzulceNMilmzs5nefOJIjfUpP2OjP93P5k5fzxIYn+PiBH+eyoy4jokTQrZo1hXvSMMzr+9iPYfIB8LFbYPJsz8903rdt0dYg+RuAG4QQS4AccK6l6l8TQvwVWApowOellE1l14QSIyfLl/3NQl8mzWdv+Qgvx9ZyUgaufM8tpGYcVf0PfZCIljTztt9ogpK3Cf6ZN3fy/ffP5M7N32HJjiV8ecZpXPD87YhIwlzG7lO9Cp+iCBJRpYwIC92ESitR5gfMh04Zni0gvZk8n7llMc+u2skvPno47zrU3NS1M72Trzz+FV7c9iKXHHEJnz3ss4iBDrjjAlj1OBx5LrzrKtcmGCEEyWh5AwonPBukFD+goasXm+AXrengV2fP5T2HlXc56sn1cNkTl/H0pqc556Bz+Nq8r7kbZbz6d7j7i4j4GM7RvsO8qe/mxFp2kTrL8FaCc/WSql5Dx0Z3Os9FNy9i0VpzbKcdXHlT18a+jXzhkS+wuns1337rtzn7wOJOc1URxCKKed3SXeYK7Y374ZAPmysWjw2LNgpNy/NpqNybpGEYEslLKXOA504AKeWPgR8P5fMbiaSaQOqCnkzzA5GbOzbw+Ts+yIpYmrOzY/nmJ+9GSZVvoKkHdrs1mxQGsOyNEc422dmX5cL5i3hlQxdff287t6z7Kj3Zbq4ZP49T/vNHMxD1kfkwrrZgF1gNKDw8efBpAailK1oDg8X23izn3biA5Vt6+cVHD+cDc82es8s7lvOlR79ER6aDn7/955w+63TYuBj+ei70bYMzr4UjP+U9No9Wck5UJb8GbQDb3pvlgpsWsnRzD786ey7vO7w8GLy2Zy1feOQLbOjdwPeO/R4f3v/DxTf1PPz7u/D878za/x+5kaU/f5mDc7WlLCfVoo1REYOIQ2ztyXDuDQt4c3uf79iceHHbi3z5sS+T1/Nc987rOG7348qOSUZVxnW/DtefbTbkPuNKeOtnqq5YCs+nX2XRYcBQlXzLIBlJgA7dTa43/sIbT3L5k5+nIyK5RMzlc5+e3xDVaXfiKSp5g0Yv5ath7c5+zr1hAZu7M3z+PRnmr/kBY6PtzNcmcNCLd8BRF8HpP6kr3gDFsblf83hYnHnjDSb5tTv7+dQNC9jWk+UP587jHQeYwbTH1z/O5U9eTnu0nZvedRMHTzoYFs+H+74O7dPgwgddrRrLxhZTy71d5/teTcudaICSX7W9j3NvXMCO3hx//NQ83nHg1LJjnt30LF9/4uuoQuUPp/2BedMcab29W+Bv58G6Z+Gtn4PTfmjuYo0tqTkGVrOSd9o1NWDV9j4+dcMCOvtz3Hje0Zwwe3LF4+958x6+98z3mN42nWvPuJZ9xu3jedz7lGe5cPlvoX0inPcv2POYms7HGWMZKewyJN8WSUIWerPNU/J3/+c6frriOqJCcvnU8/noe77esM9OWMv+Asnr6RENRL68vosLblqIJg0+cfqbzH/zeg4Zuw+/WvsmU3q3mjt2Dy8vrlYLEh5q19W03IYrb7xxmVpLNnZz3o0L0Q2DP1/0VubuOcEVYJ0zaQ6/PvnXTI2Nh3u+DItvhH1Phg/9qaqlUM2uqUoKQ4xDLF7byafnL0QRgtsvPobDZ453vS+l5Pblt3PlgivZe9zeXHvytcwYM6N4wIZFcPsnINtjjvfQorpPWn1ea0HF2IMTdXSHenl9F+fftBAB3HbxMRw2Y7zvsYY0uPbFa/njq3/kqGlH8Yu3/4LxCY/jDR0euYIf6b/kzeSh7HvxHTBmt6rnYqOi/TZM2GVIPhVrg37oz3Q35ft/c+dn+WPPU8zU4f/N/Tknzmtsw4BUTC1UoQTrJoo1vmKfFx57fRuX/N8LTBqjcOSRj/K3VfdxxoRD+OGrj5NIToALHoCSzk71IBktV7uVm3k37gF67PVtfOHPLzAuGeXmi49jv6ntrgDr6bNO54fH/5Bkpg9uPtNUsyd8BU7+rm8ZitKx1WLXDEef13+/toUv3vYi08clmH/B0ew1yb36yRt5fvr8T/nbG3/jpBkn8bMTf+YOFr70Z7jnUhgzHT75cFmfAy+bzQ/2OF0psV6IOVZrFfDQ0q186bYXmdQe45YL31qxhPBAfoBvPfUtHln3CB+a/SG+/dZvE1U9asOnu8w9Disf4l+xd3H3bpfyv3UQPJR48iOEXYbk261gSDrbNaLfq+UG+M5tZ/EvtnB4Os533/NXDthz34Z/j+3tFsgvnx622ttO/GXhOr511xL23x3Gz7qVxza+zCVj5vDZF+5DzDzGzCap80EoRTKqktG8SX7AqegavDnolmfX8L27X+Og6WP507lHMW1cwh1gPfwSPnv4ZxGbXzbV7MDOMjVbDdU8+arKb5B9Xu2xHTpjPDecO49J7e5Mla5MF1994qvmBqBDLuRLR37J3K0LZhG5h74Lz11ntrD7yE2eKxavFZgfCp58zXaNd3coKSU3Pr2GH/5rKYftMY4/nDuPqWP80yS39G/hS49+ieWdy7nsqMs456BzvPPmty+H2z4OXWvhvddww8IDSWj114MPlfwwos26OTLZnhH7zu6ON/ninR/hxWieE/qn8cNz7mLy2OGpQJeMqnT05902RrQxuwO9YBiSqx5czu+feJOj9s/SPfZ/WdG1g6uVPTjjlQfMipnvurpu/90LyZhKf9bd8NruHuVv1wweumHWa/nTU6t550FT+dXZc2mLR3ij8w2++MgX3QHWV/8O//wCpCaZK5bdj6jru5JRlR19/huAqnrV0TYY6Kz5+5xjO+XAqVz7X3NJxcpLMXzx0S+ybWAbPznhJ7xv3/cV3xzoMP331U+YzU1O/aHZsMVzbErNJB+30iGHYtdousEP713K/GfXcvrBu/HLj82tWK5gyY4lfPHRL5LW0lx78rWcOONE7wOX3w93XGRmRp17L+x1LKmXny+7J2uBp804zNhlSL7dKv6Uy40Mya9acT9ffOLrbIoITssdz48vuq6waWk4kIxFyOR1omqUiIhYds3w7ABN53S++teXuH/JFt555E6WaNeR0mLc1G1wyM6FZgXJoy5s2PclPIjQ264ZesGugZzGpbe/xENLt3L+8bP4znvmoCrCHWA94yYOnnig2eTjqWvMbJKP3gzt9ccBalXyjciu6c+aY3t42VbOO24W333vnLKqkk+sf4LL/3M5yUiSG8+4kcOmHFZ8c+tSuP3j0LMJzroO5lZufJGMquzsr62HQ80BSR+7pi+r8cU/v8Bjy7dz8Yn78I0zDqxYVfKBNQ/wnae+w+TkZK4/9XpmT/DIa5cSnvy5mf8+/XA4+/9gnBmP8Lona4FzM9RIYZch+WTMbBqd03qH/bueevJKLl95MyiCDyYu5Nuf+nLNZUwHi2RUKfifhSJIw1B7e1tvhovmL+KVjV2854SVPNVxIwekpvPrN5cyTUThU3fDrOMb+p1eAbyEmkAg3Nk1Q1Ty23oyXDh/Ea9t6uYHZx7MucfNMpf/S27kmsXXMGfSHH71jl+xm5owS8eu+DfMu8BMnxvkiiUZjVT0re3dn/6VKGuz5LZ0Z7hw/kKWbe4pjM0JKSU3vXYT1yy+hgMnHsivT/4109ocueTL7oE7PwPxMXDefTCz+p6OZEwl3VmjXVNtMrMRLb/Gm7vTXHDTIt7Y2suPP3BIxYqSUkp+//Lvue7l65g7dS7XnHQNk5Ie6cvZPvjH52DZ3XDYx8z8d7uyK3a8oX4lX3OAuYHYhUjeVPJ5rfGd3gswdG6941P8T//LTNVVPjjrSj5z6sh0ZDezNMybrtBlJ9oG/Tsa9h2vb+nhwpsW0dE/wDtP+A9P7riPd6b25MevPUNq2qHwsf+rWBJ5sPAK4AkhyisXDsGTX7a5hwtvWkhXOs8fz53HyQfuRk7PccWzV/DPN/9ZDLB2bYDb3gOdqxuyYknGKlsaESVCVIlWUPLVs2uWbOzmwvkL6cto/Onco8pSJLN6liuevcIdSLZIF8OAJ6+Gx39i1n7/2P/B2PJNUl5IVAkqO2FPZtVJ3trla9mQ9tj6szo3nHcUb9/ffzWV0TL899P/be683vdMvnfs97ybjHeugdv+C7Yvg9N+BMd+oSz/PVVHvMGJmBJDEYo7ljTM2HVI3qqNktOGx6PW+3fw07+cxV/UHvZNt/O5E27m9EP3H5bv8oJp15gbTwrNvGMp3wBVvXh8+Ta+8OcXSSXTHHbUXTy34wUuikzjC689hXLIh80NP7aSbjD8LI1UJFXiyQ+uYJc9tra4yt8+eywH7z6OnemdfPXxr/LCtheKAdYVD8EdF5oNwxu0YknFKit5qKHPq12wyyNg+PDSrXzp9hcZn4zy988dx0HT3TXrd6R3cOljl/LK9lf4/BGf5zOHfaYYeMz2mrs5X78XDv8veO81dbWtqyeFUlVUYkrMf8ViQ4jCCtU9tmM5cJp/845tA9v48mNfNndeH/llLjjkAu8A66rHzZiDlPCJv8N+p3h+XqKOzCH36YsRLze865C8VQ9DMxo/g3ZvWMjX7ruA5+NwUO8srvjQzRw4vbZWYo1CMqqS0w003XB3hxqiXSOl5KZn1vCjfy1j1vRuxLQbWdm9g59kE7xv9UI49Qqz2t4wdp6P+wTwkpGkd3ZNjSQvpeT6J1dx5QOvc+C0sfzpvHlMH5dk2c5lfPmxL7Mzs5Or3341Z+x1Ovznf+DRH8G0Q01v1qf+e72w1a6U0rcaYsWuSTHvgl1SSv7wn1X89P7XOXSPcfzxU/PKinEt3bmUSx+7lO5sN7846RecutepxTc7VplqdscbcPpP4ZjP1X2NTZKvvUlPIpKoKbVQRlMsXbuZi55Y5Ds2J17a9hJfefwr9Of7ueakazhlLw/iltLMFvr3d2Hy/vDxP5vdunxQLZZSCSHJDxPi8XEIKckbjf2fu3zh77n05V+zLaYwd+AUfnn+1UxsG3pGSb1IxoqFvIqe/JQhbZTJ5HW+dder3PnCRuYetIb16k2M0+LctK2TQ/O6qXRm+xYpbRiSUZWcZqAb0hUoLHTBsmFXo6xhYsvkdS6/4xX++dIm3nPodK7+yGGkYhHuefMefvDsD5iQmMD8M+Zz8Ji94G/nwtJ/WtUUf93QFYtdZTOTN3wzQSrWII86doDaGSo5c2x3v7yJdx86jf/5yBFln333m3dzxbNXFMZ50KSDim+++Sj87XyT1D95J+xz0uDGFqs+gTmRiqaqbvcfyGn05yK8sWErZx6+O1d+6LCKCQ13vHEHP3r+R0xLTfMPsOb6zZaES/5uVkX9wO/N2EPFc1XJ65K8bhRaWdaKkOSHCSKWIikleaNBUW1D555/XsQPu54nIVROjFzKlRedTzzSnC6HSSsNzib5/nw/JNsqLuUrYVNXms/csphXN3Zy/FELeaXvLuZGpvKLFS8yedKBcP6tFZVOI2F3JOrLaoxLFjeplNk1ilJT56BNXWkuvmURr23q4f+dfgCXnLQvmtS4csGV3LrsVubtNo+fv/3nTEp3w59Oq+jNDhUpZ39eH5JPqBW6JsWcq5eJrO8Y4DO3LGbZluLYnASb1/NctfAqbl9+O0dPO5qrTryqGHiUEp65Fh7+Hkw5yFyxWM0uBoNkTEU3ZMUJzIn2aDt9FezF9R0DXHzLYq7JqhyxW4z3l9TwdyKv57ly4ZX8ZflfOHb6sVz99qu9q1t2rIa/nANbX4NT/htO+GpN19jZzGYwJF86aa/vGCCqKr5lnYeCXYbkiaZIGhKtASSf61rHz+44m79FepmRTfHBOb/l0287qia1MlwoKMKcaddsT2+37Atp7gCtQ30+vnwbX/vry2RlJ0ccfTev9L7MR5SJfPP1RUQP+TCc+ethKQDmh7EJk9h7M3kXyScjyXLlV2UH6MNLt/L1v7+Mpkv++Kl5nHLQbmzp38LlT17OC9te4JyDzuGr875KdOndZokCoVT0ZocKVwclH7TH2unN+2SFOdJGH166lf/395fRDckN5x1VqK9jY0v/Fi578jJe3PYi5x18HpceeWmxguRAh5lN8sYDMOcsM0WyQjXFWuC8brWQ/JjYGPry3iT/wJItXPb3l5HA9KmTGTfWX7is713P5U9ezqs7XuX8g8/nS0d+yV0p08aye82+wlD3qtTZqGdMwmN3bAWUJgw8sGQzl9/xKkfuOZ4bz298l9Rdh+Rj7SSlgU6VbdNVsPLF+Vy+6EreiKkc0Ls333zfn3jLXsPX0apW2GQxkNeKNoa95Mz21ETyWU3nyvuXc8PTq9lr5hriE25jU3+aH/ZqvL/jtUF7s0PFmIR5m/Zm3ClrbdE2dqRLsod8doBmNZ2f3f86Nz69hjnTx/Kb/5rLPlPaeWjtQ3z/me+jGZq58WfmKXDvV+GFm2HG0fChP8KE2ps814tEgSz80/HGxsayoW+D95vWdf3dw69w5ctJ5kwfy28/cWTZNv77V9/PD5/7Ibqhm3GGWWcU31z7DPz9QhjYUXM1xVow1pqQezL5qs05wFTypdczk9f58b+Wcctzazlsxjiu/fhcxv1rnK8ld//q+7ni2SsQCP7n7f/DabNOKz8oNwAPfsusMTT9cLMqap0rlsLzNojgqy1O0jmdK+5dym0L1nHYjHF8/8yDq//xILDrkHw0SVKCweCUvD7QwU3/vJD/zbxBRFU5Rvs4V57/9ab4716wFW73QL7o+dnliwc6YEzl+tlLNnZz2d9fYenWrRx6+H9Yk3uUA+VYrlq3lr3HzoJP31b3bs5GoUAWaXef0/Hx8bySfcV9sMcu35fXd3H5Ha/w+pZezjtuFt9894Fk9D7+++n/5q6Vd3HIpEO48sQr2bNjHfz+BDPoeMJX4R3fAq8aJg1E4bqVjM2JsbGx9Oa8lfyqbsk+wGOvrOHCE97NZWcc4LIMu7PdXLngSu5ZdQ+HTTmMn53wM2aOtdJcc/1mMPm535kkd+FDDb3G9uTck6ktn7w91s7anrWF319Y18nlf3+FFdv6+PQJe3PZGQcSiyieqcEdmQ6uXHAl962+j8OnHM6VJ17JHu0e5azXL4R/XgI7VsDxl5qN4wexx2FMYZVSf6782NhY1nVv5n2/eYqV2/r4zNv34WunHmCObRiw65C8EMRR6Rf5sgBeRUjJa8//jite/Q1LYyp7Z8bxwcN+xbnHzWuqPVOKCW3mTdc5kGNsbCw9uR5kcoLZbDfd4ft3/VmNax56gxueXsW4ycvZ/ZC7WZfr5Pw0fGHLEmLzLoDTfjxs6ZG1wF72l5LFuPg4OrOd7sBeaqI5qeEc22qmjInzp3PncfKBU7lv9X1ctfAqurPdfPrQT3PJAf9F9NEfm8pu/F7wqX/W1NCkEZiYMgmmo78CycfH0lNSjqM3k+d//v0Grz63gjti8J3T9uKwd8wpvC+l5O437+YXi39Bd7abSw6/hIsOu6hoW6x8xGw23bUWjvq02aWrSrCxXhSuW4UJzIlxsXF0Zbvoy2r8/MHlzH92DdPGJrjx/BLrKVa05KSU3LPqHq5eeDV9+T4uOfwSPn3Yp4kqJZNzuhMeuQIW3Qhjd4dP/WPQAWWgIO529tfnDPRk8qzYLNk0sIP2jMYtFx7N22YPrxOw65A8MFaqdKh5OgdyTC4pyOSFzSv+zS+f+C4PRPppV1SOzb+P737s28ycOHJ+dK2wb7qO/jyTx0xGMzR6onHGQYH0nMjrBn9fvIFfPbyCbbk32PPgx9ipL2NGPsH3N25izth9zJ2NDd69OhjYE9jOPvcDNSE+Ac3Q6M/30x6z/OP2qcjNr/Ln59dyzUMr2NGX5RNv3ZPL33Ugb/Ys4bwHLuOFbS9w6ORD+d93XMuBK5+Ea+eZltaxXzDV+wjGGwqTc4Xt/+Pj4xnQBsjqWYSM8pdF6/n1I+bYvnLEbFgGhzk2bS7asohfv/hrXtz2IodPOZzrT72eAyYeYL65dSk89N+w8iGYuO+wXmP7nqx1+//ExCR6cj28/eqH6Og3+NQxe/H/zjiwEHgvwGrmvXDLQq5ZfA2v7niVw6cczveP/T77TdjPfayWNYn9yatNsXPMJfCObw55Qis+b7WNLavp3Pb8On7z2Ep6kwaxyWke/MoJjEvW36e2XuxSJD9ZSfKqMsCW7ow/yUvJm6/dwY3PX8MDShdGRDCnfxbnnPhz3nPogSN7wnVggqUId/ZlmT3VVAY7IhGT5HuKPdTTOZ27XtzI9f95k/UDrzJ5j+doi7yK1CN8a0cHH8mrRE78Nhzz+YYUF2sEpo5JIARs6XFbbXZWyPb0dtpj7QzkNNb2ptizYyPfvmsJR82awP9+8khy0eVc9tQXeXrj00xKTOK7b/k6H+ruQr31bOjZCPueAqf+wMyBH2HYZLG9z18R2uUFfv/0Yu54Lsu6jgGOmjWB6z/5FubuFoVlILvW8dymZ7lhyQ08t/k5Jicn8/1jv88HZn/ArB65YRE8/SuzPEFirJktdPTFg2qGXSumWT781p7KFml/VuPvizdw0+KdMBb2nKJzw7knlNW2B1O5Py0HmD9W4bkHL2C31G5ccdwVnLXfWcUqmQCZHnj5NjNbqHs9zHobnG7VoGkACkq+ygTWl9W4Y/EG/vCfVWzoTPPWvSdy1GGHMf+NR8nTC4Qk31BMik+m11jHus5uDtnDnU7Vt+MN/vHUr3hs239YEJdEVcn+A9N512Hf4xPHHk+kzjSpkUYiqrLb2Dird/bztkNNUliv9bNvrB3Z8SYvruvknpc3ccfLS0nHFjF+0gukpmwhLgXndXbxqbSk7ejPwnFfhOT45g6mBLGIwqS2OJu63GmEM8eY3vKjK5eydkM/d7+8iQ9lBd+NpvnF+9vpaX+DH754FW92v8nE+AQunXUW/9XVQeqe75jKfdbb4KzfmA0+moRULMLk9jhrd5ZnBBmG5IV1nTz4kkmSv35iAQdPmMcPzjyKkw6YghCCjX0beWjSVO5Y+1fWrL6JiYmJfH3e1/noAR8lme2HBX+Al2+HTS9AYpxZ6/64L9bVH3WwSMZUxqeibOwqT/+0x/aPlzZy90ub6MloHLD3VAaAb545lcOnj3cdv7FvI/etuo97Vt3D6u7VTI0ofHXOBXx87ucK9WAwdFj3HCy5A175K+R6zeD5mdea1kwD7dWxiQjjklHW+Fy3RWs7ufeVTdz1wkZ6sxpHzBzPjz9wKCfOnszTm3TmvwFrutcwOVm5U1UjsEuR/EGTZsP2dSxa+Rhvn3kKi1/9J8+ufpil6dW8Fs2TVRQmK3BMdg7vnfcN3nvEEbV79wHAflPbeW1jD/uNewsCwW2vPsm46EzeWPIAP1vWSax9BczoICFg32yOj3T28p7UniSO/5JZhKnBnmwjcdD0Mby0vgvdkKzd2c+L67p4fGU/SMGVj9+P6DmRubP7SI+Fj23ZjaXLvwDAocnp/Di2N6eve43469dCrB0Oep+pYofQyKSR2G9qG0s29qAbknUdAyxa08Fzqzp4euUOtvRkiMUU4vvCR0/I8pWj92dZxzKueeFWntv0HMs6lsHYBIfl0vzkmMs5LTaV+PoF8Mx7TWKXBux2KJzxM5h7zohf4wN2G8PL1nVbs7Ofl9Z18fTKHTy1cgfberMkogqnzpnGecfNYtZUg5P++mte2PYCe43di9c7Xuf5Lc/z/ObneaPzDQDmTp3LTw66kDPu+x7RIyZAxxrYsBDWPAWrHoO+reamuDnvh7debNbbGQYIIZg9tZ0lG7sL9+SiNZ08t2pnYWzxiMIZh5hjm7tncQf8fuNNS+ml7S+x59g9WbZzGYu3LWbPMXu6++Y26lyllA3/0MFi3rx5ctGiRcP2+d2bXuTUB89BR5AXIK2ZfUZOsIexB0fs+VE+fPzHmDa2eUHGoeDmZ9fw3/98zewmv8cNRNqXF96LSMlh2SzHpXOc0bYXe+33Ljj4/TD1IP8PDBDmP2M2uVAVgW6Y9+yEVJQxs+bTKYsZNlElyqHZPG/r7eL0/gFmahq072YqudmnwQHvbmoQ2Qs3PLWaK+5d6hrbxLYYb917IqcfPI1TDprKV578HM9vfr7wNxElwqGTD+UdM9/BO3t7mPngfxc/UKgwYx7s8w6Yc2ZZx6aRxB//s4of/WsZEUWgOcZ23L6TeMcBUzn9kGkuz/38B85n0dYiB8SUGHOnzuXY3Y/ljL3PMDNmDB2ufYtZJM5GajLs/TZzAp99+pBz/GvB9U++yU/ue9113Sa1xThmn0mcdvBuvPOg3WgrjSdY+OR9n+Sl7S8Vfo8oET40+0N855jvDOpchBCLpZTzPN/blUge4MEnfsndb9xLXJnA7uMP4ZhDP8pb9z2g7l1rQYRuSP7v+bVs7EwzbYLBDp5m2tgEs3t3cpiMkppyEMw8OtCK3Q+6Ibn52TVs6cmw35R2Dt59HAdOG0Nvvod73rzHVFbjZ3PYlMNI9G2H5feZKaS7HQJTDhjx3P56oOkGf16wjo1dafaZ3MYRMycwe2q7qzx1d7abe968B1VR2W/8fhw6+dCiTSGl6bX3bjbrruw+NzCWm6YbzH92Ldt6zet2yB7jOGC3Mb6lt3emd3LvqnuJKBH2n7C/e5xO9GwyNzMlx5uxlCkHjvg1zusGtz63lq09WfaZ3MbcPcez39T2mrLu7HHG1Bizx8/mkMmHeI+zRoQkHyJEiBCjGJVIvvXla4gQIUKE8MWQSF4IcYQQ4jkhxEtCiEVCiKMd731TCLFSCLFcCHH60E81RIgQIULUi6Fm11wF/EBKeb8Q4t3W7ycJIeYAZwMHA7sDDwsh9pdSDq4Ac4gQIUKEGBSGatdIwG7HMg6wd92cBdwupcxKKVcDK4HGl1cLESJEiBAVMVQl/2XgQSHEzzEnjOOs1/cAnnMct8F6rQxCiIuBiwH23LMx3XZChAgRIoSJqiQvhHgY8Cph+G3gFOArUso7hBAfBf4EvBPwyiHyTOORUl4PXA9mdk2N5x0iRIgQIWpAVZKXUvpW0hdC3Axcav36N+CP1s8bgJmOQ2dQtHJChAgRIsQIYaie/CbArsl6MrDC+vlu4GwhRFwIsTcwG1gwxO8KESJEiBB1Yqie/EXAr4QQESCD5a1LKV8TQvwVWApowOdryaxZvHjxDiHE2mrH+WAysKPqUaML4Zh3DYRj3jUwlDH7ti8L1I7XoUAIschvx9doRTjmXQPhmHcNDNeYwx2vIUKECDGKEZJ8iBAhQoxijCaSv77ZJ9AEhGPeNRCOedfAsIx51HjyIUKECBGiHKNJyYcIESJEiBKEJB8iRIgQoxijguSFEGdYJY1XCiG+0ezzGQ4IIWYKIR4TQiwTQrwmhLjUen2iEOIhIcQK698J1T6rlSCEUIUQLwoh7rV+H+3jHS+E+LsQ4nXrWh+7C4z5K9Y9vUQIcZsQIjHaxiyEuEEIsU0IscTxmu8YG1mqveVJXgihAr8F3gXMAT5ulToebdCAr0kpDwKOAT5vjfMbwCNSytnAI9bvowmXAsscv4/28f4KeEBKeSBwOObYR+2YhRB7AF8C5kkpDwFUzDLlo23MNwFnlLzmOcaSUu1nANdZPDcotDzJY5YwXimlXCWlzAG3Y5Y6HlWQUm6WUr5g/dyL+fDvgTnW+dZh84H3N+UEhwFCiBnAeyjWRILRPd6xwImYhf6QUuaklF2M4jFbiABJa+d8CrNcyqgas5TySaCj5GW/MTa0VPtoIPk9gPWO333LGo8WCCFmAXOB54HdpJSbwZwIgKlNPLVG45fAZYDheG00j3cfYDtwo2VR/VEI0cYoHrOUciPwc2AdsBnollL+m1E8Zgf8xthQThsNJF9zWePRACFEO3AH8GUpZU+zz2e4IIR4L7BNSrm42ecygogARwK/k1LOBfppfZuiIiwf+ixgb8wucm1CiHOae1ZNR0M5bTSQ/C5T1lgIEcUk+P+TUt5pvbxVCDHden86sK1Z59dgHA+cKYRYg2nBnSyEuJXRO14w7+UNUsrnrd//jkn6o3nM7wRWSym3SynzwJ2YzYdG85ht+I2xoZw2Gkh+ITBbCLG3ECKGGbC4u8nn1HAIIQSmV7tMSvkLx1t3A+daP58L/HOkz204IKX8ppRyhpRyFuY1fVRKeQ6jdLwAUsotwHohxAHWS6dgVnIdtWPGtGmOEUKkrHv8FMx402gesw2/MTa2VLuUsuX/A94NvAG8CXy72eczTGM8AXPJ9grwkvXfu4FJmJH5Fda/E5t9rsMw9pOAe62fR/V4gSOARdZ1/gcwYRcY8w+A14ElwC1AfLSNGbgNM+aQx1TqF1YaI2bnvTeB5cC7hvLdYVmDECFChBjFGA12TYgQIUKE8EFI8iFChAgxihGSfIgQIUKMYoQkHyJEiBCjGCHJhwgRIsQoRkjyIUYFhBCThBAvWf9tEUJstH7uE0JcN0zf+WUhxKca8Dm3CyFmN+KcQoQoRZhCGWLUQQjxfaBPSvnzYfyOCPACcKSUUhviZ70dOEdKeVFDTi5ECAdCJR9iVEMIcZKjFv33hRDzhRD/FkKsEUJ8UAhxlRDiVSHEA1bZCIQQbxFCPCGEWCyEeNDeel6Ck4EXbIIXQjwuhLhGCPGkVQf+KCHEnVat8B9Zx7QJIf4lhHjZqp3+Meuz/gO805o4QoRoKEKSD7GrYV/M8sVnAbcCj0kpDwXSwHssor8W+LCU8i3ADcCPPT7neKC0eFpOSnki8HvMLeqfBw4BzhNCTMKsDb5JSnm4NGunPwAgpTQwy8ke3tCRhghBSPIhdj3cL81CWK9iNqh4wHr9VWAWcAAmMT8khHgJ+A5mgahSTMcsC+yEXTPpVeA1afYAyAKrMAtOvYqp2K8UQrxNStnt+NttmFUYQ4RoKMLlYYhdDVkw1bMQIi+LQSkD83kQmAR9bJXPSQMJr8+2PivreN0AIlLKN4QQb8GsOfRTIcS/pZRXWMckrM8MEaKhCJV8iBBuLAemCCGOBbO8sxDiYI/jlgH71fPBQojdgQEp5a2YjTKOdLy9P/Da4E45RAh/hEo+RAgHpJQ5IcSHgV8LIcZhPiO/pJyA78esmFgPDgWuFkIYmNUIPwcghNgNSEurS1CIEI1EmEIZIsQgIYS4C7hMSrliiJ/zFaBHSvmnxpxZiBBFhHZNiBCDxzcwA7BDRRfFhs4hQjQUoZIPESJEiFGMUMmHCBEixChGSPIhQoQIMYoRknyIECFCjGKEJB8iRIgQoxghyYcIESLEKMb/B2hwa3wzGL7XAAAAAElFTkSuQmCC\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(model, monitors=['V'], inputs=['input', 5.])\n",
-    "runner.run(100.)\n",
-    "\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, plot_ids=[0, 1, 2], show=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Similarly, the setting of the initial values of a variable can also be realized through the above three ways: *Tensor*, *Initializer*, and *Callable function*. For example,"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "outputs": [],
-   "source": [
-    "hh = bp.dyn.HH(\n",
-    "   3,\n",
-    "   V_initializer=bp.init.Uniform(-80., -60.),  # Initializer\n",
-    "   m_initializer=lambda shape: bm.random.random(shape),  # function\n",
-    "   h_initializer=bm.random.random(3),  # Tensor\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "V:  Variable([-77.707954, -73.94804 , -69.09014 ], dtype=float32)\n",
-      "m:  Variable([0.4219371, 0.5383264, 0.8984035], dtype=float32)\n",
-      "h:  Variable([0.61493886, 0.81473637, 0.3291837 ], dtype=float32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('V: ', hh.V)\n",
-    "print('m: ', hh.m)\n",
-    "print('h: ', hh.h)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Initializing a synapse model"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Initializing a synapse model needs to provide its pre-synaptic group (``pre``), post-synaptic group (``post``) and the connection method between them (``conn``). The below is an example to create an [Exponential synapse model](../apis/auto/dyn/generated/brainpy.dyn.synapses.ExpCUBA.rst):"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "outputs": [],
-   "source": [
-    "neu = bp.dyn.LIF(10)\n",
-    "\n",
-    "# here we create a synaptic projection within a population\n",
-    "syn = bp.dyn.ExpCUBA(pre=neu, post=neu, conn=bp.conn.All2All())"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "BrainPy's build-in synapse models support **heterogeneous** synaptic weights and delay steps by using *Tensor*, *Initializer* and *Callable function*. For example,"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "outputs": [],
-   "source": [
-    "syn = bp.dyn.ExpCUBA(neu, neu, bp.conn.FixedProb(prob=0.1),\n",
-    "                     g_max=bp.init.Uniform(min_val=0.1, max_val=1.),\n",
-    "                     delay_step=lambda shape: bm.random.randint(10, 30, shape))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([0.9790364 , 0.18719104, 0.84017825, 0.31185275, 0.38157037,\n          0.80953383, 0.61926776, 0.73845625, 0.9679548 , 0.385096  ,\n          0.91454816], dtype=float32)"
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "syn.g_max"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([18, 19, 15, 21, 17, 24, 10, 27, 12, 20], dtype=int32)"
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "syn.delay_step"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "However, in BrainPy, the built-in synapse models only support homogenous synaptic parameters, like the time constant $\\tau$. Users can [customize their synaptic models](./synapse_models.ipynb) when they want heterogeneous synatic parameters."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Similar, the synaptic variables can be initialized heterogeneously by using *Tensor*, *Initializer*, and *Callable functions*."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Change model parameters during simulation"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "In BrainPy, all the dynamically changed variables (no matter it is changed inside or outside of a jitted function) should be marked as ``brainpy.math.Variable``. BrainPy's built-in models also support modifying model parameters during simulation."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "For example, if you want to fix the `gNa` in the first 100 ms simulation, and then try to decrease its value in the following simulations. In this case, we can provide the `gNa` as an instance of ``brainpy.math.Variable`` when initializing the model."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "outputs": [],
-   "source": [
-    "hh = bp.dyn.HH(5, gNa=bm.Variable(bm.asarray([120.])))\n",
-    "\n",
-    "runner = bp.dyn.DSRunner(hh, monitors=['V'], inputs=['input', 5.])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "1350538e39db46b9beb8d09e69084ae5"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# the first running\n",
-    "runner.run(100.)\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "3b025be2c08a4feea8ba1e5a2dc9dcc7"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# change the gNa first\n",
-    "hh.gNa[:] = 100.\n",
-    "\n",
-    "# the second running\n",
-    "runner.run(100.)\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Examples of using built-in models"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Here we show users how to simulate a famous neuron models: [The Morris-Lecar neuron model](../apis/auto/dyn/generated/brainpy.dyn.neurons.MorrisLecar.rst), which is a two-dimensional \"reduced\" excitation model applicable to systems having two non-inactivating voltage-sensitive conductances."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "outputs": [],
-   "source": [
-    "group = bp.dyn.MorrisLecar(1)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Then users can utilize various tools provided by BrainPy to easily simulate the Morris-Lecar neuron model. Here we are not going to dive into details so please read the corresponding tutorials if you want to learn more."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "0a26f894cd524017bcf91af6bd581670"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 576x432 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(group, monitors=['V', 'W'], inputs=('input', 100.))\n",
-    "runner.run(1000)\n",
-    "\n",
-    "fig, gs = bp.visualize.get_figure(2, 1, 3, 8)\n",
-    "fig.add_subplot(gs[0, 0])\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.W, ylabel='W')\n",
-    "fig.add_subplot(gs[1, 0])\n",
-    "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Next we will also give users an intuitive understanding about building a network composed of different neurons and synapses model. Users can simply initialize these models as below and pass into ``brainpy.dyn.Network``."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "outputs": [],
-   "source": [
-    "neu1 = bp.dyn.HH(1)\n",
-    "neu2 = bp.dyn.HH(1)\n",
-    "syn1 = bp.dyn.AMPA(neu1, neu2, bp.connect.All2All())\n",
-    "net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "By selecting proper runner, users can simulate the network efficiently and plot the simulation results."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1500 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "6ced45ee56a7438c97dae0985aa605e3"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 576x432 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 5.)], monitors=['pre.V', 'post.V', 'syn.g'])\n",
-    "runner.run(150.)\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, gs = bp.visualize.get_figure(2, 1, 3, 8)\n",
-    "fig.add_subplot(gs[0, 0])\n",
-    "plt.plot(runner.mon.ts, runner.mon['pre.V'], label='pre-V')\n",
-    "plt.plot(runner.mon.ts, runner.mon['post.V'], label='post-V')\n",
-    "plt.legend()\n",
-    "\n",
-    "fig.add_subplot(gs[1, 0])\n",
-    "plt.plot(runner.mon.ts, runner.mon['syn.g'], label='g')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
diff --git a/docs/tutorial_simulation/parallel_computing.ipynb b/docs/tutorial_simulation/parallel_computing.ipynb
new file mode 100644
index 000000000..98e869f7c
--- /dev/null
+++ b/docs/tutorial_simulation/parallel_computing.ipynb
@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parallel Simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "@[Tianqiu Zhang](mailto:tianqiuakita@gmail.com) @[Chaoming Wang](mailto:adaduo@outlook.com)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parameter exploration and selection is an essential part in brain dynamics modeling. BrainPy supports multiple kinds of approaches for parameter exploration. Technically, parameter exploration requires parallelization, because it involves simulating multiple instances of the model with different parameter settings. BrainPy supports parallelization of multi-threading and multi-processing on a single machine, and parallelization across multiple devices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "bm.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Thread-based parallelization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multi-threaded running of BrainPy models can be easily achieved.\n",
+    "\n",
+    "The first approach is directly using the Python's multi-threading support. The following pseudocode demonstrates that by utilizing the `threading` backend of `joblib` library, we can easily achieve parallel execution based on multiple Python threads. However, the multi-threading parallelization based on this approach will get stuck in the well-known issue of Global Interpreter Lock (GIL) of Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from joblib import Parallel, delayed, parallel_backend\n",
+    "\n",
+    "def run_model(par):\n",
+    "  model = YourModel(par)\n",
+    "  runner = bp.dyn.DSRunner(model)\n",
+    "  runner.run(<int>)\n",
+    "  return runner.mon\n",
+    "\n",
+    "\n",
+    "# define all parameter values need to explore\n",
+    "all_params = [...]\n",
+    "\n",
+    "# create a multi-threading environment for batch simulation\n",
+    "with parallel_backend(backend=\"threading\"):\n",
+    "  r = Parallel()([delayed(run_model)(p) for p in all_params])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We will use E-I balance network as a full example to show parallelization. In this example, we use multi-threading technique to test four different current values as input and visualize the result."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
+      "[Parallel(n_jobs=-1)]: Done   2 out of   4 | elapsed:    1.9s remaining:    1.9s\n",
+      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    2.2s remaining:    0.0s\n",
+      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    2.2s finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 864x576 with 4 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from joblib import Parallel, delayed, parallel_backend\n",
+    "\n",
+    "class EINet(bp.dyn.Network):\n",
+    "  def __init__(self, scale=1.0, method='exp_auto'):\n",
+    "    super(EINet, self).__init__()\n",
+    "\n",
+    "    # network size\n",
+    "    num_exc = int(3200 * scale)\n",
+    "    num_inh = int(800 * scale)\n",
+    "\n",
+    "    # neurons\n",
+    "    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
+    "    self.E = bp.neurons.LIF(num_exc, **pars, method=method)\n",
+    "    self.I = bp.neurons.LIF(num_inh, **pars, method=method)\n",
+    "\n",
+    "    # synapses\n",
+    "    prob = 0.1\n",
+    "    we = 0.6 / scale / (prob / 0.02) ** 2  # excitatory synaptic weight (voltage)\n",
+    "    wi = 6.7 / scale / (prob / 0.02) ** 2  # inhibitory synaptic weight\n",
+    "    self.E2E = bp.synapses.Exponential(self.E, self.E, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=0.), g_max=we, tau=5., method=method)\n",
+    "    self.E2I = bp.synapses.Exponential(self.E, self.I, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=0.), g_max=we, tau=5., method=method)\n",
+    "    self.I2E = bp.synapses.Exponential(self.I, self.E, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=-80.), g_max=wi, tau=10., method=method)\n",
+    "    self.I2I = bp.synapses.Exponential(self.I, self.I, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=-80.), g_max=wi, tau=10., method=method)\n",
+    "\n",
+    "# running EI network with different input current\n",
+    "def run_ei_net(bg_current):\n",
+    "  # instantiate EI net\n",
+    "  net = EINet()\n",
+    "  # initialize DSRunner\n",
+    "  runner = bp.dyn.DSRunner(\n",
+    "    net,\n",
+    "    monitors={'E.spike': net.E.spike},\n",
+    "    inputs=[(net.E.input, bg_current), (net.I.input, bg_current)],  # input is determined by bg_current\n",
+    "    numpy_mon_after_run=False,\n",
+    "    progress_bar=False,\n",
+    "  )\n",
+    "  # running simulation\n",
+    "  runner.run(1000.)\n",
+    "  # return variables for visualization\n",
+    "  return runner.mon.ts, runner.mon['E.spike']\n",
+    "\n",
+    "\n",
+    "with parallel_backend(backend=\"threading\"):   # using threading backend\n",
+    "  parallel = Parallel(n_jobs=-1, verbose=5)   # n_jobs=-1 means using all concurrent workers\n",
+    "  rs = parallel([delayed(run_ei_net)(c) for c in [19., 20., 21., 22.]])\n",
+    "  # visualization\n",
+    "  fig, gs = bp.visualize.get_figure(2, 2, 4, 6)\n",
+    "  for i, r in enumerate(rs):\n",
+    "    ax = fig.add_subplot(gs[i // 2, i % 2])\n",
+    "    bp.visualize.raster_plot(r[0], r[1], ax=ax)\n",
+    "    ax.set_title(f'bg_current = {i+19.}')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The second approach of realizing multi-threading parallelization is the vectorization map of JAX `jax.vmap`. `jax.vmap` vectorizes functions by compiling the mapped axis as primitive operations. It can avoid the recompilation of models in the same batch, and automatically parallelize the model running on the given machine. Different from the first approach, the multi-threading parallelization of `jax.vmap` is implemented outside of the Python interpreter, so that the GIL problem no longer exists. Following pseudocode demonstrates how simple of this parallelization approach is."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from jax import vmap\n",
+    "\n",
+    "def run_model(par):\n",
+    "  model = YourModel(par)\n",
+    "  runner = bp.dyn.DSRunner(model)\n",
+    "  runner.run(<int>)\n",
+    "  return runner.mon\n",
+    "\n",
+    "\n",
+    "# define all parameter values need to explore\n",
+    "all_params = [...]\n",
+    "\n",
+    "# batch simulation through jax.vmap\n",
+    "r = vmap(run_model)(*all_params)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We can modify the E-I balance network example into vectorization map version according to above structure."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 864x576 with 4 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jax import vmap\n",
+    "\n",
+    "# run_ei_net is already defined in previous blocks\n",
+    "rs = vmap(run_ei_net)(bm.asarray([19., 20., 21., 22.]))\n",
+    "# visualization\n",
+    "fig, gs = bp.visualize.get_figure(2, 2, 4, 6)\n",
+    "# return value from vmap is different from threading method\n",
+    "ts, spike = rs[0], rs[1]\n",
+    "for i, _ in enumerate(ts):\n",
+    "  ax = fig.add_subplot(gs[i // 2, i % 2])\n",
+    "  bp.visualize.raster_plot(ts[i], spike[i], ax=ax)\n",
+    "  ax.set_title(f'bg_current = {i + 19.}')\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Processor-based parallelization"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Multi-processing parallelization means running multiple models concurrently on separate Python worker processes, which can also avoid Python GIL problem. Users can utilize `multiprocessing` library or `joblib` package to write multi-processing program. Here we give a pseudocode of the multi-processing parallelization of BrainPy models with `joblib`:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from joblib import Parallel, delayed, parallel_backend\n",
+    "\n",
+    "def run_model(par):\n",
+    "  model = YourModel(par)\n",
+    "  runner = bp.dyn.DSRunner(model)\n",
+    "  runner.run(<int>)\n",
+    "  return runner.mon\n",
+    "\n",
+    "\n",
+    "# define all parameter values need to explore\n",
+    "all_params = [...]\n",
+    "\n",
+    "# create a multi-processing environment for parallel simulation\n",
+    "with parallel_backend(backend=\"loky\"):\n",
+    "  r = Parallel()([delayed(run_model)(p) for p in all_params])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next we still use E-I network example to help users understand how to write a parallel brain dynamic model."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 10 concurrent workers.\n",
+      "[Parallel(n_jobs=-1)]: Done   2 out of   4 | elapsed:    2.5s remaining:    2.5s\n",
+      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    2.5s remaining:    0.0s\n",
+      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    2.5s finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 864x576 with 4 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with parallel_backend(backend=\"loky\"):    # using loky backend\n",
+    "  parallel = Parallel(verbose=5)\n",
+    "  rs = parallel([delayed(run_ei_net)(c) for c in [19., 20., 21., 22.]])\n",
+    "  # visualization\n",
+    "  fig, gs = bp.visualize.get_figure(2, 2, 4, 6)\n",
+    "  for i, r in enumerate(rs):\n",
+    "    ax = fig.add_subplot(gs[i // 2, i % 2])\n",
+    "    bp.visualize.raster_plot(r[0], r[1], ax=ax)\n",
+    "    ax.set_title(f'bg_current = {i + 19.}')\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Multi-device parallelization"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "BrainPy support parallelization running on multiple devices (e.g., multiple GPU devices or TPU cores) or HPC systems (e.g., supercomputers). Different from the above thread-based and processor-based parallelization methods, in which the same model runs in parallel on the same device, device-based parallelization runs the same model in parallel on multiple devices.\n",
+    "\n",
+    "One way to express the multi-device parallelization of BrainPy models is using `jax.pmap` instruction. JAX delivers `jax.pmap` to express SIMD programs. It provides an interface to run the same model on multiple devices with different parameter values. It usage is analogy to `jax.vmap`. Following pseudocode presents an example to run BrainPy models on multiple devices."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from jax import pmap\n",
+    "\n",
+    "def run_model(par):\n",
+    "  model = YourModel(par)\n",
+    "  runner = bp.dyn.DSRunner(model)\n",
+    "  runner.run(<int>)\n",
+    "  return runner.mon\n",
+    "\n",
+    "\n",
+    "# define all parameter values need to explore\n",
+    "all_params = [...]\n",
+    "\n",
+    "# parallel simulation through jax.pmap\n",
+    "r = pmap(run_model)(*all_params)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "BrainPy also works well with job scheduling systems such as SLURM on a supercomputer center. Therefore, another way to express multi-device parallelization is to employ the classical resource management system. Following script demonstrates an example that submits a batch script to SLURM."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#!/bin/bash\n",
+    "#SBATCH -J <name>\n",
+    "#SBATCH -o <file name>\n",
+    "#SBATCH -p <str>\n",
+    "#SBATCH -n <int>\n",
+    "#SBATCH -N <int>\n",
+    "#SBATCH -c <int>\n",
+    "\n",
+    "python your_script.py"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f37317bd3e2379aba54e3aa76414bc918141342cb86849b10e642bf3607e7693"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/tutorial_simulation/simulation_dsrunner.ipynb b/docs/tutorial_simulation/simulation_dsrunner.ipynb
new file mode 100644
index 000000000..53dbf45df
--- /dev/null
+++ b/docs/tutorial_simulation/simulation_dsrunner.ipynb
@@ -0,0 +1,804 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Simulation DSRunner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "@[Tianqiu Zhang](mailto:tianqiuakita@gmail.com) @[Chaoming Wang](mailto:adaduo@outlook.com) @[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The convenient simulation interface for dynamical systems in BrainPy is implemented by ``brainpy.dyn.DSRunner``. It can simulate various levels of models including channels, neurons, synapses and systems. In this tutorial, we will introduce how to use ``brainpy.dyn.DSRunner`` in detail."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import brainpy as bp\n",
+    "import brainpy.math as bm\n",
+    "\n",
+    "bp.math.set_platform('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Initializing a DSRunner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Generally, we can initialize a runner for dynamical systems with the format of:\n",
+    "```python\n",
+    "runner = DSRunner(target=instance_of_dynamical_system,\n",
+    "                  inputs=inputs_for_target_DynamicalSystem,\n",
+    "                  fun_inputs=the_functional_inputs,\n",
+    "                  monitors=interested_variables_to_monitor,\n",
+    "                  fun_monitors=monitoring_variables_by_callable_functions,\n",
+    "                  dyn_vars=dynamical_changed_variables,\n",
+    "                  jit=enable_jit_or_not,\n",
+    "                  progress_bar=report_the_running_progress,\n",
+    "                  numpy_mon_after_run=transform_into_numpy_ndarray\n",
+    "                  )\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In which\n",
+    "- ``target`` specifies the model to be simulated. It must an instance of [brainpy.DynamicalSystem](../apis/auto/simulation/generated/brainpy.simulation.brainobjects.DynamicalSystem.rst).\n",
+    "- ``inputs`` is used to define the input operations for specific variables. It should be the format of `[(target, value, [type, operation])]`, where `target` is the input target, `value` is the input value, `type` is the input type (such as \"fix\", \"iter\", \"func\"), `operation` is the operation for inputs (such as \"+\", \"-\", \"*\", \"/\", \"=\"). Also, if you want to specify multiple inputs, just give multiple ``(target, value, [type, operation])``, such as ``[(target1, value1), (target2, value2)]``.\n",
+    "- ``fun_inputs`` is used to manually specify the inputs for the target variables. This input function should receive one argument `tdi` which contains the shared arguments like time `t`, time step `dt`, and index `i`.\n",
+    "- ``monitors`` is used to define target variables in the model. During the simulation, the history values of the monitored variables will be recorded.\n",
+    "- ``fun_monitors`` is used to monitor variables by callable functions and it should be a `dict`. The `key` should be a string for later retrieval by `runner.mon[key]`. The `value` should be a callable function which receives an argument: `tdt`.\n",
+    "- ``dyn_vars`` is used to specify all the dynamically changed [variables](../tutorial_math/variables.ipynb) used in the ``target`` model.\n",
+    "- ``jit`` determines whether to use [JIT compilation](../tutorial_math/compilation.ipynb) during the simulation.\n",
+    "- ``progress_bar`` determines whether to use progress bar to report the running progress or not.\n",
+    "- ``numpy_mon_after_run`` determines whether to transform the JAX arrays into numpy ndarray or not when the network finishes running."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Running a DSRunner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "After initialization of the runner, users can call `.run()` function to run the simulation. The format of function `.run()` is showed as follows:\n",
+    "```python\n",
+    "runner.run(duration=simulation_time_length,\n",
+    "           inputs=input_data,\n",
+    "           inputs_are_batching=whether_the_inputs_are_batching,\n",
+    "           reset_state=whether_reset_the_model_states,\n",
+    "           shared_args=shared_arguments_across_different_layers,\n",
+    "           progress_bar=report_the_running_progress,\n",
+    "           eval_time=evaluate_the_running_time\n",
+    "           )\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In which\n",
+    "- ``duration`` is the simulation time length.\n",
+    "- ``inputs``  is the input data. If ``inputs_are_batching=True``, ``inputs`` must be a PyTree of data with two dimensions: `(num_sample, num_time, ...)`. Otherwise, the ``inputs`` should be a PyTree of data with one dimension: `(num_time, ...)`.\n",
+    "- ``inputs_are_batching`` determines whether the ``inputs`` are batching. If `True`, the batching axis is the first dimension.\n",
+    "- ``reset_state`` determines whether to reset the model states.\n",
+    "- ``shared_args`` is shared arguments across different layers. All the layers can access the elements in ``shared_args``.\n",
+    "- ``progress_bar`` determines whether to use progress bar to report the running progress or not.\n",
+    "- ``eval_time`` determines whether to evaluate the running time."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Here we define an E/I balance network as the simulation model."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "class EINet(bp.dyn.Network):\n",
+    "  def __init__(self, scale=1.0, method='exp_auto'):\n",
+    "    super(EINet, self).__init__()\n",
+    "\n",
+    "    # network size\n",
+    "    num_exc = int(3200 * scale)\n",
+    "    num_inh = int(800 * scale)\n",
+    "\n",
+    "    # neurons\n",
+    "    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
+    "    self.E = bp.neurons.LIF(num_exc, **pars, method=method)\n",
+    "    self.I = bp.neurons.LIF(num_inh, **pars, method=method)\n",
+    "\n",
+    "    # synapses\n",
+    "    prob = 0.1\n",
+    "    we = 0.6 / scale / (prob / 0.02) ** 2  # excitatory synaptic weight (voltage)\n",
+    "    wi = 6.7 / scale / (prob / 0.02) ** 2  # inhibitory synaptic weight\n",
+    "    self.E2E = bp.synapses.Exponential(self.E, self.E, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=0.), g_max=we, tau=5., method=method)\n",
+    "    self.E2I = bp.synapses.Exponential(self.E, self.I, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=0.), g_max=we, tau=5., method=method)\n",
+    "    self.I2E = bp.synapses.Exponential(self.I, self.E, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=-80.), g_max=wi, tau=10., method=method)\n",
+    "    self.I2I = bp.synapses.Exponential(self.I, self.I, bp.conn.FixedProb(prob),\n",
+    "                              output=bp.synouts.COBA(E=-80.), g_max=wi, tau=10., method=method)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Then we will wrap it into DSRunner for dynamic simulation. ``brainpy.dyn.DSRunner`` aims to provide model simulation with an outstanding performance. It takes advantage of the [structural loop primitive](../tutorial_math/control_flows.ipynb) to lower the model onto the XLA devices."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/10000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "0f8d13f70ddf4654ad7b57bceb0d2eb5"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# instantiate EINet\n",
+    "net = EINet()\n",
+    "# initialize DSRunner\n",
+    "runner = bp.DSRunner(target=net,\n",
+    "                     monitors=['E.spike'],\n",
+    "                     inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                     jit=True)\n",
+    "# run the simulation\n",
+    "runner.run(duration=1000.)\n",
+    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We have run a simple example of using `DSRunner`, but there are many advanced usages despite this. Next we will formally introduce two main aspects that will be used frequently in `DSRunner`: monitors and inputs."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Monitors in DSRunner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In BrainPy, any instance of ``brainpy.dyn.DSRunner`` has a built-in monitor. Users can set up a monitor when initializing a runner. There are multiple methods to initialize a monitor. The first method is to initialize a monitor is through a list of strings:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "outputs": [],
+   "source": [
+    "# initialize monitor through a list of strings\n",
+    "runner1 = bp.DSRunner(target=net,\n",
+    "                      monitors=['E.spike', 'E.V', 'I.spike', 'I.V'],     # 4 elements in monitors\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                      jit=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "where all the strings corresponds to the name of the variables in the EI network:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(Variable([-56.016644, -56.34871 , -56.016064, ..., -55.79087 ,\n           -55.847343, -58.383217], dtype=float32),\n Variable([False, False, False, ..., False, False, False], dtype=bool))"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.E.V, net.E.spike"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Once we call the runner with a given time duration, the monitor will automatically record the variable evolutions in the corresponding models. Afterwards, users can access these variable trajectories by using .mon.[variable_name]. The default history times .mon.ts will also be generated after the model finishes its running. Let’s see an example.\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "59ec6d0a4d1b4b8b9aa5513edf6e2f85"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner1.run(100.)\n",
+    "bp.visualize.raster_plot(runner1.mon.ts, runner1.mon['E.spike'], show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The second method is similar to the first one, with the difference that the index specification is added. Index specification means users only monitor the specific neurons and ignore all the other neurons. Sometimes we do not care about all the contents in a variable. We may be only interested in the values at the certain indices. Moreover, for a huge network with a long-time simulation, monitors will consume a large part of RAM. Therefore, monitoring variables only at the selected indices will be more applicable. BrainPy supports monitoring a part of elements in a Variable with the format of tuple like this:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "015195a7a02e466b8e36662dbd8b1a75"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The monitor shape of \"E.V\" is (run length, variable size) = (1000, 3200)\n",
+      "The monitor shape of \"E.spike\" is (run length, index size) = (1000, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize monitor through a list of strings with index specification\n",
+    "runner2 = bp.DSRunner(target=net,\n",
+    "                      monitors=[('E.spike', [1, 2, 3]), # monitor values of Variable at index of [1, 2, 3]\n",
+    "                                'E.V'],                 # monitor all values of Variable 'V'\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                      jit=True)\n",
+    "runner2.run(100.)\n",
+    "print('The monitor shape of \"E.V\" is (run length, variable size) = {}'.format(runner2.mon['E.V'].shape))\n",
+    "print('The monitor shape of \"E.spike\" is (run length, index size) = {}'.format(runner2.mon['E.spike'].shape))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The third method is to use a dict with the explicit monitor target. Users can access model instance and get certain variables as monitor target:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6f06fa76a47b4474a3769e9426718586"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The monitor shape of \"V\" is = (1000, 3200)\n",
+      "The monitor shape of \"spike\" is = (1000, 3200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize monitor through a dict with the explicit monitor target\n",
+    "runner3 = bp.DSRunner(target=net,\n",
+    "                      monitors={'spike': net.E.spike, 'V': net.E.V},\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                      jit=True)\n",
+    "runner3.run(100.)\n",
+    "print('The monitor shape of \"V\" is = {}'.format(runner3.mon['V'].shape))\n",
+    "print('The monitor shape of \"spike\" is = {}'.format(runner3.mon['spike'].shape))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The forth method is similar to the third one, with the difference that the index specification is added:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "7aee6d7cf86943069cf2d8298cde18b4"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The monitor shape of \"E.V\" is = (1000, 3200)\n",
+      "The monitor shape of \"E.spike\" is = (1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize monitor through a dict with the explicit monitor target\n",
+    "runner4 = bp.DSRunner(target=net,\n",
+    "                      monitors={'E.spike': (net.E.spike, [1, 2]),   # monitor values of Variable at index of [1, 2]\n",
+    "                                'E.V': net.E.V},                      # monitor all values of Variable 'V'\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                      jit=True)\n",
+    "runner4.run(100.)\n",
+    "print('The monitor shape of \"E.V\" is = {}'.format(runner4.mon['E.V'].shape))\n",
+    "print('The monitor shape of \"E.spike\" is = {}'.format(runner4.mon['E.spike'].shape))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In spite of the four methods mentioned above, BrainPy also provides users a convenient parameter to monitor more complicate variables: `fun_monitor`. Users can use a function to describe monitor and pass to `fun_monitor`. `fun_monitor` must be a dict and the `key` should be a string for the later retrieval by `runner.mon[key]`, the `value` should be a callable function which receives an arguments: `tdi`. The format of `fun_monitor` is shown as below:\n",
+    "```python\n",
+    "fun_monitor = {'key_name': lambda tdi: body_func(tdi)}\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Here we monitor a variable that"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2569fed5e4d24eb5ab10bbf083e9ceef"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner5 = bp.DSRunner(target=net,\n",
+    "                      fun_monitors={'E-I.spike': lambda tdi: bm.concatenate((net.E.spike, net.I.spike), axis=0)},\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
+    "                      jit=True)\n",
+    "runner5.run(100.)\n",
+    "bp.visualize.raster_plot(runner5.mon.ts, runner5.mon['E-I.spike'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Inputs in DSRunner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In brain dynamics simulation, various inputs are usually given to different units of the dynamical system. In BrainPy, `inputs` can be specified to runners for dynamical systems. The aim of ``inputs`` is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording.\n",
+    "\n",
+    "``inputs`` should have the format like ``(target, value, [type, operation])``, where\n",
+    "- ``target`` is the target variable to inject the input.\n",
+    "- ``value`` is the input value. It can be a scalar, a tensor, or a iterable object/function.\n",
+    "- ``type`` is the type of the input value. It support two types of input: ``fix`` and ``iter``. The first one means that the data is static; the second one denotes the data can be iterable, no matter whether the input value is a tensor or a function. The `iter` type must be explicitly stated.\n",
+    "- ``operation`` is the input operation on the target variable. It should be set as one of `{ + , - , * , / , = }`, and if users do not provide this item explicitly, it will be set to '+' by default, which means that the target variable will be updated as ``val = val + input``."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Users can also give multiple inputs for different target variables, like:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "inputs=[(target1, value1, [type1, op1]),\n",
+    "        (target2, value2, [type2, op2]),\n",
+    "              ... ]\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The first example is providing static input. The excitation and inhibition neurons all receive the same current intensity:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "14359133f60a499dabb1156cdd6aa586"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "runner6 = bp.DSRunner(target=net,\n",
+    "                      monitors=['E.spike'],\n",
+    "                      inputs=[('E.input', 20.), ('I.input', 20.)],  # static inputs\n",
+    "                      jit=True)\n",
+    "runner6.run(100.)\n",
+    "bp.visualize.raster_plot(runner6.mon.ts, runner6.mon['E.spike'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The second example is providing iterable inputs. Users need to set `type=iter` and pass an iterable object or function into `value`:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/12000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c6121665d53f4be38a63045ce8d026d6"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "I, length = bp.inputs.section_input(values=[0, 20., 0],\n",
+    "                                    durations=[100, 1000, 100],\n",
+    "                                    return_length=True,\n",
+    "                                    dt=0.1)\n",
+    "\n",
+    "runner7 = bp.DSRunner(target=net,\n",
+    "                      monitors=['E.spike'],\n",
+    "                      inputs=[('E.input', I, 'iter'), ('I.input', I, 'iter')],  # iterable inputs\n",
+    "                      jit=True)\n",
+    "runner7.run(length)\n",
+    "bp.visualize.raster_plot(runner7.mon.ts, runner7.mon['E.spike'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "By examples given above, users can easily understand the usage of inputs parameters. Similar to monitors, inputs can also be more complicate as a function form. BrainPy provides `fun_inputs` to receive the customized functional inputs created by users."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2708d95534de4b33a2e383f0e6fe29e4"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def set_input(tdi):\n",
+    "    net.E.input.value = bm.ones(3200) * 20.\n",
+    "    net.I.input.value = bm.ones(800) * 20.\n",
+    "\n",
+    "runner8 = bp.DSRunner(target=net,\n",
+    "                      monitors=['E.spike'],\n",
+    "                      fun_inputs=lambda tdi: set_input(tdi),    # functional inputs\n",
+    "                      jit=True)\n",
+    "runner8.run(100.)\n",
+    "bp.visualize.raster_plot(runner8.mon.ts, runner8.mon['E.spike'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/tutorial_simulation/synapse_models.ipynb b/docs/tutorial_simulation/synapse_models.ipynb
deleted file mode 100644
index a8ced94f2..000000000
--- a/docs/tutorial_simulation/synapse_models.ipynb
+++ /dev/null
@@ -1,1642 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "096f2ee4",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Building Synapse Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9c1ae039",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "@[Chaoming Wang](https://github.com/chaoming0625) @[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bed1c4f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Synaptic computation is the core of brain dynamics programming. This is beacuse in a real project most of the simulation time spends on the computation of synapses. In order to achieve efficient synaptic computation, BrainPy provides many useful supports. Here, we are going to explore the details of these supports. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1e518e11",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import brainpy as bp\n",
-    "import brainpy.math as bm\n",
-    "\n",
-    "# bm.set_platform('cpu')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f111708e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Synapse Models in Math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c5bbda2",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Before we talk about the implementation of synapses in BrainPy, it's better to understand the targets (synapse models) we are going to implement. For different illustration purposes, we are going to implement two synapse models: [exponential synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.DualExpCOBA.html) and [AMPA synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.AMPA.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee864f9e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 1. The exponential synapse model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "266c7fa7",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "The exponential synapse model assumes that once a pre-synaptic neuron generates a spike, the synaptic state arises instantaneously, then decays with a certain time constant $\\tau_{decay}$. Its dynamics is given by:\n",
-    "\n",
-    "$$\n",
-    "\\frac{d g}{d t} = -\\frac{g}{\\tau_{decay}}+\\sum_{k} \\delta(t-D-t^{k})\n",
-    "$$\n",
-    "\n",
-    "where $g$ is the synaptic state, $t^{k}$ is the spike time of the pre-synaptic neuron, and $D$ is the synaptic delay. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f30b788",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Afterward, the current output onto the post-synaptic neuron is given in the conductance-based form:\n",
-    "\n",
-    "$$\n",
-    "I_{syn}(t) = g_{max} g \\left( V-E \\right)\n",
-    "$$\n",
-    "\n",
-    "where $E$ is the reversal potential of the synapse, $V$ is the post-synaptic membrane potential, $g_{max}$ is the maximum synaptic conductance. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7de41ac6",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 2. The AMPA synapse model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07ffde7f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "A classical model of AMPA synapse is to use the Markov process to model ion channel switch. Here $g$ represents the probability of channel opening, $1-g$ represents the probability of ion channel closing, and $\\alpha$ and $\\beta$ are the transition probability. Specifically, its formula is given by\n",
-    "\n",
-    "$$\n",
-    "\\frac{dg}{dt} =\\alpha[T](1-g)-\\beta g\n",
-    "$$\n",
-    "\n",
-    "where $\\alpha [T]$ denotes the transition probability from state $(1-g)$\n",
-    "to state $(g)$; and $\\beta$ represents the transition probability of\n",
-    "the other direction. $\\alpha$ is the binding constant. $\\beta$ is the\n",
-    "unbinding constant. $[T]$ is the neurotransmitter concentration, and\n",
-    "has the duration of 0.5 ms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca0858af",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Moreover, the post-synaptic current on the post-synaptic neuron is formulated as\n",
-    "\n",
-    "$$I_{syn} = g_{max} g (V-E)$$\n",
-    "\n",
-    "where $g_{max}$ is the maximum conductance, and $E$ is the reverse potential."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a8e0ffa",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Synapse Models in Silicon"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6c96d37",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "The implementation of synapse models is accomplished by ``brainpy.dyn.TwoEndConn`` interface. In this section, we talk about what supports are provided for the implementation of synapse models in silicon. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e5f55f7",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 1. ``brainpy.dyn.TwoEndConn``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7aa075a6",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "In BrainPy, `brainpy.dyn.TwoEndConn` is used to model two-end synaptic computations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "297b0de9",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "To define a synapse model, two requirements should be satisfied:\n",
-    "\n",
-    "1\\. Constructor function ``__init__()``, in which three key arguments are needed.\n",
-    "  - `pre`: the pre-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
-    "  - `post`: the post-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n",
-    "  - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb).\n",
-    "\n",
-    "2\\. Update function ``update(_t, _dt)`` describes the updating rule from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0f5d5a8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 2. Variable delays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e9c232a",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "As seen in the above two synapse models, synaptic computations are usually involved with variable delays. A delay time (typically 0.3–0.5 ms) is usually required for a neurotransmitter to be released from a presynaptic membrane, diffuse across the synaptic cleft, and bind to a receptor site on the post-synaptic membrane.\n",
-    "\n",
-    "BrainPy provides several kinds of delay variables for users, including:\n",
-    "\n",
-    "- ``brainpy.math.LengthDelay``: a delay variable which defines a constant steps for delay.\n",
-    "- ``brainpy.math.TimeDelay``: a delay variable which defines a constant time length for delay."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Assume here we need a delay variable which has 1 ms delay. If the numerical integration precision ``dt`` is 0.1 ms, then we can create a ``brainpy.math.LengthDelay`` which has  10 delay time steps."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b9ced2ed",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
-     ]
-    }
-   ],
-   "source": [
-    "target_data_to_delay = bm.Variable(bm.zeros(10))\n",
-    "\n",
-    "example_delay = bm.LengthDelay(target_data_to_delay,\n",
-    "                               delay_len=10)  # delay 10 steps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_delay(5)  # call the delay data at 5 delay step"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_delay(10)  # call the delay data at 10 delay step"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Alternatively, we can create an instance of ``brainpy.math.TimeDelay``, which use time ``t`` as the index to retrieve the delay data."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [],
-   "source": [
-    "t0 = 0.\n",
-    "example_delay = bm.TimeDelay(target_data_to_delay,\n",
-    "                             delay_len=1.0, t0=t0)  # delay 1.0 ms"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_delay(t0 - 1.0)  # the delay data at t-1. ms"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "DeviceArray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_delay(t0 - 0.5)  # the delay data at t-0.5 ms"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0a2bf84",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 3. Synaptic connections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f83608c5",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Synaptic computations usually need to create connection between groups. BrainPy provides many wonderful supports to construct [synaptic connections](./synaptic_connections.ipynb). Simply speaking, ``brainpy.conn.Connector`` can create various data sturctures you want through the ``require()`` function. Take the random connection ``brainpy.conn.FixedProb`` which will be used in follows as the example, "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "61de48c2",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "example_conn = bp.conn.FixedProb(0.2)(pre_size=(5,), post_size=(8, ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88b50ec8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "we can require the connection matrix (has the shape of ``(num_pre, num_post)``:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b8e2ac09",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([[False, False, False, False,  True, False, False, False],\n          [False, False, False, False, False, False,  True, False],\n          [False, False, False, False, False,  True, False, False],\n          [False, False, False, False, False, False, False, False],\n          [False, False, False, False, False, False, False, False]],            dtype=bool)"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_conn.require('conn_mat')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dff17faf",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "we can also require the connected indices of pre-synaptic neurons (``pre_ids``) and post-synaptic neurons (``post_ids``):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3344a58d",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "(JaxArray([0, 0, 1, 1, 2, 2, 3, 4, 4, 4, 4], dtype=uint32),\n JaxArray([1, 4, 4, 5, 2, 3, 6, 1, 5, 6, 7], dtype=uint32))"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_conn.require('pre_ids', 'post_ids')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28e86024",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Or, we can require the connection structure of ``pre2post`` which stores the information how does each pre-synaptic neuron connect to post-synaptic neurons:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8db2a319",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "(JaxArray([0, 3, 4, 1, 0, 2, 7], dtype=uint32),\n JaxArray([0, 3, 3, 4, 7, 7], dtype=uint32))"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_conn.require('pre2post')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44fa4941",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "More details of the connection structures please see the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc2af88d",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Achieving efficient synaptic computation is difficult"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ecabe94",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Synaptic computations usually need to transform the data of the pre-synaptic dimension into the data of the post-synaptic dimension, or the data with the shape of the synapse number. There does not exist a universal computation method that are efficient in all cases. Usually, we need different ways for different connection situations to achieve efficient synaptic computation. In the next two sections, we will talk about how to define efficient synaptic models when your connections are **sparse** or **dense**.   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e494598",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Before we start, we need to define some useful helper functions to define and show synapse models. Then, we will highlight the key differences of model difinition when using different synaptic connections. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "bd522429",
-   "metadata": {
-    "code_folding": [],
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Basic Model to define the exponential synapse model. This class \n",
-    "# defines the basic parameters, variables, and integral functions. \n",
-    "\n",
-    "\n",
-    "class BaseExpSyn(bp.dyn.TwoEndConn):\n",
-    "  def __init__(self, pre, post, conn, g_max=1., delay=0., tau=8.0, E=0., method='exp_auto'):\n",
-    "    super(BaseExpSyn, self).__init__(pre=pre, post=post, conn=conn)\n",
-    "\n",
-    "    # check whether the pre group has the needed attribute: \"spike\"\n",
-    "    self.check_pre_attrs('spike')\n",
-    "\n",
-    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
-    "    self.check_post_attrs('input', 'V')\n",
-    "\n",
-    "    # parameters\n",
-    "    self.E = E\n",
-    "    self.tau = tau\n",
-    "    self.delay = delay\n",
-    "    self.g_max = g_max\n",
-    "\n",
-    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
-    "    self.delay_step = int(delay/bm.get_dt())\n",
-    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
-    "\n",
-    "    # integral function\n",
-    "    self.integral = bp.odeint(lambda g, t: -g / self.tau, method=method)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0d47e7ef",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Basic Model to define the AMPA synapse model. This class \n",
-    "# defines the basic parameters, variables, and integral functions. \n",
-    "\n",
-    "\n",
-    "class BaseAMPASyn(bp.dyn.TwoEndConn):\n",
-    "  def __init__(self, pre, post, conn, delay=0., g_max=0.42, E=0., alpha=0.98,\n",
-    "               beta=0.18, T=0.5, T_duration=0.5, method='exp_auto'):\n",
-    "    super(BaseAMPASyn, self).__init__(pre=pre, post=post, conn=conn)\n",
-    "\n",
-    "    # check whether the pre group has the needed attribute: \"spike\"\n",
-    "    self.check_pre_attrs('spike')\n",
-    "\n",
-    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
-    "    self.check_post_attrs('input', 'V')\n",
-    "\n",
-    "    # parameters\n",
-    "    self.delay = delay\n",
-    "    self.g_max = g_max\n",
-    "    self.E = E\n",
-    "    self.alpha = alpha\n",
-    "    self.beta = beta\n",
-    "    self.T = T\n",
-    "    self.T_duration = T_duration\n",
-    "\n",
-    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
-    "    self.delay_step = int(delay/bm.get_dt())\n",
-    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
-    "\n",
-    "    # store the arrival time of the pre-synaptic spikes\n",
-    "    self.spike_arrival_time = bm.Variable(bm.ones(self.pre.num) * -1e7)\n",
-    "\n",
-    "    # integral function\n",
-    "    self.integral = bp.odeint(self.derivative, method=method)\n",
-    "\n",
-    "  def derivative(self, g, t, TT):\n",
-    "    dg = self.alpha * TT * (1 - g) - self.beta * g\n",
-    "    return dg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d3640a4a",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# for more details of how to run a simulation please see the tutorials in \"Dynamics Simulation\"\n",
-    "\n",
-    "def show_syn_model(model):\n",
-    "  pre = bp.models.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
-    "  post = bp.models.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
-    "  syn = model(pre, post, conn=bp.conn.One2One())\n",
-    "  net = bp.dyn.Network(pre=pre, post=post, syn=syn)\n",
-    "\n",
-    "  runner = bp.DSRunner(net,\n",
-    "                       monitors=['pre.V', 'post.V', 'syn.g'],\n",
-    "                       inputs=['pre.input', 22.])\n",
-    "  runner.run(100.)\n",
-    "\n",
-    "  fig, gs = bp.visualize.get_figure(1, 2, 3, 4)\n",
-    "  fig.add_subplot(gs[0, 0])\n",
-    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['syn.g'], legend='syn.g')\n",
-    "  fig.add_subplot(gs[0, 1])\n",
-    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['pre.V'], legend='pre.V')\n",
-    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['post.V'], legend='post.V', show=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dde06bd8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Computation with Dense Connections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e5abebb",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Matrix-based synaptic computation is straightforward. Especially, when your models are connected densely, using matrix is highly efficient. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "984c65a4",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### ``conn_mat``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a5bad33",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Assume two neuron groups are connected through a fixed probability of 0.7. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "102c71e7",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "conn = bp.conn.FixedProb(0.7)(pre_size=6, post_size=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a791b6c",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Then you can create the connection matrix though ``conn.require(\"conn_mat\")``:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4bbb027f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "JaxArray([[False,  True, False,  True,  True, False,  True,  True],\n          [ True,  True,  True, False,  True,  True,  True,  True],\n          [False,  True,  True,  True,  True,  True,  True,  True],\n          [ True,  True,  True, False,  True,  True,  True,  True],\n          [False,  True, False,  True,  True,  True,  True, False],\n          [ True, False,  True,  True, False,  True, False,  True]],            dtype=bool)"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conn.require('conn_mat')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c925c9f4",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "``conn_mat`` has the shape of ``(num_pre, num_post)``. Therefore, transforming the data with the pre-synaptic dimension into the date of the post-synaptic dimension is very easy. You just need make a matrix multiplication: ``brainpy.math.dot(pre_values,  conn_mat)`` ($\\mathbb{R}^\\mathrm{num\\_pre} @ \\mathbb{R}^\\mathrm{(num\\_pre, num\\_post)} \\to \\mathbb{R}^\\mathrm{num\\_post}$). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c2553fc",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "With the synaptic connection of ``conn_mat`` in above, we can define the **exponential synapse model** as the follows. It's worthy to note that the evolution of states ouput onto the same post-synaptic neurons in exponential synapses can be superposed. This means we can declare the synapse variables with the shape of post-synaptic group, rather than the number of the total synapses. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b8e7b088",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class ExpConnMat(BaseExpSyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(ExpConnMat, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # connection matrix\n",
-    "    self.conn_mat = self.conn.require('conn_mat')\n",
-    "\n",
-    "    # synapse gating variable\n",
-    "    # -------\n",
-    "    # NOTE: Here the synapse number is the same with \n",
-    "    #       the post-synaptic neuron number. This is \n",
-    "    #       different from the AMPA synapse.\n",
-    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    # pull the delayed pre spikes for computation\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    # push the latest pre spikes into the bottom\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    # integrate the synapse state\n",
-    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
-    "    # update synapse states according to the pre spikes\n",
-    "    post_sps = bm.dot(delayed_spike, self.conn_mat)\n",
-    "    self.g += post_sps\n",
-    "    # get the post-synaptic current\n",
-    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "4acb4081",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "282c0c7284384fbca6fae38ad0a4baf1"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(ExpConnMat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1eb27017",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We can also use ``conn_mat`` to define an **AMPA synapse model**. Note here the shape of the synapse variable $g$ is ``(num_pre, num_post)``, rather than ``self.post.num`` in the above exponential synapse model. This is because the synaptic states of AMPA model can not be superposed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "37736f86",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class AMPAConnMat(BaseAMPASyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(AMPAConnMat, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # connection matrix\n",
-    "    self.conn_mat = self.conn.require('conn_mat')\n",
-    "\n",
-    "    # synapse gating variable\n",
-    "    # -------\n",
-    "    # NOTE: Here the synapse shape is (num_pre, num_post),\n",
-    "    #       in contrast to the ExpConnMat\n",
-    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    # pull the delayed pre spikes for computation\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    # push the latest pre spikes into the bottom\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    # get the time of pre spikes arrive at the post synapse\n",
-    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
-    "    # get the neurotransmitter concentration at the current time\n",
-    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
-    "    # integrate the synapse state\n",
-    "    TT = TT.reshape((-1, 1)) * self.conn_mat  # NOTE: only keep the concentrations\n",
-    "                                              #       on the invalid connections\n",
-    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
-    "    # get the post-synaptic current\n",
-    "    g_post = self.g.sum(axis=0)\n",
-    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "fab4f7cb",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "0ce7acb058bf4a0cab173b36c6702682"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(AMPAConnMat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1a02e48",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Special connections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69362ac5",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Sometimes, we can define some synapse models with special connection types, such as all-to-all connection, or one-to-one connection. For these special situations, even the connection information can be ignored, i.e., we do not need ``conn_mat`` or other structures any more. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7b3f691",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Assume the pre-synaptic group connects to the post-synaptic group with a all-to-all fashion. \n",
-    "Then, exponential synapse model can be defined as, "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "b41ef340",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class ExpAll2All(BaseExpSyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(ExpAll2All, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # synapse gating variable\n",
-    "    # -------\n",
-    "    # The synapse variable has the shape of the post-synaptic group\n",
-    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
-    "    self.g += delayed_spike.sum()  # NOTE: HERE is the difference\n",
-    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d1f3cca3",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "a7b5e04467f24a96bc7b877617b0ffae"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(ExpAll2All)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d37e8b1d",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Similarly, the AMPA synapse model can be defined as"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "01ce8789",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class AMPAAll2All(BaseAMPASyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(AMPAAll2All, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # synapse gating variable\n",
-    "    # -------\n",
-    "    # The synapse variable has the shape of the post-synaptic group\n",
-    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
-    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
-    "    TT = TT.reshape((-1, 1))  # NOTE: here is the difference\n",
-    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
-    "    g_post = self.g.sum(axis=0) # NOTE: here is also different\n",
-    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "51a07101",
-   "metadata": {
-    "lines_to_next_cell": 1,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "a69481cdc6e5423a9621cbbc969438c4"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(AMPAAll2All)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8eb7c494",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Actually, the synaptic computation with these special connections can be very efficient! A concrete example please see a [decision making spiking model](https://brainpy-examples.readthedocs.io/en/latest/decision_making/Wang_2002_decision_making_spiking.html) in BrainPy-Examples. This implementation achievew at least four times acceleration comparing to the implementation in other frameworks. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d819b14f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Computation with Sparse Connections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d0e7131",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "However, in the real neural system, the neurons are connected **sparsely** in essence. \n",
-    "\n",
-    "Imaging you want to connect 10,000 pre-synaptic neurons to 10,000 post-synaptic neurons with a 10% random connection probability. Using matrix, you need $10^8$ floats to save the synaptic state, and at each update step, you need do computation on $10^8$ floats. Actually, the number of synapses you really connect is only $10^7$. See, there is a huge memory waste and computing resource inefficiency. Moreover, at the given time $\\mathrm{\\_t}$, the number of pre-synaptic neurons in the spiking state is small on average. This means we have made many useless computations when defining synaptic computations with matrix-based connections (zeros dot connection matrix results in zeros).\n",
-    "\n",
-    "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b67256b8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4806dc08",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### The ``pre2post`` operator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "882dd9de",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "A notable difference of brain dynamics models from the deep learning is that they are sparse and event-driven. In order to support this significant different kind of computations, BrainPy has built many useful [operators](../apis/auto/math/operators.rst). In this section, we talk about a set of operators needed in ``pre2post`` computations. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "059255e0",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Note before we have said that exponential synapse model can make computations at the dimension of the post-synaptic group. Therefore, we can directly transform the pre-synaptic data into the data of the post-synaptic shape. [brainpy.math.pre2post_event_sum(events, pre2post, post_num, values)](../apis/auto/math/generated/brainpy.math.operators.pre2post_event_sum.rst) can satisfy your requirements. This operator needs the synaptic structure of ``pre2post`` (a tuple contains the ``post_ids`` and ``idnptr`` of pre-synaptic neurons). \n",
-    "\n",
-    "If ``values`` is a scalar, ``pre2post_event_sum`` is equivalent to:\n",
-    "\n",
-    "```python\n",
-    "post_val = np.zeros(post_num)\n",
-    "\n",
-    "post_ids, idnptr = pre2post\n",
-    "for i in range(pre_num):\n",
-    "  if events[i]:\n",
-    "    for j in range(idnptr[i], idnptr[i+1]):\n",
-    "      post_val[post_ids[i]] += values\n",
-    "```\n",
-    "\n",
-    "If ``values`` is a vector, ``pre2post_event_sum`` is equivalent to:\n",
-    "\n",
-    "```python\n",
-    "post_val = np.zeros(post_num)\n",
-    "\n",
-    "post_ids, idnptr = pre2post\n",
-    "for i in range(pre_num):\n",
-    "  if events[i]:\n",
-    "    for j in range(idnptr[i], idnptr[i+1]):\n",
-    "      post_val[post_ids[i]] += values[j]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff96270d",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "With this operator, exponential synapse model can be defined as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "94d26b81",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class ExpSparse(BaseExpSyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(ExpSparse, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # connections\n",
-    "    self.pre2post = self.conn.require('pre2post')\n",
-    "\n",
-    "    # synapse variable\n",
-    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
-    "    # NOTE: update synapse states according to the pre spikes\n",
-    "    post_sps = bm.pre2post_event_sum(delayed_spike, self.pre2post, self.post.num, 1.)\n",
-    "    self.g += post_sps\n",
-    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "afd6a770",
-   "metadata": {
-    "lines_to_next_cell": 1,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "2fde1a65713c4b52a193c91726cdc69c"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(ExpSparse)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eed2af26",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "This model will be very efficient when your synapses are connected sparsely. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6300cda5",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### The ``pre2syn`` and ``syn2post`` operators"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f39c2f8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "However, for AMPA synapse model, the pre-synaptic values can not be directly transformed into the post-synaptic dimensional data. Therefore, we need to first change the pre data into the data of the synapse dimension, then transform the synapse-dimensional data into the post-dimensional data. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae7c55b3",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Therefore, the core problem of synaptic computation is how to convert values among different shape of tensors. Specifically, in the above AMPA synapse model, we have three kinds of tensor shapes (see the following figure): tensors with the dimension of pre-synaptic group, tensors of the dimension of post-synaptic group, and tensors with the shape of synaptic connections. Converting the pre-synaptic spiking state into the synaptic state and grouping the synaptic variable as the post-synaptic current value are central problems of synaptic computation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89a546a3",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "![](../_static/pre2syn2post.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4aeef36",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Here BrainPy provides two operators [brainpy.math.pre2syn(pre_values, pre_ids)](../apis/auto/math/generated/brainpy.math.operators.pre2syn.rst) and [brainpy.math.syn2post(syn_values, post_ids, post_num)](../apis/auto/math/generated/brainpy.math.operators.syn2post.rst) to convert vectors among different dimensions.\n",
-    "\n",
-    "- ``brainpy.math.pre2syn()`` receives two arguments: \"pre_values\" (the variable of the pre-synaptic dimension) and \"pre_ids\" (the connected pre-synaptic neuron index).\n",
-    "- ``brainpy.math.syn2post()`` receives three arguments: \"syn_values\" (the variable with the synaptic size), \"post_ids\" (the connected post-synaptic neuron index) and \"post_num\" (the number of the post-synaptic neurons)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8400124a",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Based on these two operators, we can define the AMPA synapse model as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "fa62799e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class AMPASparse(BaseAMPASyn):\n",
-    "  def __init__(self, *args, **kwargs):\n",
-    "    super(AMPASparse, self).__init__(*args, **kwargs)\n",
-    "\n",
-    "    # connection matrix\n",
-    "    self.pre_ids, self.post_ids = self.conn.require('pre_ids', 'post_ids')\n",
-    "\n",
-    "    # synapse gating variable\n",
-    "    # -------\n",
-    "    # NOTE: Here the synapse shape is (num_syn,)\n",
-    "    self.g = bm.Variable(bm.zeros(len(self.pre_ids)))\n",
-    "\n",
-    "  def update(self, _t, _dt):\n",
-    "    delayed_spike = self.pre_spike(self.delay_step)\n",
-    "    self.pre_spike.update(self.pre.spike)\n",
-    "    # get the time of pre spikes arrive at the post synapse\n",
-    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
-    "    # get the arrival time with the synapse dimension\n",
-    "    arrival_times = bm.pre2syn(self.spike_arrival_time, self.pre_ids)\n",
-    "    # get the neurotransmitter concentration at the current time\n",
-    "    TT = ((_t - arrival_times) < self.T_duration) * self.T\n",
-    "    # integrate the synapse state\n",
-    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
-    "    # get the post-synaptic current\n",
-    "    g_post = bm.syn2post(self.g, self.post_ids, self.post.num)\n",
-    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "3ccfcf3b",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "91838581d14b44f5adc658b8c5c4e509"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 800x300 with 2 Axes>",
-      "image/png": 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WLAHanxTdmjYr24JDtE6jExxDnGVB8A7rD5dwxwdbsChw9cRMiag4yKYjZdz2/mbMFoXLxvWSiIqDbMsrZ/57m2i0KFw4JkMiKg6y81gFt7yzCaPZwjkj0n3OUQEnC+zbSxWy54fpgw8+4OGHH2bp0qWkpqZ265jh4eHExcW12FyJL8xaiYuLY9WqVZx33nkMHjyYBx98kGeffZZzzz0XAL1ez9y5czGbzdxwww0OH7+iooJ9+/bZ/u7Xrx8ff/wxn376KaNHj+aVV17hgQceAGgzSbo9mqtMVrYdR/TXPRRxlgXB4+wtqOSmdzZibLRw1rA0nrh0pDgqDnCwsIp5b2+g3mThtCEp/P2K0eKoOEB2cQ2/XbKBWqOZkwYm8+yVY8RRcYDcklrmLt5AdUMjU/r1YOE1Y33OUQEHnZXk5GQMBkObiEdhYWGbyEhrli5dyrx581i2bBlnnXVWi/fS09OdOqY78QVnZdiwYXzzzTcUFhZSX1/Pvn37WLBgQYt98vPzOe+882wTnDXmzp3bZjbKJZdc0sKgmzt3bpualIsuuogDBw5QX1/PDz/8QElJCb179yYiIqJLeRWJDHQLMba7hzh7guBZTlTWM+etX6mqb2RSViL/vHacQ4aOzanx/uPWKxRVNTDnrQ2U15oYk5nAy9eNJ9Qh/an/Bqn6KK0xcsNb6ymtMTKqVzyLZk8gLMTx6y9Y0+jKa43MWfwrxdUNDOsZx+tzJhLhQBMoT+KQsxIWFsaECRNYsWJFi9dXrFjB9OnTO/zcBx98wNy5c/n3v//N+eef3+b9adOmtTnm8uXLOz2mO2h+wTb6SM1KR1RUVLBy5Uref/997rjjDpcd9+WXX2bDhg0cPnyYd999l6effpo5c+bY9VlLqwJxwTFEf91DalYEwXPUm8zc8s5GTlQ2MCg1hjdumOSzho4v0tBoZv57mzhWXkf/5GgWz51k9ww2AUxmC797bxN5pXX06RHFW3MnESNzaOym0Wxhwb+3kF1cQ6+ESN7+7SSf7kDq8P/Ze++9l9mzZzNx4kSmTZvGa6+9Rm5uLvPnzwfUWpJjx47xzjvvAKqjcsMNN/DCCy8wdepUWwQlMjKS+Hh1kuhdd93FKaecwt///ncuvvhi/vvf/7Jy5UpWr17tqvN0GLMP1Kx0xsUXX8yvv/7Krbfeytlnn+2y4x44cIDHH3+c0tJS+vTpw3333Wfr0tYVUiDePZpfchKZcpzm119xtTh7guAuFEXh/k93sO1oBQlRobw5ZxLxUb5r6PgaiqLw0H92sulIGbERIbwxZyI9osO8LZZf8cj/drE+u5SY8BDenDORlFhpVe8IT3y1h9UHi4kKM/DGnImkxnWdPeNNHHZWrr76akpKSnj00UfJz89n5MiRfPXVV/Tt2xdQ05Jyc3Nt+7/66qs0NjZy++23c/vtt9tenzNnjq14e/r06Xz44Yc8+OCDPPTQQwwYMIClS5e6ZcaKvfhCGlhntG5T7Cqef/55nn/+eac+KwXi3UMaFHQPRSIrguARXlt1mM+2HMOg1/HytePpkxTl1HGa0ph8+3nrapasyWHZxqPodfDPa8fTPyXGqePYulkFl/p4b90R3luXi04HL1wzlkFpsU4dJ1i7gS3bkMfiX3IAeO6qMW6bK+hKnIqZ3Xbbbdx2223tvqc5IBr2GtVXXHEFV1xxhTPiuIzmP5i+ngbmiyhSM9AtRH/dQ9LABMH9rD5QzN++2QvA/10wnOkDk70skX+x7nAJj3+5B4C/nDeMUweneFki/2LTkVIe/nwXAH+YNYQzh8lALUfYllfOA//ZAcDdZw3inJE9u/iEb+B7Jf9uxJEBh74eWfEVLM1yl8RY7B4tI1MNDl2vQss0utJaI2a5hwXBpRRW1nP30i0o1hbFN0zr65LjBstPXUl1A3d+sMXWonjeSf1cdOTgUGBZjdE2C+SC0T353akDvC2SX1FRZ+L2f2/GZFaYNSKNO88Y5G2R7CYoqpFCQ0PR6XQUFRWRkpLSYVtFi9GIYrV4jEoj9fVBoR6nUBQFo9FIUVERer2esLCwFsZ2dUMj9SazFFw6QHP9mcwKVQ2NPl3w5ms015+iqJ1OkmIkj1kQXIHZonD30q0UVxsZmh7LIy6YpRJMDWYtFoV7lm2jsKqBgakxPO6CFs/B1CFaURT+8PE2jlfU0y85mr9dPtpl+guGhUFFUfjzJ9s5WlZHZo9Inr5yjF+1yA4Ka9xgMNC7d2+OHj1KTk5Oh/udqKi3RVT0OtBVR3pIQv8lKiqKPn36oNfrad2ToLTGSEaC6NBeWv9ellYbxVlxgDb6qxFnRRBcxT+/P8iaQyVEhRn457XjZSHKQRatOsSq/UVEhOr517XjpfOXg7y5OpuVewoJC9Hzz2vHSecvB3l33RG+3llAqEHHP38z3u9si6D5vx0TE8OgQYMwmUwd7vPnV9e26CL09V0nExYiP8gdYTAYCAkJadarvKW1KM6KY1ha6a+kxkhWcrSXpPE/2tOf/wS5BcF32XSklBe+2w/A45eMZGCqcwXhbQiSORdb88p5drmqv0cuGsGQdOcKwjsi0PW381gFf/tarZN66ILhjMiI97JE/sW+gioe/0Ktk/rzucMYk5ngXYGcIGicFVCNa4OhY+cjv9pMYZXZ9neNWU9cjG+3c/MltBIBnU798ZT2sY7RWn8loj+H0JyVJv1J3ZQgdJdaYyP3LtuGRYHLxvfisvG9vS2SX1FvMnPfsq2YrXUWV03MdNmxdUGQSNfQaOa+ZdtotKh1FtdP6eOyYwdDGp2x0cK9y7ZiNFs4c2gqN87I8rZIThFUBfZd0boeV4wdx9CMxSRrv3jRn2O01l+x6M8htNXFJv2JsycI3eVvX+/lSEktGfERPHzRCG+L43c8/e0+DhXVkBobzuOXdL9OJdhYuPIA+05UkRQdxpOXjhL9Ocg/fzjIruOVJESF8tTl/qs/cVaaoaUxhViLjsTYcQzN2UuNVaNRRaI/h1Ba6U+uP8fQnD3RnyC4htUHinln7REA/nHFGJfnuTfNuQjMPKZ1h0t465dsAP5++WgSolw8+NE2pyYw2XSkjFd/OgTAk5eNcnkNohaZCtQ0uu1Hy/nXDwcBNX1Tezb6I+KsNKPJ2FFvCFnZdgzN2UuNU/VXVCXGoiNo11+a6M8pNGc5Pd7qLIv+BMFpqhsa+ePH2wCYPbUvJw2SeSqOUGts5A8fb0NR4JpJmZw+NNXbIvkV9SYzf/jImn44rhezRqR7WyS/Qkuf09IPLxid4W2RuoU4K82wRQbixNhxBpuxHSv6c4YmZ0X05wytFxtEf4LgPM8t38/xinr69Ijiz+cO9bY4fscL3x0gr7SOXgmRPHD+MG+L43f864eDHC6uIS0unL9eKOmHjvLaT4c5UFhNckwYj1080tvidBtxVprRemVb0kgcQ3P20uIlDccZ2jjLoj+HUER/guASdh6rYMkaNX3p8UtGEu2mNrFNcy7ccnivsSe/kjd+VvX36MUjiHVTm1hbGl2AKfBgYRWLrOlfD184gvgoN+nPlkYXWPrLLq7hJWv610MXDCcx2sXph15AnJVmaPe7rGw7h6xsdw9FnOVu0WaxQa4/QXAYs0XhL5/twKLAhWMyOGVwirdF8issVv2ZLQrnjEjnzGFp3hbJr1AUhb98thOTWeGMoamcM1LSvxxBURQe+s9OjI0WTh6UzEVj/Dv9S0OclWZIGk73aOPsibHtEG0aFMj15xCtC+yLqhsCbsVRENzNu2tz2H60gtiIEB66wL3pS7YCZ7d+i2f5YEMuW3LLiQ4z8NeLhnvkOwNJfx9tOsqv2aVEhOp55KIRftu9ylt8vu04qw8WExai57GLA6f7nDgrzWgTGRBj2yFar2yX15owNlo6+4jQDLOlpf5qjWZqGhq9KZJfYbZeatr9azIrVNR1PARWEISWFFc32IYX/umcoX7dPcgblNUY+cc3+wC4b+YQesa7dyhyoBiiGhV1Jtvwx3vOGkxmjyiPfG+grGlVNzTy+Jfq8Mc7zxgYUEOlxVlphkXSwLqF5qwkRoXZ2j+X1IgO7UXTX0x4CJGh6vBSSQWzHy2KEhFqID5SzXEW/QmC/Ty7fD9VDY2M6hXPtZNdN3wvWHjhuwNU1JkYmh7LDdP6elscv+Of3x+gtMbIwNQYbjypn7fF8Tte/uEgRVUNZCVFccspA7wtjksRZ6UZrVvvVtSZaGg0d/YRoRmas2fQ60iOkboVR1Ga6y9WLYgT/dmP5uzpdZAco+qvUPQnCHaxJ7+SpRtyAbUoV693/6p9IBXYHyys4t116kya/7tgOCEGz5lXgaC/7OIalqzJAeCB84cRKvpziLzSWt5YrTZ1+Mt5wwgLCSzzPrDOpptoxnZiVBihBm0wpMxasRfFZizqSJEie4exNNefOHsOo92/Orn+BMEhFEXh8S93Y1Hg/FE9mdyvh7dF8jue+HIPZovCWcPSmD7QMzNpAikJ7Kmv9mAyK5w6OIXTh3hmJk0gpdH97Zu9GBstTB+QxNnDA6+pgzgrzRBjsXtoxqJeh81YlDQc+9GuP53ozymaR1ZSbFPsZbFBELriuz2F/HKwhDCDXmaqOMFP+4v4YV8RoQadzFRxgjWHilm++wQGvY4HRX8OszGnlC+356PTwYPnDw8oJ0xDnBUriqLYQoHNjW1xVuynydjW2dJwRH/20+TsSWTAGZTm+pPFBkGwi0azhSe/Votybzypn8eKmqF5ZMB/83AsFoWnvlL1d8O0LPp5sKi5aU6I/2KxKDxp1d+1k/swKC3WY99tm1PjxxpUo6Kq/q6emMnwjDgvS+QexFmxYml2rYqx6Dji7HWf5ml0tpofiazYTfPIqNT8CIJ9fLrlGIeLakiMCuX20wOrKNcT/G/7cfYWVBEbEcKdZwzytjh+xze7Cth5rJLoMAN3nyX6c5SVewrZmldORKiee2cO9rY4bkOcFSuWZhVW4qw4jtLa2RNj22HaS6MrqpI0JntpkUYXI2l0gtAVDY1mXlh5AID5pw5w26T1rvDXAmeT2cLzK9RWz7ee0t9tk9a7wl/nSZktCs9Z9TfvpH4kWX+3BfuwWBSeXa62yp47vV9AtxoXZ8VKc2dFp6eZsV3vLZH8itbOXrJWcyHGtt00T6MTZ89xbM6eXhYbBMEelm3I41h5HSmx4dwwLcvj3+/vqfWfbj5KTkktSdFh/HaG51vt+rv+/rv1GAcLq4mPDOWmU/p7/Pv9vRvdlzvy1aheeAjzT/W8/jyJOCtW2kQGxNhxiOZpdC2dPdGfPbROo2ty9kR/9qK0aF0s158gdEad0cxL3x8E4I4zBhIZZvCyRP5FQ6OZF79T9fe70wYQHR7iZYn8C5PZwkJrVO/WU/sT56Wonr/S2Cyqd9PJ/UmICvOyRO5FnBUrLSMDUnPhKJJG1z06S6Pz1xC/p2neoECbYl9aY8RsEf0JQmveXZdDYVUDvRIiuWaSdwZA6qwlzv54h36wPpdj5XWkx0Vw/VQZAOkoyzbmkVtaS3JMOHOnZ3lbHL/j0y3HOFys1prdeFKWt8VxO+KsWOmwwF5WZu2ipbHdFBmobmikziiDNbuiI2fP2Gihsr7RW2L5Fc1rVnpEh6HTqTnRZbWSiigIzak3mXlt1WEA7jpzUMANkHM3DY1mFv2k6m/BGQOJCPVOVErnp5NWTGYLL/9wCIDbThtAVJh3olL+6iw3mi386wc1qufNWjNPIr9QVlrUrDRLIymuMsrKth20NrZjw0MItz4Apci5a1qn0UWEGoi1phVIdKprWqbR6Qgx6OkRJR3BBKE9lm3Mo7jaSK+ESC4d38vb4vgdn20+RkFlPelxEVw5sbe3xfE7Pt96nGPldSTHhHHtFO9E9fyZr3YWcKSklsSo0KCJ6omzYkWxNP1389axdSYzNRIZ6JLWzl7zKeKFVdKkoCtaO3sgqYiO0DqNDkR/gtAeJrOFV61Rgfmn9ifU4EUzwFbg7D8LgmaLwqKf1KjATSf3IzzE+7U+fqQ+LBaFV6z6m3dSf69FpfwVRVF42RpV+e2MfkFTKyXOipXWxmJ0eAjR1oLDwkoxtruidRodQFqc2kbvRKUYi13ROo0OIDVOnD17aX7/GqzXX6rt+hP9CYJG81XtKydmelscv+OrHfnkWFe1fzPZu1EBf+wGtnz3CQ4WVhMbEcL1U31Ef37k7P2wr5C9BVVEhxmY44UOft5CnBUrrQvsocnYKZSV2S6xWNpGBtKsxrYYi13TXmQlXYxtu2mdRgeQbnP25P4VBJBV7e6iKAov/6jqb+704FnVdhWq/tSowJxpWUFRa+FKFEXhX9Zan+un9vXaXB9vIM6KlRbGjrYyGyvGtr205+xJZMV+WqfRQZP+CipEf13RnrPXpD+5fwUBfGtVG2xZYH6zsP3jviL25KvT1udM951aAcVPNPjLwRK2H60gIlTPb2dkeVucZteff+jv1+xSNh0pIyxEz7yTPD/Xx5uIs2Kl+YwGjfR4Wdm2l/acvTSJDNhNe2l0tjQmSQPrkvbT6OT6E4TmvPGzWqsiq9rOoXVQu25q34Cfa+EOXrNef9dM6iPT6p3g9Z+zAbhyQm/b8y1YEGfFSvMZDRrpsrJtN+06e2Is2o3SWRqYRAa6RNLoBKFzdhytYOORMkINOm7woagA+EeB+J78StYeLsGg1/ncXBB/0N/BwmpW7S9Cp4MbZwRXVMAVHCmp4bu9JwC4MciiKiDOig2LzdhusrZlZdt+2nP2tALxAjEWu8TSTmTAVvMj11+XtJ9Gp6VxymKDICz+RV2VvWB0BqmxvrEqq/OjCvElv+QAcM6IdDISIr0rjBW/0t8a9fo7a1gafZKivCyNiqY+f3D23l5zBEWB04akMCAlxtvieBxxVqw0HyinISvb9tOes6elgRWKsdglLY3ttt3U/Km1pzdoL41Ou3+Lqhtkir0Q1BRW1fO/7ccBfKJWwN8oqW7gs63HANGfM1TUmvhkk+jPWarqTSzbmAeo7YqDEXFWrCjtpYHFS2TAXtpz9jRju7qhkeoGmcLeGZZ20ui0yJSx0UJ5rckbYvkN7aXRJcWEo7dOsS+RwaRCEPPeulxMZoUJfRMZ3TvB2+LY8JcC+w9+zcXYaGFUr3gm9E30tjht8PW1rA835FJnMjM0PZZp/ZO8LY7f8fGmo1Q3NDIgJZpTBiV7WxyvIM6KlXaNxdimyICsbHdOe85eTHgIMdbWjlI30Dnt6S88xECitTWhOMyd014anUHfNJhU9Oe/NDQ0MHbsWHQ6HVu3bm3xnk6na7MtWrTIO4L6KA2NZv69/gggq9rOYDJbeHddk/58KfXKdyTpmEazhXfW+qb+NA36snVnsSi8vSYHgLkz+vmY/jyHOCtWOqu5MJotlMnKdqe05+yBzFqxF5v+WilQOqrZR3tpdNC8yF4iK/7KH//4RzIyMjp8f/HixeTn59u2OXPmeFA63+eLbfkUVxvpGR/BrBHp3hbH7/h6ZwEnKhtIjgnn/NE9vS2O37FyzwmOldeRGBXKxWN7eVscv+PH/YXklNQSFxHC5eODV3/irFhpL40pPMRAj2i1PaEYi51jc/Y6MLalbqVzmpzllq+L/uyjI2dZ2hf7N19//TXLly/nmWee6XCfhIQE0tPTbVtkpG8UP/sK//41F1CHyIUafOuR31Tg7Ltr21pU6topfQgP8c0hmr48J+T99er1d83kPjKE1An+bdXfVRMziQoL3iGkvvXL5UWULla2JY2kc9orsAfRn71YLO3rL130ZxcWi/pvR/oTZ8X/OHHiBDfffDPvvvsuUVEddw9asGABycnJTJo0iUWLFmHRLoYOaGhooLKyssUWqOw/UcWmI2WE6HVcObG3t8XxOw4XVbPucCl6HVwzKdPb4rTB1zOC8kprWX2wGIDfTPL+ENLW+LqznF9Rx/d7CwH4zRTf058nEWfFSntpYADpWhqTdATrlPbmrICkMdlLx86epNHZg+gvsFAUhblz5zJ//nwmTpzY4X6PPfYYH330EStXruSaa67hvvvu48knn+z02E899RTx8fG2LTPT94xQV/GBNapy5rBUn2lX7E8s3aB2YDptSKrPtCv2J5ZuyENR4KSByT7Trtif+GjjUSwKTO7XIyjbFTcneGNKrei45kJy3u2hI2dPjEX70PTXeqVM0pjso700TmjSX4Hcvz7Bww8/zCOPPNLpPhs2bGDNmjVUVlZy//33d7rvgw8+aPvvsWPHAvDoo4+2eL01999/P/fee6/t78rKyoB0WOpNZj7drLaL/c1k31yV9eXIgLHRwsebjgK+GVUB0GkF4j4YGGg0W2ztdn32+rP+64Pqw2xRbM7ytT6qP08izoqVjtJIJI3JPpqMxY7ScMRY7IyOIgOiP/tor5saNOmvUO5fn2DBggVcc801ne6TlZXF448/zrp16wgPD2/x3sSJE7nuuut4++232/3s1KlTqays5MSJE6SlpbW7T3h4eJvjBiLf7Cygos5Er4RITh6U4m1x/I4Vu09QUmMkNTacM4amelscv+P7vYUUVjWQFB3G2cPbvxeFjvn5QBHHyuuIjwzlnJHSGEOcFSsdGovxYuzYQ5Oz1/J1iQzYR1dpdOIsd05XkVHRn2+QnJxMcnLXcwJefPFFHn/8cdvfx48fZ9asWSxdupQpU6Z0+LktW7YQERFBQkKCK8T1a7TC+qsnZWJofWP4CL4cGfhwQ5P+QnysMUFrfFB9fGiNClwxoTdhIb6tP19ES+G8bHwvaUyAkzUrL7/8Mv369SMiIoIJEybw888/d7hvfn4+1157LUOGDEGv13P33Xe32WfJkiXt9suvr/ecgaF02I1J5jTYQ1c1AzKrpnM6TKOzDiYtrm6g0dx54XAw03HNmeqslNeaqDeZPS2W4CR9+vRh5MiRtm3w4MEADBgwgN691ULx//3vf7z++uvs3LmTQ4cO8cYbb/DAAw9wyy23BEXkpDMOFlbza7ZaGC6F9Y6TW1LLzweK0enULky+iq+m0R0vr+PHfWph+NU+mkIHTZkgvmaaFFbWs3KPtbBeUsAAJ5yVpUuXcvfdd/PAAw+wZcsWTj75ZM4991xyc3Pb3b+hoYGUlBQeeOABxowZ0+Fx4+LiWvTKz8/PJyLCcwWBHaUxSYG4fXTk7GlFnTKrpnM6uv6SosMx6HUoChRXG70hml/QUc1KXGQI4dZVvaIqSaULJEJDQ3n55ZeZNm0ao0eP5oUXXuDRRx/l2Wef9bZoXueTzWqtxelDUukZL4XhjqLp76SByWT2kMJwR/lsyzEsCkzp14P+QV4Y7gz/2XoMs0VhfJ8EBqfFelscn8DhNLDnnnuOefPmcdNNNwGwcOFCvv32W1555RWeeuqpNvtnZWXxwgsvAPDWW291eFydTkd6uvfy8pqG8rV8XVuZLa42YjJbfK5Pva/QkbEdFqInOSaM4moj+RV1trk1Qks6SqMz6HWkxoaTX1HP8Yo6W1qi0BItatc63UWn05EeH8GRklqOl9eJ4eGnZGVltYnMnnPOOZxzzjleksh3sVgU/rtFLay/fIJvR1VsrWN9KJFJURT+s1XV3xU+rj8bvqM+FEXhU6uz5+vXn6+iNcYQ/TXhkOVtNBrZtGkTM2fObPH6zJkzWbNmTbcEqa6upm/fvvTu3ZsLLriALVu2dLq/q3vld5RGkhgVRpjVQZHoSsd05OwBtpW9/HLRX0d0lEYH2Fpmiv46pqP7FyBDu/6k/bgQBKzPLuV4RT2xESFSGO4Em3PLOFJSS1SYwecLw30xC2znsUoOFdUQHqLnXB8vDPfFbmB78ivZW1BFmEHP+aN6elscn8EhZ6W4uBiz2dymy0paWhoFBQVOCzF06FCWLFnC559/zgcffEBERAQzZszgwIEDHX7G1b3ylQ6MRb1eZ6sbEGOnYzo1FhPUaMDxijpPiuRXdJRGB9DTGk3JF/11SEeRPYCecv0JQcRnW9RV7fNH9ZTCXCfQVrXPGZke1BPDneVT6/V39vA0YiNCvSyN//Efa1T09KEpJERJJoqGUzlNrQ0CRVHaNRLsZerUqVx//fWMGTOGk08+mWXLljF48GBeeumlDj9z//33U1FRYdvy8vKc/n7oeM4FNK3MHi8XY6cjOnL2oCmyclwiAx3SmbGtRVaOyfXXIR2l0YHcv0LwUG8y8/UOdeHw0nG9vCyN/fhKgXNDo5kvtucDcNk4/0nB8ZU0ukazhf9tOw6oXawExzBbmlIQL/Wj688TOLRskJycjMFgaBNFKSws7LCnvTPo9XomTZrUaWTF1b3yzZaOje1eCWJsd0Wnzp62si3GYod0lkaXoUVW5PrrEEmjEwR1NkhVQyO9EiKZlNXD2+L4HT/uK6KizkRaXDjTBiR5W5wu8bVuYD8fKKa42khSdJhfzPax6c9HvOW1h0o4UdlAfGQopw/1ff15EociK2FhYUyYMIEVK1a0eH3FihVMnz7dZUIpisLWrVvp2dNz+XodzbmAZmkkYmx3iF3GoqThdEhnaXQ9RX9d0mkanS0NTJwVIbDRUkguGZeB3kdnq/gyn1lTwC4e28tnZ9P4Mp9Zr78Lx2RIMyIn0PR3/uiehIdICmdzHE7IvPfee5k9ezYTJ05k2rRpvPbaa+Tm5jJ//nxATc86duwY77zzju0zW7duBdQi+qKiIrZu3UpYWBjDhw8H4JFHHmHq1KkMGjSIyspKXnzxRbZu3cq//vUvF5yifXRecyHGYld0NJQPJA3MHjpLo+tlSwMT/XVEZ2l0TZFRuX+FwKWkuoGf9hcB/pNC4ktzLipqTXy/V51t4T8pdL6jv+qGRpbv9q8UxKZudN6nzmjmm51aCqJ/6M+TOOysXH311ZSUlPDoo4+Sn5/PyJEj+eqrr+jbty+gDoFsPXNl3Lhxtv/etGkT//73v+nbty85OTkAlJeXc8stt1BQUEB8fDzjxo1j1apVTJ48uRun5hj21QyIsdgRSifOnmYsFlTWY7YosmLVDp2l0WkF9sXVDTQ0mmXFpR0670an6q+izkStsVGKZoWA5OudBTRaFEb1imdgqsy2cJRvdxVgNFsYmh7LsJ5x3hbH71i5+wT1Jgv9k6MZ3Tve2+L4Hd/vLaTGaCazRyQT+iZ6Wxyfw6mn9m233cZtt93W7ntLlixp81pXk8uff/55nn/+eWdEcRmdRQakQLdrOnP2UmLDCdHraLQoFFU1yKyQdugsja5HdBjhIXoaGi2cqGigT5LMCmlNZ5HR2IhQYsNDqGpo5Hh5vRhyQkDy1Q51Vfb80f7T7rSpdaz317a/1PTnh+1iva+9Zvob3bNbDZeCFe3+PW+U6K89JKnQSmeRAa1AvKLORE1DoyfF8hssndQMGPQ60qzDNaWjVft05izrdDrpCNYFnaXRQVN0VBYchECkuLqBdYdLAP80tr1NRa2JXw4WA3CePzl7PmLTVtWbbCmI5/nR9afzkTS6OqPZloIo92/7iLNipTNjMTYilNgINQgldSvt01lkAJocPtFf+3TmLIPMWumKztLooKnIXvQnBCLLd53AosCoXvFk9pDIq6Ms362m0A1Nj2VAikReHeX7vYUYG9UUsKHpsd4Wx+/4cV8hdSYzvRMjGdVLUujaQ5wVK03GTgfGthSJd0pn3dRApth3RWdpdCCRga7o2lmWujMhcNFSSM4d5dsTw1tjK3D28sq2TX8j/XNVu6tUe3cjKUzd40vRX5eIs2Kls8gKyKyQrtCG8nVlbEsaU/t0lkYHTbNWpP1u+3R5/9pm1cj1JwQWpTVG1koKmNNU1JlYbU0BO3+0nzl73hYAqGlo5Md9/pcCBtgU6M2aqXpTUwqY3+nPg4izYkVy3ruHvc6epOG0T1eRAdusFbn+2qXrNDqt/bg4e0Jg8e2uAswWhREZcfRNiva2OH7Hyt0nMJkVBqfFMDBVUpgc5bu9hTQ0WshKimJYT9Gfo/y4r5Bao5leCZGMkS5qHSLOipXOuglBM2dFjJ126Up/Yix2TldpdE3OsuivPSSNTghWmqfg+Bu2AmcvyuDP+tPwqv62SwpTd/hyhzqb5rxR6aK/ThBnxUqTsdP++5IG1jldR6ZEf53RZRqdLQ1M9NceXabRJTTpz9v53YLgKspqjKw5pKaA+bOx7S2q6k38fMDaBcwP9edt47bW2MiP+/03hcnWOttLj4R6k5nv95wA4Fw/1J8nEWfFSpeRFZm10ilddWPS9FdcbaTeZPaQVP5DV2l0WhpYVX0jVfUmT4nlN3SVRqfN9qk3WSirFf0JgcEP+woxWxSGpMXSL1lSwBzlp/1FGM1qF6vBaZLC5Cg/Hyim3mQhs0ckIzJkkKajrD1UQo3RTFpcOGN7J3hbHJ9GnBUrSicTsKFlGpiszLalK2MxISqUqDB18ro4fG3pSn8x4SEkRIUCcLRM9Ncai6VzZy88xEBqbDgAR8tqPSWWILiV7/aoq9pnD0/zsiTOYfu589Ij1d/1p+Etk+Q7a1TgrGFpXo/y+CMrm+lP39HDSwDEWbFhz8qsXgfGRgtF1Q2eFM0v6CqNTqfTkZmo9v/PE2O7DV1FpgCb/sRZaYumv84UqM2fEP0JgYCx0WIbxHfmsFQvS+N/NJotti5MZw7zT2fFm+atxaLY9HeWv+pP572hkIqi2Jxlf9WfJxFnxUpXNQOhBr2tSDyvVIyd1mg3u6GT1YHMHpr+ZGW7NV11swLonSj66wgt2mno5Okt+hMCifXZJVQ3NJIcE84YP00haQqseN5a3HSkjIo6EwlRoYzvk+Dx73cl3gisbD1aTnG1kdjwECZl9fCCBP7NruOVFFTWExlqYNqAJG+L4/OIs2LFFhnoZB/N2JE0krZ01Y0JoLctsiL6a01XkT1oigyI/trSVc0Z0CyyJ/oT/B9tVfbMoamSQuIEWgrOGUNSCTH4pynkzcwrLQXs1CEphIX4qf6s/3rD2dOuv5MHJRMRavCCBP6Ff15hbsC+yIDV2JGV2TbY0pg62cfm7Elkqg1dpdEBZCZKZK8jFHv010P0JwQGiqKwYrc1393P6y28hc3ZkxQcp1i5W1KYuoOtXkXuX7sQZ8WKPZEV28qsGDtt6GpOCEhkwB46TQOz1VyI/lrTVLLSdWRF9Cf4O/tOVHGsvI7wED0nDUz2tjhOo92unq4ZOFRUzeHiGkINOk4Z7L/6s+FhBeaV1rLvRBUGvY7ThqR49LsDgfyKOnYeq0SngzOGSr2ZPYizYsUuY0dbmRVjpw321FxIgXjHOBJZOVoms0Ja01XrZ2hKQxT9Cf7OSmtU5aSByUSGSQqJo2j6m9o/idiIUC9L4zzeSgPTogIT+yaSEBXmHSFcQJOz7NnngRbVG5eZQHJMuEe/218RZ8WKPcaORAY6xq6aFauzV1pjpKah0SNy+Qtag4fOC+zV66+6oZFymRXSgqY0xI711zNB7ejX0GihqEo6+gn+y0pJYeoW0oWpewRKy2dvodX7yP1rP+KsWHGkdezx8noazRYPSOU/2KO/uIhQ4iPVVSxx+FpiT2QlItRAinVWiOivJV3NSYJWHf1Ef4KfUlLdwLaj5UAgtCy2to714DdW1JrYeKQUCJwUHE/qr6ahkfXZJUDg6M+T1JvMrDmk6k+cZfsRZ0XDjm5MqbHhhIXoMVsU8ivqPSWZX6C1nuyqKY0UObeP9rDp7PqDlqlgQhP2pCFC0/Un+hP8ldUHi1EUGNYzjrS4CG+L43f8cqgYiwIDUqJt2RL+SmeRZHex9lAJJrNCnx5R9E+J8fj3uxJv6G99dikNjRZ6xkcwOM2/9edJQrwtgK9gT+tTvV5H74RIDhfXkFdW6/c/dK7EHv2BGp3aeaxSOqq1wp4GBaCmgm3OLRf9tcKeNETQUulKRX+C36INggyIwnAvsMqmPykMd4ZVB/z/+lMUhcbGRnRmE71iDSRH6qiv98wC9KZDJ+gVa+Dc4ck0NAR+OrLBYCAkJKTLZ3NXiLNixZ40HFA7Mh0urlHb7w7wgGB+gj2tY0GmiHeEbQJ7Fys90uShfexpnQ3S0U/wbxRF4ecDxQCcOsj/jW1PFzgrihKQzoon68Nt+vPT689oNJKfn09tbS1RJjMPn55KqEFHdna2R75/fA8To05PJSna4LHv9DZRUVH07NmTsDDnmzGIs2KlqebCvjQcMRZbotipv96iv3ZpSmPqfD8xttvH3siUOHuCP7Mnv4qiqgYiQw1MyEr0tjh+x6Giao5X1BNm0DO1XwBMDfdwFlNuSS05JbWE6HV+OXXdYrGQnZ2NwWAgIyODRkWPvryOsBAD/ZKj3f79xkYLxuJqdED/lBi/HUZqL4qiYDQaKSoqIjs7m0GDBqHvrLC0E8RZsWK/sSODIdvDngns0NzYFv01x279yayVdrG/ZkUie4L/8rM1BWdq/x6Eh/h/y2JPTxBftV+NSk3qlxhQLZ8VD2nwJ+v1N75Pol+2fDYajVgsFjIzM4mKiqK63oQuxIwh1EBEhPvrv2prjOhCwogKCyEmOjjKCCIjIwkNDeXIkSMYjUan9RzYbp0D2G3saMa2GDstsNgbGeghs0Law55uVtAUmTpaVofFIvrTsL9mRdXf8fI66egn+B1N9QL+mYLjbWz689MUptZ4ujx8VYDUSzm7ut9dqurVkQMxEcEVJ3CFvsVZsWLPBHtoMrZzJTLQAsVO/fVOjEKnU2eFlNYY3S+Yn2DPnBCAjIRIDHodDY0WTlRJRzoNe1pnA6TFRhAeoqfRonC8XPQn+A+1xkY2ZJcB4qw4Q73JzLrDastY0Z/jmMwW1h4S/TmLoihUW+fLxYYHl7PiCsRZsWLPBHuAvj3UvMaiqgZqjTLYUMPeyFREqIGe1nabOSU17hbLb7C3QUGoQW+rm8opFodZw97W2Xq9jr5JanRUrj/Bn1h/uBSj2UKvhEj6eyC/3hNoz1tPBNk35pRRb7KQGhvO0PRY93+hB/GE/jYfKaO6oZEe0WGMzIh3/xcGGHVGM2aLgkGvIyqAUhA9hTgrVuyZYA8QHxVKYpSaqynGYhP2puEAZFkftKK/Juxt/QzQN8mqPzG2bYj+hEDnp2ZdrLrbBjQY0VLATh4UOPrz5Hlo+jtpYDL6rgwlf8MDzl6VNaoSE979Nr7BiDgrVuyNDIAYO+1hbxoOiP7aw15nGbB1LRH9NWFvZAqa6U+cZcGP0IrrT/XzegFvESj1Ft5Ca5ktKWDOUV1vdVZ8uF7l2WefJT4+ntrats/G+vp6EhISeO6557wgmTgrNpwydsRYtOGYsa2l4YixqGFv62egKY2pWK4/Da3ZgEP6k/tX8BNOVNZzqKgGnQ6m9Q8cY9tT3cBKqhvYW1AFwIyBgaM/DXengVXUmdh5rAJQIyuCY5gtCrVGM+C5ehWj0fGa4BtuuIG6ujo++eSTNu998skn1NbWMnv2bFeI5zDirFixd84KQJYWGRBjsQ0ORaZEfza0mgt7nGUtje6IOHs2tGe1PddfP4nsCX6GVhg+IiOO+Cj/axnrbX7NLgVgcFoMyTHhXpbGdXgqmWhDdikWRV2oTY93f4tfT6EoauOFOpOZWmOj27bi6nrqTI1YLAqNFoVaY6PD3VBPO+00FixYwIIFC0hISCApKYkHH3zQdpysrCwef/xx5s6dS3x8PDfffDMAa9as4ZRTTiEyMpLMzEzuvPNOamraf/alpKRw4YUX8tZbb7V576233uKiiy4iJcU7kTXfjUd5GEciA1laZEDSSGw4srLdPDKlKIrkb+JYzUVWM2PbYlECL3/YCextnQ3Q13r95ZXW0mi2BPxgLsH/0ZyVaf39bxBfZ3jqp39tgOrPU2j6mxpg+qszmbnq1XVe+e7dj84iKswxE/ztt99m3rx5rF+/no0bN3LLLbfQt29fm2Py9NNP89BDD/Hggw8CsGPHDmbNmsVjjz3Gm2++SVFRkc3hWbx4cbvfMW/ePC644AKys7Pp168fADk5Ofzwww98+eWX3Tjj7iFPaSuO1Fxoxna2rMzacER/fayD+arqpX2xhr2ts0GdFWLQ66g3WSisanCvYH6Cva2zAXrGRRAWosdkVsivkPbFgu+jtYwNNGPRhpvzmAJdf+5Oo2vSXw83f5PQGZmZmTz//PMMGTKE6667jjvuuIPnn3/e9v4ZZ5zB73//ewYOHMjAgQN5+umnufbaa7n77rsZNGgQ06dP58UXX+Sdd96hvr79Z9+sWbPIyMhgyZIlttcWL15MRkYGM2fOdPcpdohEVjTsnCAOTWlMRVUNVDc0EiM9s+1uHQtq++KM+AiOV9STU1JLUgCF5Z3FkQYPWvvinJJasotrAios7yyO6E+v19G3RxQHCqvJLq6xTbUXBF8kv6KOnJJa9DqY1E+MRUcprm7gQGE1AFMCzFnxRGSqvNbInoJKIPAiU5FhBpbdOpXwEAOD0mLc8h1mi8Le/CoUFAanxRIWosYIIkMdb188derUFpko06ZN49lnn8VsVuthJk6c2GL/TZs2cfDgQd5//33ba4qiYLFYyM7OZtiwYW2+w2AwMGfOHJYsWcJf//pXdDodb7/9NnPnzsVg8F7LZbGyrTiShhMfGUqP6DBKa4wcKalhhPQcd0h/oDp8xyvqySmuYULfRDdK5h/YO8Feo29SNDkltRwpqWHagMB6gDiDI62zQdXfgcJqjpTUANLdRvBdtBSwUb3iiYsIrHoV7XZ1Z2RA09/Q9Fh6RIe58Zu8h6P1D46wPrsURYEBKdGkxgXWwphOpyMi1EB4iMHhlCx7qaw3ER6qJyxET0KUe6+/6OiW85csFgu33nord955Z5t9+/Tp0+FxbrzxRp566im+//57AHJzc/ntb3/rWmEdRJwVKxYHuoEBZCVFUVpjJKe4VpwVmhnbdiowKzmatYdLrMai4EiDB1BTEX/aXySpiFYcSUOEpo502VJ3Jvg4thQcWZRwCk1/gbioo/NAiX0g688T1GjzVVzgDK1bt67N34MGDeow4jF+/Hh27drFwIEDHfqeAQMGcOqpp7J48WIUReG0005jwIABTsvtCqRmxYptgr2dN3+WtC9ugcXBlZ0sa/vYbOloBThWswJN7XePiLENONYgA5pSOcVZFnydQC1u9hSiv+6xTvTXLaqtzkq0C+ar5OXlce+997Jv3z4++OADXnrpJe66664O9//Tn/7E2rVruf3229m6dSsHDhzg888/54477rDtc//993PDDTe0+ey8efP49NNP+eyzz5g3b163Ze8u4qxYcdTY0dqfZkv7XcCxmgFo3n5X9AfO60+c5ZbYqz9pkiH4A0fLaskrrcOg1zEpK/DqVbTFQXdlMRVW1nPYOp9maj8xth2ltMZom08jzorjmC0W6q3zVVwRWdHmoEyePJnbb7+dO+64g1tuuaXD/UePHs1PP/3EgQMHOPnkkxk3bhwPPfQQPXv2tO2Tn59Pbm5um89efvnlhIeHEx4ezmWXXdZt2buLpIFZsRmLdnorfZNlVkhzHGkdC82MxSJpXwzN0+js27+5syztix1rnQ1Nzl5eaS0ms4VQaV8s+CDrDqvzQUb3jpdGLk6gRVWG9wzM+TTufmyut+ov0ObTaLj7qVnTYEYBwkMMhIZ0/xkTGhrKwoULeeWVV9q8l5OT0+5nJk2axPLlyzs8ZvOuX82JjIykvLzcCSndgzyhrdiMHTv3758skZXmKA7W/PRNikKvg6qGRmm/i+M1K70TIwkz6GlotHCsvM6NkvkHjtas9IyLIDLUgMmskFcqqXSCbxLoLXfdjaQwdQ9JoesethSwcO910QoUxFmxYqtZsdPa6Z+iOislNUbKZFaIQxPEQV1p0OoGDlrbSgYzjjZ4CDHobdGpg0WiP0fTOPV6HQNS5frzZbKystDpdC22P//5zy32yc3N5cILLyQ6Oprk5GTuvPNOjMbA+T3+NSfAjUVbNzD35IGtt06uD1j9WXFXGt2vQaI/d1FjtBbXS1S024gGrThq7ESFhdArIZJj5XUcLKpmUnTg5RM7gqNpOKC2QswuruFQUTUzBia7SzS/wFFnD2BAajT7TlRxqLCa04ekukcwP8Ep/aXEsPNYJQeLqvHeqCuhMx599FHbdGaAmJimWQhms5nzzz+flJQUVq9eTUlJCXPmzEFRFF566SVviOtSCivrySutQ6+D8X0SvC2O31FaY+RwkZr5MDFA2+O7M42pos7EvhNqvcrErMDUnzsxWxTqjRYAl7RF/vHHH7t9DH9GnBUrjhY4AwxIjVGdlcLqgCx+dARHa1ZA1d/KPYWyso3jzjLAwBTVcDskkRWHJthr2PRXKKmcvkpsbCzp6entvrd8+XJ2795NXl4eGRkZADz77LPMnTuXJ554gri4uHY/19DQQENDU+ppZWWl6wV3ARuPlAEwJD2O2ACbr6LhTmN7k1V/A1KiSQzQ+SruZEtuGYoCfXpEkRobWPNVPEGdsREFhVCD3jYIUnAe0aAVR2suoMnYEWPb8TQmEP01R3GwZgVUZw9Ef+D4UEiAgZr+xNnzWf7+97+TlJTE2LFjeeKJJ1qkeK1du5aRI0faHBWAWbNm0dDQwKZNmzo85lNPPUV8fLxty8zMdOs5OMvGHNXYDtSoQHPckca08YiawhQMC4nuSKPTnL1guP7cQY21C1h0mNSruAKJrFhxtMAZYFCaGIutcTQyBRIZAOec5QHi7NlwNjIKcKiwWjrS+SB33XUX48ePJzExkV9//ZX777+f7Oxs3njjDQAKCgpIS0tr8ZnExETCwsIoKCjo8Lj3338/9957r+3vyspKn3RYNlmNbUnBcY5NVmdvQiAb2278ydKc5QlBcf253tmrtTorUVKv4hKciqy8/PLL9OvXj4iICCZMmMDPP//c4b75+flce+21DBkyBL1ez913393ufp988gnDhw8nPDyc4cOH89lnnzkjmtM4lYYjK9s2urOyfaKygcp6k1vk8hcszhjbVmelrNZEaZA3eXAmDbFvUhQGvY5q6UjnMR5++OE2RfOtt40bNwJwzz33cOqppzJ69GhuuukmFi1axJtvvklJSYnteO393nTleIaHhxMXF9di8zVqjY3sPK6mp00M4MiA9v/J1ZGVepOZ7UcrgMDWn4ar9WcyW9iaVw4ER2TK1SiKQq3WCUwiKy7BYWdl6dKl3H333TzwwANs2bKFk08+mXPPPbfdoTKg5genpKTwwAMPMGbMmHb3Wbt2LVdffTWzZ89m27ZtzJ49m6uuuor169c7Kp7TOGMsamlMx8rrqLV2fQhWnHH24iJCSY1Ve7cfCnKHzxn9RYYZ6JUQCYjD7ExkKjzEQJ8eUYDoz1MsWLCAPXv2dLqNHDmy3c9OnToVgIMHDwKQnp7eJoJSVlaGyWRqE3HxN7bmlWO2KPSMj7Dd44L97DxWgdFsISk6jKykKG+L4zZ0bgqt7MmvpM5kJi4ixGbnCPZTb7JgVhT0Oh0RoeKsuAKHnZXnnnuOefPmcdNNNzFs2DAWLlxIZmZmu0NqQG0/+cILL3DDDTcQHx/f7j4LFy7k7LPP5v7772fo0KHcf//9nHnmmSxcuNBR8bqB4wW6idFhJFkL97SuI8GKLY3OwR/PgbZUsODWn9JN/QW7sd3k7DmmP0ml8yzJyckMHTq00y0iov1i3i1btgDYpi9PmzaNnTt3kp+fb9tn+fLlhIeHM2HCBPefjBsJihQmN6I1J5jQN1HSO51gY7PrLxgGDrs6CUxbvI4KM8j15yIcclaMRiObNm1i5syWjT5nzpzJmjVrnBZi7dq1bY45a9asTo/Z0NBAZWVli607WNQOcw7fmFLkrKI4kYYDYixqODrBXmOg1P0AzjUoANGfr7J27Vqef/55tm7dSnZ2NsuWLePWW2/loosuok+fPoD63Bk+fDizZ89my5YtfPfdd/z+97/n5ptv9snULkfYGCTFzdrd6mpjUTO2gyWFydX6sxXXB4n+XI2tuF7qVVyGQ85KcXExZrO5TYg9LS2t04LGrmivULKrY7q6o4sz3axAVrY1bMa2g9a26E/FmQYPIM6ehqMT7DUGpMhgSF8kPDycpUuXctpppzF8+HD+7//+j5tvvpkPPvjAto/BYODLL78kIiKCGTNmcNVVV3HJJZfwzDPPeFHy7mOxKGzOFWPRWRSlSX+BXhzujkV7RVHYkKM2d5DInnNo9SpRflKv8uOPP6LT6SgvL+9wn08++QSDwdBhycfQoUO588473SShk93AWhtUruik4+gxXd3RxTbB3tE0HDEWgSZnz1GanJUqV4rjdzjrLEtHOhVnan4ABqXFAnAgyPXna4wfP55169Z1uV+fPn344osvPCCR59hfWEVVfSNRYQaGpsd6Wxy/43BxDaU1RsJD9IzMaD/1XOiYo2V1FFY1EKLXMaZ3grfF8TtMjRaMZgs6XDMM0le46KKLSEpK4u233+ahhx5q8d4vv/zCvn37WLp0qdu+36HISnJyMgaDoU3Eo7CwsFsFje0VSnZ1TFd3dHHe2FGNxf1Bbmw70zoWYLDVWDxSWhvUTQqcmcAOMDhV1d+x8rqg76gGjutvkNVZLqpqoKRaOoIJ3meDNYVpXJ8EQgyBPQpNu10VF7az2miNCozpnRA0w/hcqj9ry+wRveKJ9JPIgNMoCjpTLTpTLRhrXLLVVFegM9USSQOGxk6O6+D/s9NOO40FCxawYMECEhISSEpK4sEHH7T9vy8rK+OGG24gMTGRqKgozj33XA4cOGD7/JEjR7jwwgtJTEwkOjqaESNG8NVXX5GTk8Ppp58OqK3fdTodc+fObfP9oaGhzJ49myVLlrS53t566y0mTJjQYRMtV+CQ2xcWFsaECRNYsWIFl156qe31FStWcPHFFzstxLRp01ixYgX33HOP7bXly5czffp0p4/pKM4a20OsK185xTXUm8xB2/nBmdaxACmx4STHhFFcbeTAiWrGZCa4XDZ/wNmalfioUHrGR5BfUc++gqqgydFujTOts0HNKe7TI4rc0lr2FVQxfWC4O8QTBLvZbCsOD857ubto9RaBngIG7hmzElTDIE21jHp7mEsPmWDduuQvxyEs2qFjv/3228ybN4/169ezceNGbrnlFvr27cvNN9/M3LlzOXDgAJ9//jlxcXH86U9/4rzzzmP37t2EhoZy++23YzQaWbVqFdHR0ezevZuYmBgyMzP55JNPuPzyy9m3bx9xcXFERrbfgXDevHk899xz/PTTT5x22mkA1NTUsGzZMv7xj384dC6O4nCM6t5772X27NlMnDiRadOm8dprr5Gbm8v8+fMBNT3r2LFjvPPOO7bPbN26FYDq6mqKiorYunUrYWFhDB8+HFCHf51yyin8/e9/5+KLL+a///0vK1euZPXq1S44RftwNg0nJSacpOgwSmpUY3tU7+AMOytOdmMC1eErPljC3oLKoHVWtAYPzqRTDk2PJb+inr3irDj18B6aHktuaS17C6qYPjDZtYIJgoNss863GN8nwatyeAJ3tN7dlqfOVxnfJwiMbSuuLLAPRv35C5mZmTz//PPodDqGDBnCjh07eP755znttNP4/PPP+eWXX2yL/O+//z6ZmZn85z//4corryQ3N5fLL7+cUaNGAdC/f3/bcXv0UO2G1NRUEhISOvz+4cOHM2XKFBYvXmxzVpYtW4bZbOY3v/mNe07aisPOytVXX01JSQmPPvoo+fn5jBw5kq+++oq+ffsC6hDI1gU448aNs/33pk2b+Pe//03fvn3JyckBYPr06Xz44Yc8+OCDPPTQQwwYMIClS5cyZcqUbpyaYzjbTUin0zEkPZY1h0rYU1AZtM6Ks84ewJC0OH45WMLeguBNpeuW/tLj+GFfEfsKutcRz59xZk6SxtD0WJbvPsG+IL7+BN+gotbE4WK1jbvUCzhOdUOjLSV7TJA+i7tDvcnMnnz1OTImMwj0FxrFjjl7CAvRMySt+/VhFkVhd34ViqIwODWG8M4ybUIdn/8zderUFjbqtGnTePbZZ9m9ezchISEtbOakpCSGDBnCnj17ALjzzjv53e9+x/LlyznrrLO4/PLLGT16tMMyzJs3j7vvvpt//vOfxMbG8tZbb3HZZZd16uS4AqcSOm+77TZycnJoaGhg06ZNnHLKKbb3lixZwo8//thif0VR2myao6JxxRVXsHfvXoxGI3v27OGyyy5zRjSncbZmBWBoulovE8zGjq1BgTPGYk/1R0L057yxDUGuPyfTEEF19gD2nghe/Qm+wfZj5QD0TYoi0TrDS7CfnccqUBTIiI8gNa79eT2BhKtneOw6XkmjRSE5JiwohpHqdDqU0CiUkCg1JaubW4MuAktIJPrwaMKiYjvf3wPzV5o3qrrppps4fPgws2fPZseOHUycOJGXXnrJ4WNec8016HQ6li5dysGDB1m9ejXz5s1ztehtCI7qMztwtmYFmozFvbKy7aSzp+mvyqWFgv5Et5zlnqI/ZybYa2j6219QhcUSnPoTfAMtBSxYoipNBfauOZ5Nf8GWTuxq/fVOkGGGTlBrna8SGeqeYZCtOySuW7eOQYMGMXz4cBobG1m/fr3tvZKSEvbv38+wYU01OZmZmcyfP59PP/2U++67j9dffx1Q69EBzGZzlzLExsZy5ZVXsnjxYt566y369+9vSwlzJ+KsWFHovrGzNz94V2a7UzMwKDUWnQ5Ka4wUBWlHpu44y/2TYwjR66iqb+R4Rb2LJfMPnC2wB8hKiiY8RE+dyUxuaa2rRRMEu9lqrRcIOmPbRWw7Wg4Ej/5cbQ8Hm/5cXTKlOSvualmcl5fHvffey759+/jggw946aWXuOuuuxg0aBAXX3wxN998M6tXr2bbtm1cf/319OrVy9b86u677+bbb78lOzubzZs38/3339scmb59+6LT6fjiiy8oKiqiulpt5f/Pf/6TM888s40c8+bNY82aNbzyyivceOONHnFsxVmx0p0C50Gpseh1UFJjpKgqWI1t54ZCAkSGGchKUrtiBGsqk7NzagDCQvS24ZDBWrfSnZoVg15na0EezHVTgndRFIWt1pXtscFiLLqYrbnlQPBEplzNNrn+ukWdzVlxT1fYG264gbq6OiZPnsztt9/OHXfcwS233ALA4sWLmTBhAhdccAHTpk1DURS++uorQkNDATVqcvvttzNs2DDOOecchgwZwssvvwxAr169eOSRR/jzn/9MWloaCxYsANRB8IcOHWojx0knncSQIUOorKxkzpw5bjnX1gTOxJpu0p00HM3YPlxcw96CSlJiU1wsne/jbIMCjaHpsWQX17CvoIqTBwWv/pwxtkGN7u07UcWe/CrOGOr8zCN/pTs1K6DWne08VsnegkrOGZnuOsEEwU6OV9RTXK0O4xuR0b25Yf6CdrsqLshjKqys53hFPTodQdfoxhVZYOW1RnJK1Mjy6CDTnyswWyzUN1rTwNzkrISGhrJw4UJeeeWVNu8lJia26MLbmq7qUx566KE2wx4ffvhhHn744Xb337t3b9cCuxCJrFjpzsosSCpYd5w9aJpXs0f05xRDgrzIvjs1KyBNCgTvo61qD+0ZG7TzurrDtqNqCt2g1BhiwoNjHdaVyTea/volR5MQJc0dHEWLqoQZ9IQG+DBXbyAateF8zQU0dQTbE+RpOM72zdf0tzs/OPXX3cjUsCDXX5OzJ9ef4J8EW3E94FJrOyj150Ka9CdRFWeoNbk3qhLsBMfygx10N7IyrKfV2DkenMZOd9NwRvZS9XfgRBX1JnPQrSxqaRDO6m+EVX+Hi6qpNTa6rcDPV+lO62zAlnZzpKSWynoTcRGhLpJMEOxja7B2ssI13cCCrji8Ga7oAhm0ndRchLvrVVqPBAk2JLJipTtD+aCZsV1YTb2p6/ZvgUZ3JtgD9EqIJCEqlEaLwv4gnHfRnQYPAKmxEaTGhmNRgtNhborsOUdidBi9E9W5AjuPVbhGKEGwE7NFYYf1upPiZsexWJSgLA53VRMmRVGC29lzwTFsbYuDbKHQU4izYqW7aTjpcREkx4RhtihBmUrSXWdPp9Mxqpcaft55LHj156yzBzTTX/AZ292t+QEYmaHqb1cQXn+CdzlYWE2t0Ux0mMHW2S8Y0NKGu2ss5pTUUFnfqE4iT+/+JHJ/o7v6O1pWR3G1kRC9juE9A7+5g6vnkZnMFkxmCzrUGStCS1yhb3FWrHTX2NHpdIwMamNR/bc7/bZHWI3FHUGtP+ePMaKXpr/gM7a70zpbQ+sgFIzXn+BdtKjAqN7xGLrjcQcpWlRgZEacFDc7gaa/YT3jAjoFW2vjW1vr2nlaWlQlPNQg9287aPrW9O8MEq+y0t3WsaCubP+4r4gdR4PP2NH85u7cp1pkYNfx4NMf3axZgeDWX3cjo9BUt7IzCPUneJftx8oBKQ53lu3WZ+7ooNOfawzjHTb9BXZxvcFgICEhgcLCQgB0oWEojUYsFj319c4PVK6sbkBpNBIWEtqt4wQaiqJQW1tLYWEhCQkJGAzOO8LirFjpbhoTYIusBOPKbHdrVqCp7mdvfhXGRgthIcGzQuaKyFTruqlAXiFrje3+7cYxtPs3u7iG6obGoGl/KngfLfVVuwaDBe3nrrtpIlrq5qgg059Gd7NstAWaYNBfero6R6uwsBBjo4XCKnW2kb4mwuljFlc3UG+yYIwKpa5MnhutSUhIsOndWUSrVlyxMqvd6EFtLHbDWuzTI4rYiBCq6hs5UFhlSwsLBlxhbKfHRZAUHUZJjZE9+ZWM65PoGuH8gO528wNIjgmnZ3wE+RX17D5eyeR+PVwknSB0TKPZwl5ry/tgGQbpSizN6kS1roiC/SiKwq7j2vUX+M9cnU5Hz549SU1NZVdeKQ9/vomUmHA+vHWa08f846I1lNYYefGacfQLAofPEUJDQ7sVUdEQZ8WKKwp0e8Y3GYt7C6qCqiuJK5w9nU7HyIx41h4uYdexyqD44dRwhbGt1U39tL+InccqgspZUVxw/4K6sp1fUc+OYxXirAge4XBxDfUmC9FhBrKSor0tjt9xpLSW6oZGwkP0DAyi5gTgmm5gx8rrKK81EaLXMTg9ePRnMBjQh4ZxrMqMWWchIsK5yEphVT07CurQ6WBYZhIR0g3MLQRPnk0XuKJmpXmRfbClgrnC2YPgLXJuKhDv3nG0VLDg05/6b3fuX2jqCBaMTTIE76DVmA3PiOtWgwh/RDvb7mQxafobmh5LSJAW1yvd0KAWVRmUFkt4SPBkg7gKTX8DUmKCbr6ZJwnOO7sdujuUT0NLBdth7a4RLLgiMgBN+tsWZPpzlbGtFZhuywsuY9tiC+117zijM63Xn7U7kyC4G61eJZgiya5EMxaHB6H+XOHaNqWASQqdM+yyLmyJ/tyLOCtWLLaFie7d/tpApS255d06jr+huKDmAmBcnwRAHWwYTMM1LS7q+z7Oev3tL6yiqt7kkmP6A65ylsdanb3DxTWU1Ri7KZUgdE3zyEqw0Z20YQ0tCjpS6lWcQjO2Rwbh9afhisiUOCvuRZwVK65KY9KM7QOF1VTUBY+x6IqaFVAn2afEhtPYbKJzMOCqyEpqXAS9EiJRlKZ2nsGAK1pngzrJvn+yWjewNciie4LnaV7cPDIIIwM2nLQVFUVhdxAVh3dEd9a6bMa2FIY7hdy/nkGcFSuuWplNjgmnT48oILhSSVzl7Ol0OsZbHb4tuWXdlMp/cMUEe41xQag/xQXd6DTG2vRX3v2DCUIn5JXWUVXfSJhBz6C04CludhUnKhsoqTFi0OsYGoST67u7OFhc3UBBZb1aHB4Ek+tb09Q627nPV9SZyC1VBx4GY2TUk4izouFCY0cztjcHkbHoKmcPsHWxCiZjsSmy0v1jBaP+XOvsafoLnvtX8A5aCtiQ9NignLxuMxad/LyWAjYwJSaoRgW0xlljW4sK9EuKlrlSTqBF9XonRpIQFeZlaQKb4Pt17ABXGtvj+wafsejKlW2t7mJzblm3h4X5C66YU6Nhi0zllQeP/izqv67Igdf0tzWvHIslOPQneAdtGJ/kuztHUwqT6M8ZNGdZUsCcY5fcvx5DnBUrrjQWx2U2rcwGi7HTVDPQfQWO6h2PQa/jRGUD+RX13T6eP6DpzxXG9vCMOMIMekprjBwpqe328fwBV3XzAxiSFktkqIGq+kYOFVV3/4CC0AFSnNs9mpy94DS2u/tzt+tYcF9/OqsGnbXSgmmYprcRZ8WKqwqcAYb2jCUiVE9lfSOHi4PD2HGlsxcVFsKwnmr+cbBEp1yZxhQeYrCtNG7JC45UJm1NQOeCZp4hBj2jrfN+guX6E7yDrW1xkK5s2+asOBkB3i3OXreQyED3kE50nkOcFSuuNLZDDXrbvIvNQWLsuDINB5qiU5uOBJux7Ro0/W0+Uu6iI/o2rppgr6HVrQRT3ZngWQor6ymubkCvg2HpYuw4SlmNkWPldYAUNztDVb2JHGvkXSIDjlNnNNsi76I/9yPOihVXRlYAxluNnU05wWHsuKp1rMbELFV/G3JKXXNAX8dFE+w1NP39mh0c+nNV62yNiX2DS3+C59FSmPqnxBAZFrzF4c6ipeD0TYoiLiLUy9J4h+783GlRqYz4CHpEB2dxeHe6ge0pqMSiQHJMGKmx4a4VTGiDOCtWXBlZAZjSrwcA67NLXHNAH0dxYRoTwGSr/nYdr6AyCIYbWlxsbGv623eiKiiGG7r6/p3Urwc6nTocsrAqOOqmBM+yJ78KgOFB2DJWozvdwPbkWyfXB7H+NJxJo7PpT6JSTqHpb1jPOJc9t4WOEWfFiq3A2UWJOBOzEtHrIKekloIgKBJ3tbHYMz6SPj2isCjBkQrmypoVUOf9DExV5zb8GgTRKVd28wOIjwy1peZIdEVwB82NHcFx9hSI/rpjr+wtUJ3loNZfNx4Xe2WxwaOIs2LFVUMNNWIjQm15jMEQXXFlgbOGLTp1OPCNRVfXrECT/oLB2HZ1zQo0RaeCQX+C59lnNRaDcZhhE87fsJr+hgS1/pxnr+ivGY5HpuT68yzirFix1ay40NrRjMV1QWBsu8NYnNI/CQgOZ8/VaXTQZGwHhf6s/7pSf1P7B4+zLHiWhkYzh4trADF2wPGagUazhQOFanFzcDt7Ko6a2haLwv4T4iw7i6Io7LVG9uT+9QzirFgRY7t7uNPZ23G0glpjo8uO64u4coK9xlTr9bf7eGXA1/1YFNeHpiZlNdX9lAZB3Y/gOQ4V1mC2KMRGhNAzPsLb4vgdOSW1GBstRIUZyEyM8rY4XsPZtZmjZXXUGs2EhejJSop2rVB+hLOZIAWV9VTWN2LQ62zp1oJ7EWfFStPsRheubGdZi3SLAr9I11az4sJj9k6MJCM+gkaLEvAteJtqflynwbS4CLKSrHU/Ad6VTmud7crISlJMOIOsD6Kg6UoneIR9J9RV2aHpsUFdnNtUYO9YbEBLwRmUFuvSBTJ/xdHIlBYVGJgSQ4hBzEDH9adef/2SowkPkU5+nkCuUiuurlkBiI8KZai1SDfQU0lc3c1KO9ZkWypdYEenXN2gQGNKPzW6sjZI9Odqu2WKNRVs7aHA1p/gWfba6lWkONcZNGN7mKTgOIXUS3UP0Z/nEWfFiqvnrGhMs6birDlU7NLj+hraypirjcUZA5MB+PlgoOtPxdXX3/SB6vX384HA1p+GKxs8AMwYoF5/qwP8+hM8ixTndg8pDu8ee0+I/sD51tnirHgecVasKG5a2T55kGrsrNpf7FQvdH/BHWk4ACcPSgFg+9FyymsDt27AHTUrACdZnb09+ZUUVTW49uA+hLsiK9MHJKPXwcHCavIr6lx7cCFo0dqeBruxo92ujj4axdlriaNpdHvzpTi8O+yx6U8io55CnBUrrp7ToDGlfw/CDHqOldeRbe3+Eoi4y9lLj49gcFoMigK/HAzcVBx3pYElxYQzwjr065cAjg64Iw0R1FTO0b0TgOCJTgnupaLWREGlWsM4WIxFh6lpaCS3tBaQNDpnqDeZySkR/TmLyWzhUJF0ovM04qxYcZexGBUWwsSsRABW7S9y7cF9CHelMQGcNFCNrqw+GMD6c5OxDU3RqVUHAll/7omsAJxijY6KsyK4Aq3eoldCJHERoV6Wxrs483untdxNiQ2nR3SYq0XyK5x5XBwsrMZsUUiICiUtLtz1QvkRTZE9+yNT2cU1mMwK0WEGeiVEukcwoQ3irFhxp7F9ymDVWAxkY8ddzh7AyYMDP5XO1RPsm9Pc2A5U/bnV2bPev6sPFGGxBKb+BM+xT+oF2uDIz9JeqRdogyP6s6XQpQV3Jzpn0a6/wenSic6TiLNixV1pTNBUt7L2cAnGRovrv8AHcFcaHajzVgI9lc5dNSsAE7ISiQjVU1TVYDOUAg131awAjM1MICY8hLJaE7uOV7r+C4SgQozt7iHFzU0401BknwyDtOGMubKvQGs7Lil0nkScFSvuNLaHpceRHBNOrdHMxiOB2cLYnc5eMKTSNc2pcb0Cw0MMthbGP+0LVP2p/7pjpTDUoLcN2Pxpf6HLjy8EF1Ic3oQzd2vT5HAxFp2hqZOa6E/DkXi5OMveQZwVK+5cmdXrdbZUnB8D1Fh0V+tnjdOHpALw3d7ANBab0pjcc/zTh6ipTN/tCVD94T5nGeCMoer1tzJA9Sd4BkVRmhk7Yiw6Skv9ibGo4ZixLZ3AuoO0zfYO4qxYcWfOO8BZw9MAWLH7REDWDbjT2YMm/a07XEJlvck9X+JF3FmzAnDmMFV/G4+UUlYTeC2g3dU6W+PMYaqzsu1oOYVV9W75DqElWVlZ6HS6Ftuf//znFvu0fl+n07Fo0SIvSdw1R8vqqG5oJNSgo39KtLfF8TsKqxooqzWh18HA1Bhvi+N1HP25K6sxcqJSbWEvxjY4GturqjdxtExtYS/OsmcRZ8VKUxqOezhlcAphBj3ZxTUcKgq8uoumumP3aLBfcjQDUqIxmZWATAWzuDmyktkjiqHpsVgU+GFf4EUH3NkNDCAtLoLRveNRFPghQKN7vsijjz5Kfn6+bXvwwQfb7LN48eIW+8yZM8cLktqH1slqQEoMoQZ5/NqG8tm5gKdFVbKSo4kINbhLLL/D3vVPrV6ld2IkMeEhbpTIv7BXf/tPqC2L0+LCSYgK7k50nkZ+La24O40pJjyEaQPUvPcVu0+45Tu8ibuNRYCzrNGBlQGtP/cp8GxrdGrlnsDTnztrzjS062/FbnFWPEVsbCzp6em2LSam7Wp6QkJCi30iI323neiBQtXYGZQmq7LOoOlvcKrozxls+pPrzykOFlo7gYn+PI5TzsrLL79Mv379iIiIYMKECfz888+d7v/TTz8xYcIEIiIi6N+/f5sw/ZIlS9oN59fXey7dwhPG4lkBbCy629mDJv39sK+IRnNgdVVrap3tvu/QUsFW7S+modHsvi/yAo5OcHYGzVlZfbCIelNg6c9X+fvf/05SUhJjx47liSeewGhsm8K4YMECkpOTmTRpEosWLcJi6fy3oaGhgcrKyhabpzhoNRYHpkgKkzPY9CcpYIDjkfhDor8WOBrZ066/AXL/ehyHnZWlS5dy991388ADD7BlyxZOPvlkzj33XHJzc9vdPzs7m/POO4+TTz6ZLVu28Je//IU777yTTz75pMV+cXFxLUL5+fn5REREOHdWTuDuNByAs6x575tzyyiubnDfF3kBd9dcAIzvk0hiVCgVdSZ+zQmsrmpNc2rcp7/RveJJiQ2nuqGRdYcDTX/qv+68/ob1jKVXQiT1JgurA3hmkq9w11138eGHH/LDDz+wYMECFi5cyG233dZin8cee4yPPvqIlStXcs0113Dffffx5JNPdnrcp556ivj4eNuWmZnpztNogRjbLdG6H9q71CDGdkc4ZmyLs+wccv96D4edleeee4558+Zx0003MWzYMBYuXEhmZiavvPJKu/svWrSIPn36sHDhQoYNG8ZNN93EjTfeyDPPPNNiP51O1yKUn56e7twZOYk7hxpq9IyPZFQvNe99+a7Aiq54wtkz6HW21e2vduS774u8gDZs0J360+t1tlSwr7YHlv5skVE3JrbqdM30F2DXn6d4+OGH242iN982btwIwD333MOpp57K6NGjuemmm1i0aBFvvvkmJSUltuM9+OCDTJs2jbFjx3Lffffx6KOP8vTTT3cqw/33309FRYVty8vLc+s5ayiKIsZ2NzlYJPrrDrbIgOjPKeT68x4OPdqNRiObNm1i5syZLV6fOXMma9asafcza9eubbP/rFmz2LhxIyZTU1en6upq+vbtS+/evbngggvYsmVLp7K4OpTviTQmgPNG9QTgi+3H3fo9nsbdrWM1LhiTAcDXOwoCKhWsKQ3MvQq8YLR6/X2zqyCgBpTauvm5rUWGyoVjVP0t331CUsGcYMGCBezZs6fTbeTIke1+durUqQAcPHiww+NPnTqVyspKTpzoeDEoPDycuLi4FpsnOFHZQFVDIwa9jqzkKI98p6/jyM9dSXUDpTVGdDpJw9Fw5Peust5EQaWaWi/GtoqmPXviUnVGs60T2CDRn8dxyFkpLi7GbDaTlpbW4vW0tDQKCgra/UxBQUG7+zc2NlJcrKZSDB06lCVLlvD555/zwQcfEBERwYwZMzhw4ECHsrg6lK8Z254yFtceLqGwMnBaoHoiDQdg+oAkEqNCKakxBlQqkzsn2DdnSr8kUmLDqagzsfpg4HRVc3frbI1xmYlkxEdQ3dAYsDOT3ElycjJDhw7tdOso/VdbwOrZs2eHx9+yZQsREREkJCS4Q/xuoa1q9+0RRXiIdLJqjj0lA5r+eiVEEhkm+muOPfrTonqpseHER4a6WaLA41BRNYoCiVGhJMWEe1ucoMOppInWefWKonSaa9/e/s1fnzp1Ktdffz1jxozh5JNPZtmyZQwePJiXXnqpw2O6OpTviTQmUFvIju+TgKIEViqJJxoUgDpN/JyRgRed8kTND6ipdOdr0b1tgXP9uXOCfXP0eh3njw6868/XWLt2Lc8//zxbt24lOzubZcuWceutt3LRRRfRp08fAP73v//x+uuvs3PnTg4dOsQbb7zBAw88wC233EJ4uO8ZE1onIUnBcQ5JwWmLIz93Um/RFkeeF4fk+vMqDjkrycnJGAyGNlGUwsLCNtETjfT09Hb3DwkJISkpqX2h9HomTZrUaWTF1aF8T9SsaFxoTWX6XwDVDXjK2YOmVJyvdwZOKpPF3kbvLkCL7gVSKpOnIisAF4xW79/v9hRSa2x0/xcGIeHh4SxdupTTTjuN4cOH83//93/cfPPNfPDBB7Z9QkNDefnll5k2bRqjR4/mhRde4NFHH+XZZ5/1ouQdY2tbLMaOUxw4IfrrDgfl+usYOx6/2vU3UNpmewWHnJWwsDAmTJjAihUrWry+YsUKpk+f3u5npk2b1mb/5cuXM3HiREJD2w9FKorC1q1bOw33uxpP1awAnD+qJzodbDpSxtGyWrd/nydQPOjsNU9l+uVgYHRlsl1/HrC2x/dJpKc1lemnQBmw6aHICsDo3vH06RFFncnMd3tk5oo7GD9+POvWraO8vJy6ujr27t3Lww8/TFRUU63HOeecw5YtW6iqqqKmpoYdO3Zw1113ERLim8PuZGW7Y+xpPS4r2x1jz1KXXH/dQ/TnXRxOA7v33nt54403eOutt9izZw/33HMPubm5zJ8/H1DTs2644Qbb/vPnz+fIkSPce++97Nmzh7feeos333yT3//+97Z9HnnkEb799lsOHz7M1q1bmTdvHlu3brUd090077HtAVub1LgIpvZTo0r/C5BUHE/VrEDLVKbPthxz+/d5Ak/VrIDqEGnRlf8EiP48GVnR6QJPf4L7EWO7e4ix2BZHfu60NDpJQ2zCGf3J9ecdHHZWrr76ahYuXMijjz7K2LFjWbVqFV999RV9+/YFID8/v8XMlX79+vHVV1/x448/MnbsWB577DFefPFFLr/8cts+5eXl3HLLLQwbNoyZM2dy7NgxVq1axeTJk11wil1jabYs4QljG+CScWoqyUeb8uweSOSreNrZA7hsfC9A7WpVUWvqYm/fx5aG6CENXja+N6AOKC0JgJk/nnSWoUl/P+wr5EQANcoQ3ENZjZHianWgpXSyaqJpKF/n+1XVm8ivsHaySpE0nNZ0ZUPUm8zklapZHGJst6UrC8xktpBTXAOI/ryFUwX2t912Gzk5OTQ0NLBp0yZOOeUU23tLlizhxx9/bLH/qaeeyubNm2loaCA7O7tNxOT555/nyJEjNDQ0UFhYyLfffsu0adOcEc0pmt/onjJ2zh+dQVSYgcNFNWw8UuaR73QXihecvVG94hmaHoux0cJ/tvr/6rYnJtg3Z1jPOEb3jsdkVgIiOuWJCfbNGZgaw4S+iVgU+HjTUY9+t+B/aKuyvRIiiQ73zTQ1X+ZQkWoopsSGEx8lnawc5XBRDRYF4iNDSZFOVg5zpKSGRotCdJiBjHjPDSsXmnDjCDX/oXlkRechjcSEh3ChtVD3w189M5TMXVi84OzpdDqunqS2q166wb/1B56ZYN+aqyY26c/fo3sWD9b8aFxt1d9HG/1ff4J7kWF83UMmr7ePvY+L5ilMnnzG+DpNkb3Of7+b37+iP+8gzgotjW1PXoZXT1aNnS93HKey3n9Tmbzh7AFcMrYXYQY9u/Mr2XmswnNf7GIURfFozYrGRWMziAjVc6Cwmi155Z77YjegeLBmReP80T2JDjOQU1LL+uzAmfkjuB4xtttHS3vtytWXepXOsVt/cv05hejP+4izgnfSmADGZSYwOC2GepOFz7f678wGbzl7idFhzByhtsz+cENuF3v7Ls2vP0+u2sRFhHKetVHBUr+P7qn/evL+jQ4PsbUhXxYA0T3BfdjaxqaJseMMor/ucUj01y1szoroz2uIs0LLfHdPGjtqKpM64Oz99bkBkUriSf0BXGPV33+2HKfKT6NTzf+vezIyAE36+3zbccprjZ79chdia53t4e/VUhG/2JEfEI0KBPcgkYH2sTuNyTpQU1a2W2OfAg/IQNJ2sTeyd0AiK15HnBVapTF52Nq5fHwvIkMN7MmvZO3hEs9+uYvwRs2KxoyBSQxMjaG6oZFlG/2z0LlFZMrD+puUlciwnnHUmcx84MfRFU9NsG/N2MwExvSOx9ho4f31/hvdE9xHTUMjx8rrADF2OqKzdbp6k5lc6WTVKZ3pr9FsIVvrZCXXn8NYLIq0HfcBxFmhtbHo2e9OiArjiglqG9S3Vmd79stdhDedPZ1Ox40z+gGwZE02Zov/RadaOnue/W5Vf1kAvLM2B5PZ4lkBXIDibf2dpF5/7647QkOj2bMCCD6PZigmRYeRGB3mZWn8jyMltVgUiI0IISVWOlk1x57nbV5ZHSazQkSonl4Jke4Xyo+wR3/HK+qoN1kINejo0yOq6w8IbkGcFbxXs6LxW6uxuHJPIYetHrw/oXjR2QO4dFwvEqJCySutY8XuE54XoJt4q2ZF48IxGSTHhJFfUc/XOws8/v3dxeJl/Z03qidpceEUVTXwRYAMeRVcx2Grs9I/JdrLkvge9tyu2cXqM7F/inRicgZNf/2SYzzaLdGf6CwypS029E2KJsQgJrO3EM3jnTkrzemfEsOZQ1MBWPxLjse/v7t4Y6hmcyLDDFw7Wa298MfoVEtn2fPfHxFq4Lop6lDXN/1Sf96LrACEGvTcMC0LUPUXCLVnguvIts4IyUoSZ6VjOr5nNGevX5KsandEZ785h63XX79k0Z8zaM6K3L/eRZwVWq3MekmGedZUko825VHsZ4W63nb2AG6YlkWIXsevOaVsOuJfbWS9WfOjcf3UvoQZ9GzLK2ftIf+qnfJ2ZAXg2sl9iAhV22j/fKDYKzIIvklOidVYlMiKU2iTw/slS71Aa+z5tbNdf8ly/TlDtkRGfQJxVvBuzYrGtAFJjO4dT73JwuurDntHCCfxBWcvPT7CVvuzcOUBL0nhHBYfWIlPiQ23dbZauHK/l6VxDG/W/GgkRodx7WQ1OrVw5X6Jrgg2bGlgYiy2wdaNyY40HHH2OqazX5tscfa6ROlEg036k+vPm4izQtMPpU7nvZVZnU7H3WcNAuCdtUf8Krri7ZoVjdtPH0iIXsfPB4rZdKTMe4I4SMvWxd5T4O9OG0CoQcf67FLW+WlnOm/mtM8/tT/hIXo255az+qBEVwT1tzG7qKlmQHCcbHH2ukV2kRjb3UGcFd9AnBW8N6OhNacPSWVM73jqTGZe86PoisUHnD2AzB5RXD5eja688J3/RFeUZg24vFn/mJEQaYuuvOBH0SlfiKwApMZFcO0UtXbqhZUHJLoiUFpjpLK+EYC+UnPRhq4eF5X1Joqr1flPWWIstqGr522d0czxinpAjO326Or6MzZayLO2zRb9eRdxVvDO9Ov2UKMrgwG1jWxhVb1X5bEXzSjztv5Aja4Y9DpW7S9iY45/1K74Qs2Kxm2nDSTUoGPt4RLWHPKP6IC3Gzw053enDiA8RM/GI2WsktqVoEerF+iVEElEqMHL0vguHbn1Wr1KSmw4MeEhnhPI3+hAgUdKVf3FR4aSGBXqQYH8i47WlfLK1LbZUWEGUqVttlcRZ4WmfEVvGzoApw1JYWxmAvUmC8+v8I/VbVtkxbtiANAnKYqrJqrRlce/3IPFD+au+ELNlEZGQiS/sXZWe+LLPX4xt8aX9JcaF8H1U9XalSe/3EOjH86tEVzHYUnB6RaSgtM9mqeASdtnxxH9+Q7irNAyjcnb6HQ6Hjx/GABLN+Sy+3illyXqGl9y9gDuOXswUWEGtuaV87/tx70tTpc0dwd84QfxrjMHERsRwq7jlXy6+ai3xemSFnNqfMBlvuOMgcRHhrLvRBXLNvq+/gT3YWt7Km1j26Wru9Xm7Enb2HbpUn/i7HWK9rztaEmu6f4V/XkbcVbAtvruA3YiABOzenDB6J5YFHjsi90+n/vuS84eQGpsBLedNgCAv3+9l3qTb08Vt9jS6LwsiJWkmHDuOGMgAE9/u4+ahkYvS9Q53p6z0pqEqDBbs4znVuyjqt7kZYkEb9HUNlaK6zujo2ectH22j67S6MRZcY7sEmnu4CuIs0LTyqyvRAYA/nzuUMJC9Kw9XMK3u3x7qrivOXsAN53cn4z4CI5X1LPop0PeFqdTmrrR+Y4C50zPok+PKAqrGvjXDwe9LU6n+FLNisb1U/vSPzma4mojL/pRswfBtWiRATF2nEPSwLqH6K97SCc130GcFXwvjQmgd2IUt5zcH4C/fr6LSj9YnfUl/UWEGrj/PDWd7uUfDnGwsMrLEnWMr0VWAMJDDLZ0xNdWHWZPvu+mI/pSzYpGqEHPQxcMB9Sp9juPVXhZIsHTWCyKDOTrAu1+bS8yoLZ9FmevM2z66yAyJc5K59geFx2EpkR/voM4K/heGpPGgjMG0i85mhOVDfzt673eFqdDLD7UDaw5F4zuyelDUjCaLfz5kx0+W2zvi5EVgJkj0jlnRDqNFoU/f7LdZ4vtW9Ss+JAOTx+ayoVjMrAo8MePt2OSYvugoqCynnqThRC9jt6Jkd4Wx+8orjZS1dCITqe2pRcco6LOREmNtH12llpjIwWV0vbZVxBnhSZj23fMHJWIUANPXTYKgH+vz/XZQX2+6uzpdDoev3QU0WEGNh4p4731R7wtUrv4YmRF45GLRxAbEcK2oxUs/iXb2+K0i+LD+vvrhcNJiApld34lb/zsm/oT3INWL9CnRxQhBnnUOoq0fe6azhqKaNdfqrR97pDObJacYnW+SmJUKAlRYR6SSOgI+QWlWc2KD1o7U/sn2QbN/f6jbVTU+V46mK86e6A+6P54zlAAnvpqr0+mg9kiKz6owbS4CP5iTaf7x7f72Fvge+lgvjInqT2SY8J58Hw1Hez5FfslHSyIkE5M9mDtxtRO0FbqBeynvZi3pDDZj9KOBkV/voU4K/jWUMP2uP/coWT2iORoWR1/+XSHz3UH82VnD2D21L6cNDCZOpOZOz7Y6nPdwZoaPHhXjo64ZlImZwxNxdho4c4PtlBn9DH94XsNHppz+fhezByehtGs6s/Xu6sJrkHannYPcfa6h+ive2QXVwNy//oK4qzgW0MN2yM2IpSXfjOeEL2OL3fk8+GGPG+L1AJfd/b0eh3PXTWGHtFh7Mmv9Ln6H1+t+dHQ6XQ8fcVoUmLD2X+imse+3O1tkVpg8dGaHw2dTsc/rhhNz/gIDhfX8NfPd3lbJMEDSNvYruk8DUf01xWiv+7RWTZDtjUNTJo7+AbirNAsjclHjR2AsZkJ/GHWEEDtDrY1r9y7AjXD4uORAVAniz975RgAlqzJ8alhh03Xn5cF6YSkmHCev2osOp1aP7V0Q663RbKhNU7w5esvISqM569W9ffxpqO8u84366cE16FFVsTY6Zr2sgUkDcd+2k2jE/3ZTfv6UyMrMiPJNxBnBd9Pw9G4+eT+nDVMTce5+Z2N5FfUeVskoHnrWN9W4OlDU7n9dHVY5J8/3cGW3DIvS6Ti65EBjZMGJXP3mYMBePA/O9mQU+pliVR8ueanOVP7J9kWHB7+fBdrDhV7WSLBXTSaLeSWqiuzMtDQcZq3fe4vxqLDKIrS5CzL9ecU4uz5FuKs4PtpOBp6vY6F14xjSFosRVUN3PzORp/If/cXZw/gvrOHcPbwNIyNFm55dxNHy2q9LRLg+5EBjTvPHMj5o3piMivc+u4mjlgNCm+i+JH+fnfqAC4Zm4HZonDb+5s5XFTtbZEEN3CsvI5Gi0J4iJ602Ahvi+OzdHTLFlTW09Cotn3OSBD9OUpJjZFqa9vn3onS9rkjOprzU1FnoqxWbWbUN0n05wtIPzuaz7nwrhz2EBMewhtzJnLxv35h57FKbnl3I2/OmeTV1o7+4uyB6vA9f/VYrnhlDXsLqrj+jfV8NH86KbHhXpPJl7tZtUan0/HMlWM4UlrDzmOVXP/mej6eP520OO8ZFP6mv79dPprsklq25ZWr19/vptMrQeZwBBJaVKVPjyifbTziS7Q2FjX99U6MtK/ts8UMZTlQfABqi6GuTH1Np4eQcIhKgphUiO0JiVlgCHX1KXiV1t2sNP31jIvo3DawmCF/GxzdAMX7ofQw1JZCg7Xroz4UQiMhJg1i0yCuN6QMgZShkDQg4PSokWfVX3JMONGdtX02m+DoRjj6q6q/ksPqtWfTX0gz/aVDXC9Vd6lDIXmw+p5gF+Ks4JsT7Dsjs0cUb82dxHWvr+OXgyXc8cEWXr5uPKFe6uXvT84eqA7fkt9O5opFa8gpqWX2m+tZess04qO888PrDzUrzYkMM/DW3ElcuWgtR0pquf6N9Sy9dRo9or3Ti97f9BcRauDNORO56tW1HC6qYfYb61k2fxrJMd5zmAXXcqSkyVkRHCfXqr9Oh0GW58Ku/8DBlZD3KzTamRatD4Ee/VVjMX009J4AGeMhqkf3BfcROtWfoqjOyZZ3YffnUF/u3JfoQyBtBGROgd6TIXMSJPT1nx/iTmi6f9txJhQFcn6GLe/B3i/BaEd0vKi9pj461XHJnGzdpkDSwIDQnzsQZwXfHWrYGWMzE3hjziTmLP6VFbtPcNv7m3npN+O8EmHxN2cPID0+gvfmTeHKV9eyt6CKa15fxzs3TvZKhMVXJ9h3RmqsVX+L1nKgsJprXlvLu/OmeCXC4o/6S44Jt+nvcHENV7+q6i9DIiwBgbYy20dSSDpF10EejhYZaDcFJ2c1rF6oOinNPxgSAcmDICYdIhPVVX+LWXViakqgpggq8sBUq66CF++HvV80fb7HAOgzFfqdom5xGS49V3dgU5+9+sv+Gb57VI0EaITHQZ9pkDYckgZBdLL6mk6nRg6MNVB9AqoKoPyIangX7VON9Pxt6vbra+qx4npB/9PUrd+pajTGh9GeGK0bPDTpr1m9iqLAwe/g+8cgf2vT61FJ0HcGpA5Xr78W+mtU9VRdCFX5qv4K90LRHjUCU7RH3Ta/rR4rOkXVm6bDhEx3nbrfIc4K/rcyqzFtQBKLrh/P/Pc2s2L3CeYu/pXXb5hIbIRnIwT+6OyB2j/9vXlTuO6N9ezJr+SqV9fy7rzJHs/x9eUJ9p2R2SOK926awnVvrGP/iWquXLSW9+ZN8biB5ssT7DsjIyFS1d/r6zhUVKPq76YpUtAZAEhkpXs0T6OzUXIIvv4THFzR9FrWyTD0AtWwSx4E+i4W6ywWqDquOipF++DYZji2UU1/Kj2kblvfV/dNGtjkuPQ71a8iL22uv9pS+PI+2PWp+ndIBIy4DMZeqzoqBgdNQUVRI1vHNkLeBtX5yd8OlcdU/Wk6TB1uNbxPh6wZEOYfv225pWotpi0yVV0IX9zT5NyGRsPoq2DsddBrAugdzGpRFNUB1FLI8n6F41tUh3rnx+oG6jWo6a/fKRAR55oT9EPEWcH354R0xhlD03j7t5O5+Z2NrDtcyjWvreP1GyZ6dIXWn2pWWjMkPZaP50/jujfWk11cw+WvrOH1GyYyuneCx2RoalDgf/obmBrDx/Onc/2b6zlSUsvli9bw6uwJjO+T6DEZ/KlmpTX9kqP56HfTmf3Geg4X13DFK2tYNHsCk7L8xzAS2tKusS3YzZHm+lMU2PgWLH9QjYroQ2DCXJh6m1o34Qh6PcT3VrcBZzS9XluqGo5HVkP2Kji+FUoOqtvGtwAd9BwDA05XjcfMqRDq/cL/jqLJTZG9aDUS9fGNqnGsM8DE38Ipf1BrKJz/Ykjsq24jL1dfM9VB7lo4/KO65W+Hwt3qtu5lMISpkasBZ8CAMyFtpONGvqvp4JHR4v499AN8Mg9qS9Qanim3wkn3qBEUp79Xp+p/2AXqBtBoVNPzNP0d29R0DW54Q/1/lzlZ1d3AM6Dn2K6d8wBCnBX821gENcLywc1TmbP4V3Ydr+TCl1bz8nXjmdI/ySPfr/hpZEojKzmaT343ndlvrudAYTVXLFrL3y4bxWXje3vk+22RPY98m+vJ7BHFR7dO44a3flVT6l5dx+OXjOSqSZ4JYftrZFSjV0Iky+ZPY+7iX9l5rJJrX1/HwxeN4Lopfb0tmuAEiqJ0nsbUGWaTutp/fItq5JVlQ02xuuLa2KA+rBQLhEVBRDxEJKipIwmZEJ+p/ttjgLV41/vGdFfY0nBavW4ztuND4bP5sP1D9Y1+p8AFCx13UroiqgcMnqluoKboHFmjOi6Hf1JTdfK3qtvq5yEkEvpOU1e8+5/mdcO7tf6OWCMDY4u/hP/eDxYTJA+BSxdBr/HuESI00uqIWJ3AmhLIWaUa+4d+gIpcVZ/Zq2DlwxCdqjp/2mdiUt0jlx200Z81MjWx+D/wxf+BYoa0UXDpK5A+yj1ChISp0aesGXDGA1BfATm/wOEf4ND3qtOSu1bdfnhcTXXs30x/8b3cI5ePIM4K/pvG1JxRveP57+0zuOXdTezJr+S6N9bzh1lDuOnk/hjcnB/j784eqDUsn9w2nXuXbmXlnkLuXbaNzbllPHDecCLD3Lt64Y81F61JjYvgk99N595lW/l21wn++Ml2NueW8dAFwzvvpuICAkF/yTHhLLt1Gn/4eDtfbs/ngc92svlIOQ9fNNzjaZ1C9yirNVFtbSlvV0ppoxH2fwPbl6qGsbGq68+YalQHpiN0erWIPGUopA5TowIZ49SaAh+/T6rqTZTWGImknsEr56jRDp0Bzn5UjaZ4wimITISh56sbqPUah39Uje7DP0J1gWpAHvpefT8qWXVatMhLvGcWutqj3mTmRGUD1xlW0ufnt9QXR1wKF7+sOrmeIjpJ/d4Rl6o/0iWH4NB3qs6yf4aaQvWa375U3T99VJPh3Wea2sXNC5jMFo6X1zHH8C1Za621JKOvgYte9KxMEfEw9Dx1Ayg7Yr3mvoPDq1SHetenTal9KcNU3Q08Q62hCbBOY+Ks4P8r2xqZPaL49HfT+eMn2/nftuM89fVevt9byLNXjXFrHUYgOHsAcRGhvDZ7Is+v3M9L3x/kvXW5rDlUwsKrx7o1LcyWRufnU4+iw0N45boJvPT9QRZ+t58PN+Sx7nAJz1091q1pYf5a89OaqLAQ/vmbcYzIiOPpb/fxyeajrM8u4fmrx0pamB+hzR5K76ptbEO1mt6x9p8tHY/IRNVYSx2uRkhiUtWUk1Drb7hOB8ZadeW1vlw1pCvyoDxPrSMo3q++rqWQNC8ij05RnZaMcWoaScY4iOvpahXYTXvPjNzSWsIx8nbEc4Qc2QlhsXDVEhh4lsflsxGbDmOuUTdFUYvMD/2grnrn/KK2S25RazDI6ricDlknua3WoL2fvLzSWi7Tr+KJUKujMm0BnP2Ydx8wOh0kD1S3KbeqUcK8X5ucl/xtULBD3X55Qb3W+86AgWeqBnjyYLcYGO0NEj5WVscV+h94JNTqqJx0D5z5V+8bOIl91RS+ib9VC/ePbVL1d/A7OL65qVh/3b/AEA59p1udlzPV3xJvy99NxFkhMCIDGpFhBl68ZiwzBiTx6Be7WZ9dyqznV3H3WYOZOyPLLe2NA8XZA3UOy30zhzC5Xw9+/9E2DhfVcOnLa5gzLYu7zx5EnBtWuf255qI1er2Ou84axKR+idy3bBs5JbVc8coaZk/ty70zhxAf6Xr9BdL9q9PpuO20gUzs24N7lm7laFkdV726lmsn9+EPs4aQEOWd9tCC/XRZr6IosONj+PYv6uoyqB2sxlwDIy6B9DHdMyy14t3CPapRfWKXmr5UuEd1ig4sVzeNmHQ1NShjPPQa55U2vs27MeUWV7Mw9F9MZieExcDsz9S2uL6CTqdGq1KHwbTbrLUGvzZFXo5vhpID6vbra2pUqPdEa8rO6WpBtqvnkzTLY6rYt4q/hb6u/jH1Npj5uO8ZqiHh0O9kdTvrYagusurPGjmoPqE2UtCaKcT1VnU38Ey3NDto3gysYs/3PBFidfRm3OUbjkprDCHQZ4q6nf4XteYq+yfVcTn0vdro4LDVmV7xkHqPa45L/9PVqJefIc4K/l1g3x46nY5rJvdh2oAk7lu2jY1Hynjiqz0s3ZjHwxeO4KRB3SgMa4dAMhY1Th6Uwrd3n8KD/9nJF9vzeeuXbP63/Th/Pmcol4zr5dLUOiWAnD2N6QOS+eauU/jr5zv5z9bjvL32CF/uyOeP5wzl8vG9Xaq/QHKWNSb368HXd5/MI5/v5pPNR3l/fS5fWfV35YTe9g3KE7yCNuOi3a54NSXw2a1NRlhiP7XYefRVrjNgteLd2HTVwNMw1amOy/EtTVvRXjWlad9X6qaRmGV1XsY3RWHCY1wjXxckb36RSYYNNOpCCbl2qW85Ku0REqZGT7JOgjMeVNNzsn+2Gos/qp3G8tar209/UyNF/U5uak/ryqhBxTFG/Hw7YTozm2JOZcLMJ3zP0G6PmBQYfaW6KYp6nWqOy5G1UHlUnQuz5V01xTFjvKq7PlOh9ySITHCNHOV5DF11O6E6M+uiz2DqWY/4h/6ierRMuSve3+S45KxW7/Ft/1Y3rVlE/9PUCG7mZL/odCfOCoGTxtSavknRLLt1Gh9vOsrfvtnLwcJqrn9zPVP69eCeswcz1UUF+IHm7GkkRIXxz2vHc9XEIv76+S6yi2u476NtLPrpEHefNZhzR6a7ZDq1tqgTaPqLjwpl4TXjuHJiJv/3350cKqrhjx9vt+nvglE9Xao/f65ZaY+4iFCevWoMV07szf/9dyf7T1Rz/6c7ePWnQ9x11iAuGuNap1lwDR1GVo5tgqU3qIaXIVx1Umbc6bk8+NBIdYW/98Sm14w1atem45uthf2bVeO6LEfdtHx4dOrkcpsDMx7SR3Zb9jZpOIe+Z1LOIgC+6/8nZmWd1K3je4XIRBh+kbqBWmtw2Fpknv2T6sw0dw4jE1WDWxus2GsChMfa9VUtfvIsFvjP74g0lbHL0pfvhz7CBH/MLdbp1GsrfaR6fxhr1WYHmvNStFdtmXxso/YBNc2pzxS1S1ufKXYPp2xPf+GmCrZb+vHd4IeY6o/PFJ31Xk0Zokb+TPWQt67JeTmxs6lZxC8L1c8kD1Edvz5T1eGUPfr7nEEszgrNuwn51v8cV6DX67hqUiazRqSz8Lv9vL8ul/XZaovjyf16cOOMLM4altatldpAdfY0Thmcwjd3n8ybq7NZ9OMhDhRWc/u/NzMoNYa5M7K4bFzvbhXhWyz+3c2qK2YMTObru05hyZpsXv7xEIeLarjzgy28sHI/c2f047JxvbpVhB8oNT8dMbV/El/eeTLvrD3Cv344SE5JLfcs3caL3x1k7vQsLp/Qmxg3NzEQ7OdIe53ADn0PH1yrDijsMQCufled/u1twqLVrlZ9pzW9Vlemtu61OTBb1LSSor3qtu3f6n76UPUcmjsvyUOcKuJWtO/9z+0AvNd4JuHDrun26fkEiX3VVssT5qpDKgu2N9W75K5Xz7t5ap5OrxrfvSdZo1qj1b87cQwVFFi/CLJ/okEXzh2mO5iX4vur5XYRFgWDzlI3gIpj6v10ZI3aGassGwp3qdtGa/pWTLq1NmustTZrbNetmtf+E3J+pl4XwZ2mBcxN9lz7fbcSGtEUxeMxtcbt0A9q44rc9Wq6YvE+dWs+nLJFbdtYiO3pVSNFnnA0X9n2qhhuJT4qlL9eOIKbT+7Pv344yLKNefyaXcqv2aVkxEdw3dS+XDw2w6lC/EB29jTCQwzcdtpArp/alzd/zuat1dkcKKzmgc928o9v9nHVxN5cNr43w3o6XkjZ5OwFrv7CQvTccsoAfjO5D4t/yeH1nw9zqKiGh/6zk398s5erJmZy2fheDO8Z57AemtLoAld/oQY9807qx9WTMnl7TQ6vrTpMdnENf/18F898u4/LJ/Tm8vG9GdnLcf0JrkVru2sbKLfvG1g2G8xGdUbClYvVTj++SmSitaVssxSyqhMtoy/HNkNdadMKLVYjUadXU9vShkPqCOu/w9WV2q5mQnz1R6g6Tq4ug8cbr+ftQJxRozc0GYEn36vWuxTsaBoMeHSD2izhxE5127TY+rkQtbNb+mjVeUkfrTqHqDVs/TgO3z0CwKLw33K4LoO+PfxjAKPDxPeC8bPVDdSBjbnr1DS73HVqsX51Aez/Wt00YtKbnJf0Ueq1Gao2lxigO6ZOpgdejbyJnLqeHh9u7DFi02Hsb9QN1Nbomu7y1jcNp2xd2xad2kp/I9R73UOrhDqleWWbH1NZWUl8fDwVFRXExTlmMP6wr5DfLt7AqF7x/O8OPww7O0F+RR3vrTvCB7/mUVpjtL0+OasHF43NYOaINFJj7evT/9P+Iua89SsjMuL48s6T3SWyT1FZb2LZhjzeWXvElvYBMCQtlovHZXDuyJ52TyJfc6iYa19fz+C0GJbfc6q7RPYpqupNfLzpKG+vySGnpEl/g1JjuGRcL84ZmU7/5Gi7DO+NOaVcsWgtWUlR/PiH07vcPxCobmjk081HWfJLDoeLa2yvD0iJ5pKxvTh3VDoDUmI84rh057fXF3Cl/PUmM8P+7xsUBTY9eBZJZdvh7QugsR6GXQiXv+m1lqwuRVGg/Ihq2DSfC1Nb0v7+IRFqbUbyIHUqd9IgSB7IUX0vTlq4gfNCt/Cy4WkUnZ4rjI+wyTyAdfefSXq878+KcTmV+arzcnSDmqJXsF2NvrSDObYXP5cnM1B/jN66YpT+pzNk/y0YGxVW/eH0wDW4O8NUp0YG87c2/Vu8X51P1AolNIptDelk6EpJ1ZWjDDybkfvnUWO0sPLeUxmY6pk6LZ/CVK86fM31V7S3Xf0RGqU60f1Pg7P+6tTX2fv7K84K8P3eE9y4ZCOje8fz+YLgcFY06k1m/rftuLVNammLrhgje8Vx2uBUThuSwujeCYSFtO9BB6Ozp2G2KPywt5CPNx3l+72FGM1NN3RWUhSnDUnl9KGpTOyb2GGq0y8Hi7nujfUMTY/lm7tP8ZToPoHFovDT/iI+2pTHyj2FGBub9NenRxRnDE3l1CEpTMrq0WGq06/ZpVz16lr6J0fz/e9P85DkvoHForDqQBEfbTrKyt0naGimv96JkZwxVL1/J2X1cNu8FnFWmjhYWM1Zz/1EdJiBnXcPQffGWWpb28HnwNXvq118AhVFUVe5C3fBid1N08sL96rpbx1QqCSQSBWhOjOV429j9JqTCAvRs/fRc1xS0+b3KApUHFWdFs15yd+u1j41J7YnhVd9weR/7cOg17HvsXOkEYeGsUaNYB3fohrghbugaJ8a7bSixGdSfs0XjHthFzod7Hn0nM5bjwcTxlo10qc5Lyd2qvprrFffH3wOXLvUqUPb+/sbwL+c9hMIQ+WcJSLUwJUTM7lyYib5FXX8b9txvtiez/ajFew8VsnOY5X884eDhIfoGdM7gQlZiUzsm8jIXvGkxoaj0+maFdh7+WS8gEGv46zhaZw1PI2KWhNf78znf9uP82t2KTkltSxZk8OSNTkY9DqG94xjQt9EJmYlMrpXAr0TI9HrdUGRRtcRer2O04eqDl1lvYlvdhTwv+3HWXe4hNzSJv3pdTCsZxyTsnowoW8io3rF06dHVCv9eflkvIBer+O0IamcNiSVqnoT3+ws4H/b81l3qISjZXW8s/YI76w9gl4HQ9PjmJiVyIS+iYzunUBfq/4E15FrnRw+sEcIug+vUx2V9NFqRCWQHRWwdiFLUzdtijmodRplOerqrDb7pdj6b00hqbpydb9+p7Jz0O2wZpvt3hZQ9ZqQqW7akEqAujJKc3bwzHv/IV5Xy59ueYTsIvUa65UQKY5Kc8KimwrINcyNlB/by18WLSVBV8MTtz5IdrF6zXU5IynYCItSu4ZlTm56zWJWm3Gc2OWRtNYA//W0j6Y5F96Vw9v0jI/kllMGcMspAyiqamDV/iJ+2FfILweLKas18WtOKb/mlNr2T4wKZWh6HMfKratmwWgtNiM+KpRrJvfhmsl9qKo38cvBEn7YW8jPB4o4XlHPjmMV7DhWwZI1OQBEhxkYkh5LYVUDEFitd50hLiKUqyZlctWkTGoaGvnlYDE/7Cvi5wNFHC2rY9fxSnYdr7TpL8qqv/JaExCczl5zYiNCbQsPtcZG1hws4Yd9haw6UEReaR278yvZnV/JO2uPABAZqupvWM9YhqarjuDwDP+LjPgSWtvieyxvq6u30Slw7TKPtf31SfQGSBqgbq04mp/PbS9+RHyIiXdn303OhmNAJzNqhCYiEzH1msy/zVUY9Dr+FJtG7r48oFVzB6F9DCFYkgbzlUV1YB6PSCSv9DjQrN5M6Bi9QU3rTB7kka9zyll5+eWXefrpp8nPz2fEiBEsXLiQk0/uuFbhp59+4t5772XXrl1kZGTwxz/+kfnz57fY55NPPuGhhx7i0KFDDBgwgCeeeIJLL73UGfEcxhKgrXe7Q0psuFq0O6E3iqJwqKiGTUdK2ZhTxubcMrKLayirNbH2cFOOcphB9KcRGxHKOSPTOWek2oHkWHkdG3NK2XRE1d/+gmpqjGY255bbPtNRml0wEh0ewswR6cwcoeqvoKKejdbrb9ORMvadqKLWaGZLc/3JSqKNqLAQW8QP4ERlPRtzytiQU8rm3DL2FVRRZzKzNa+crXnlAMydnsXDF/lAh6pmfPnllzz66KNs376d6OhoTjnlFD799FPb+7m5udx+++18//33REZGcu211/LMM88QFuad4ZlHSms5Tb+F0yo/V1+4dJFXJ8T7Okp4PNuVAUSgB72BI9bIlDgrzpHburmD4BBHSjpoOy54HYedlaVLl3L33Xfz8ssvM2PGDF599VXOPfdcdu/eTZ8+fdrsn52dzXnnncfNN9/Me++9xy+//MJtt91GSkoKl19+OQBr167l6quv5rHHHuPSSy/ls88+46qrrmL16tVMmTKl+2fZBYE4lM+V6HQ6BqbGMDA1hqsnqf+P601mDhZWsye/kr0FVRwpqeXaKZleltR36ZUQSa+xvbh4bC8ATGYLOcU17CmoYm9+JUdKarliQm8vS+m7pMdHcMHoDC4YnQFAo9lCTkkNe/Kr2FtQSU5xLZeM6+VlKX2XtLgIzh/dk/NHq4azqr9a9hZUsteqw4lZvtWq85NPPuHmm2/mySef5IwzzkBRFHbs2GF732w2c/7555OSksLq1aspKSlhzpw5KIrCSy+95BWZC4tLeDzU2sFp6m0w8CyvyOEvtF4fzOtoRo3QLq1tFs1Z6Sv6swvRn//gcIH9lClTGD9+PK+88orttWHDhnHJJZfw1FNPtdn/T3/6E59//jl79uyxvTZ//ny2bdvG2rVrAbj66quprKzk66+b2sydc845JCYm8sEHH7QrR0NDAw0NDba/KysryczMdKpI8usd+fzu/c1MzurBsvnTuv6AIAiCALinwL6xsZGsrCweeeQR5s2b1+4+X3/9NRdccAF5eXlkZKhO7IcffsjcuXMpLCzsUBZXPjuKC/I4vPRPtr/1ZdlMZDf10b2IuGuDmisvdMjRslpO+vsP6HVw5YRMvttbSHF1A2/cMNEWFRQ6prCynslPfgfA1RMz+WFfIYVVDbxy3XjOHSURva4oqzEy7rEVAFw1sTer9hdTUFnPC9eMtS0sCu7F3ueHQ3kTRqORTZs2MXPmzBavz5w5kzVr1rT7mbVr17bZf9asWWzcuBGTydTpPh0dE+Cpp54iPj7etmVmOr+qH+hDDQVBEPyJzZs3c+zYMfR6PePGjaNnz56ce+657Nq1y7bP2rVrGTlypM1RAfW50dDQwKZNmzo8tiufHXXV5Uwu+9K2TWQ3ADVn/V0cFTuICQ/BoNdhUWDpxjyKq1UncnCafRPcg53IMAOh1vTrpRvzbPWPg0R/dhERarClXy/beJSCSrW7lVx/vodDaWDFxcWYzWbS0lqueKSlpVFQUNDuZwoKCtrdv7GxkeLiYnr27NnhPh0dE+D+++/n3nvvtf2trY45w5D0WP54zhB6BmNPd0EQBB/j8OHDADz88MM899xzZGVl8eyzz3Lqqaeyf/9+evTo0e5zIzExkbCwMI89O+ISU1nb7/YWr0VnDGP0uAudOl6wkRAVxptzJrLreKXttUGpMcE5H8QJYiNCeWvuJLYfrbC9NiAlOjjngzhBZJiBJXMnscVatweQlRTt1HBnwb04VWDfuuuOoiidduJpb//Wrzt6zPDwcMLDXTNcS63HGOiSYwmCIAjt8/DDD/PII490us+GDRuwWNR5MQ888ICttnHx4sX07t2bjz76iFtvvRVovwOcJ58d8UlpTJvzpEuOFaxorbcF5zh5UAonD0rxthh+y/SByUwfmOxtMYQucMhZSU5OxmAwtFm1KiwsbLPCpZGent7u/iEhISQlJXW6T0fHFARBEPyPBQsWcM0113S6T1ZWFlVVVQAMHz7c9np4eDj9+/cnNzcXUJ8b69evb/HZsrIyTCaTPDsEQRACCIdqVsLCwpgwYQIrVqxo8fqKFSuYPn16u5+ZNm1am/2XL1/OxIkTCQ0N7XSfjo4pCIIg+B/JyckMHTq00y0iIoIJEyYQHh7Ovn37bJ81mUzk5OTQt29fQH1u7Ny5k/z8fNs+y5cvJzw8nAkTJnj83ARBEAT34HAa2L333svs2bOZOHEi06ZN47XXXiM3N9c2N+X+++/n2LFjvPPOO4Da+euf//wn9957LzfffDNr167lzTffbNHl66677uKUU07h73//OxdffDH//e9/WblyJatXr3bRaQqCIAj+QlxcHPPnz+evf/0rmZmZ9O3bl6effhqAK6+8ElAbuwwfPpzZs2fz9NNPU1payu9//3tuvvlml3UlEwRBELyPw87K1VdfTUlJCY8++ij5+fmMHDmSr776yrbalZ+fbwvTA/Tr14+vvvqKe+65h3/9619kZGTw4osv2vKQAaZPn86HH37Igw8+yEMPPcSAAQNYunSpR2asCIIgCL7H008/TUhICLNnz6auro4pU6bw/fffk5iozoMxGAx8+eWX3HbbbcyYMaPFUEhBEAQhcHB4zoqv4o5e/4IgCELn+Ptvr7/LLwiC4K+4Zc6KIAiCIAiCIAiCpxBnRRAEQRAEQRAEn0ScFUEQBEEQBEEQfBKnhkL6IlrpTWVlZRd7CoIgCK5C+8311/JHeXYIgiB4B3ufHwHjrGhDxDIzM70siSAIQvBRVVVFfHy8t8VwGHl2CIIgeJeunh8B0w3MYrFw/PhxYmNj0el0Dn22srKSzMxM8vLygrIbjJy/nL+cv5y/s+evKApVVVVkZGSg1/tfZnF3nh0g14+cv5y/nL+cv7ufHwETWdHr9fTu3btbx4iLiwvKi01Dzl/OX85fzt8Z/DGiouGKZwfI9SPnL+cv5y/n7wz2PD/8bxlMEARBEARBEISgQJwVQRAEQRAEQRB8EnFWgPDwcP76178SHh7ubVG8gpy/nL+cv5x/sJ5/dwl2/cn5y/nL+cv5u/v8A6bAXhAEQRAEQRCEwEIiK4IgCIIgCIIg+CTirAiCIAiCIAiC4JOIsyIIgiAIgiAIgk8izoogCIIgCIIgCD6JOCvAyy+/TL9+/YiIiGDChAn8/PPP3hbJLTz11FNMmjSJ2NhYUlNTueSSS9i3b1+LfRRF4eGHHyYjI4PIyEhOO+00du3a5SWJ3cdTTz2FTqfj7rvvtr0WDOd+7Ngxrr/+epKSkoiKimLs2LFs2rTJ9n4g66CxsZEHH3yQfv36ERkZSf/+/Xn00UexWCy2fQLp/FetWsWFF15IRkYGOp2O//znPy3et+dcGxoauOOOO0hOTiY6OpqLLrqIo0ePevAsfBt5djQRSPdOZ8izQ54d8uzwwrNDCXI+/PBDJTQ0VHn99deV3bt3K3fddZcSHR2tHDlyxNuiuZxZs2YpixcvVnbu3Kls3bpVOf/885U+ffoo1dXVtn3+9re/KbGxsconn3yi7NixQ7n66quVnj17KpWVlV6U3LX8+uuvSlZWljJ69Gjlrrvusr0e6OdeWlqq9O3bV5k7d66yfv16JTs7W1m5cqVy8OBB2z6BrIPHH39cSUpKUr744gslOztb+eijj5SYmBhl4cKFtn0C6fy/+uor5YEHHlA++eQTBVA+++yzFu/bc67z58////buPybq+o8D+PM4fp2/roPDu5RBakxI0BDGGqFoOvqhpbNcEeGpfziYECebabXU/pD8o7nWajTKWBvW+Q9Nq5lh2iGrxTogQVpqqRhCtxap6xCCe33/8HsfPQ8Vkfvh556P7Tbv/X7fvd+vNz+ee/uBDzJ9+nRpaGiQlpYWWbx4scybN0+GhoYCXE3oYXYwOzzUXjuzg9lxvWBkR9gfVnJycqSkpMSrLTU1VbZu3RqkFQWO0+kUAGK320VExO12i9lsll27diljrly5Inq9Xj744INgLXNcXb58WVJSUqShoUHy8/OVwAmH2rds2SJ5eXk37Vf7HixbtkzWr1/v1bZq1Sp56aWXRETd9d8YOKOp9Z9//pGoqCix2WzKmO7ubomIiJCvv/46YGsPVcwOZodIeNTO7GB2eAQrO8L6x8AGBwfhcDhQUFDg1V5QUIDvv/8+SKsKnIsXLwIA4uLiAABnzpxBb2+v137ExMQgPz9fNfuxceNGLFu2DEuXLvVqD4faDxw4gOzsbKxevRpTp05FZmYmPvzwQ6Vf7XuQl5eHb7/9FidPngQA/Pzzz2hqasJTTz0FQP31X280tTocDvz3339eY6ZNm4b09HTV7cedYnYwOzzCoXZmB7PDI1jZEXl3y763/fXXXxgeHobJZPJqN5lM6O3tDdKqAkNEUFlZiby8PKSnpwOAUvNI+3Hu3LmAr3G82Ww2OBwO/PTTTz59aq8dAH7//XdUV1ejsrISr732Gpqbm/Hyyy8jJiYGa9asUf0ebNmyBRcvXkRqaiq0Wi2Gh4exc+dOFBYWAgiPzwGP0dTa29uL6OhoGAwGnzFq//54O8wOZoeH2msHmB3MjmuClR1hfVjx0Gg0Xs9FxKdNbcrKynD8+HE0NTX59KlxP86fP4+Kigp88803iI2Nvek4Ndbu4Xa7kZ2djaqqKgBAZmYmTpw4gerqaqxZs0YZp9Y92LdvH+rq6vDpp59izpw5aGtrg9VqxbRp02CxWJRxaq1/JGOpVc37cafC6XPFg9kxMjXW7sHsYHbcKNDZEdY/BmY0GqHVan1Oek6n0+fUqCbl5eU4cOAAjh49isTERKXdbDYDgCr3w+FwwOl0IisrC5GRkYiMjITdbse7776LyMhIpT411u5x//3346GHHvJqS0tLQ1dXFwB1f/wBYPPmzdi6dSteeOEFZGRkoLi4GJs2bcJbb70FQP31X280tZrNZgwODqKvr++mY8IVs4PZwexgdjA7rvF3doT1YSU6OhpZWVloaGjwam9oaEBubm6QVuU/IoKysjLU19fjyJEjmDFjhlf/jBkzYDabvfZjcHAQdrv9nt+PJUuWoL29HW1tbcojOzsbRUVFaGtrw8yZM1Vbu8ejjz7qc7vRkydPIjk5GYC6P/4A4HK5EBHh/S1Pq9Uqt59Ue/3XG02tWVlZiIqK8hrT09ODjo4O1e3HnWJ2MDuYHcwOZsdVAcmOMf1avop4bj+5Z88e6ezsFKvVKhMnTpSzZ88Ge2njrrS0VPR6vXz33XfS09OjPFwulzJm165dotfrpb6+Xtrb26WwsPCevf3e7Vx/RxcR9dfe3NwskZGRsnPnTjl16pTs3btXJkyYIHV1dcoYNe+BxWKR6dOnK7efrK+vF6PRKK+88ooyRk31X758WVpbW6W1tVUAyO7du6W1tVW5te5oai0pKZHExEQ5fPiwtLS0yGOPPcZbF/8fs4PZ4aH22pkdzI5gZ0fYH1ZERN5//31JTk6W6OhomT9/vnI7RrUBMOKjtrZWGeN2u2X79u1iNpslJiZGFi5cKO3t7cFbtB/dGDjhUPsXX3wh6enpEhMTI6mpqVJTU+PVr+Y9uHTpklRUVEhSUpLExsbKzJkz5fXXX5eBgQFljJrqP3r06Ihf7xaLRURGV2t/f7+UlZVJXFyc6HQ6Wb58uXR1dQWhmtDE7KhVxqjpa+d2mB3MDmZHYLNDIyIytmsyRERERERE/hPWv7NCREREREShi4cVIiIiIiIKSTysEBERERFRSOJhhYiIiIiIQhIPK0REREREFJJ4WCEiIiIiopDEwwoREREREYUkHlaIiIiIiCgk8bBCdBs7duzAww8/HLT533jjDWzYsMFv7+90OpGQkIDu7m6/zUFEFG6YHUTjg3/BnsKaRqO5Zb/FYsF7772HgYEBxMfHB2hV1/z5559ISUnB8ePH8cADD/htnsrKSly6dAkfffSR3+YgIlILZsdVzA4KBB5WKKz19vYq/963bx+2bduGX3/9VWnT6XTQ6/XBWBoAoKqqCna7HYcOHfLrPO3t7cjJycGFCxdgMBj8OhcR0b2O2XEVs4MCgT8GRmHNbDYrD71eD41G49N246X8tWvXYuXKlaiqqoLJZMJ9992HN998E0NDQ9i8eTPi4uKQmJiIjz/+2Guu7u5uPP/88zAYDIiPj8eKFStw9uzZW67PZrPhmWee8WpbtGgRysvLYbVaYTAYYDKZUFNTg3///Rfr1q3D5MmTMWvWLBw8eFB5TV9fH4qKipCQkACdToeUlBTU1tYq/RkZGTCbzfj888/HvplERGGC2XEVs4MCgYcVojE4cuQILly4gMbGRuzevRs7duzA8uXLYTAY8OOPP6KkpAQlJSU4f/48AMDlcmHx4sWYNGkSGhsb0dTUhEmTJuGJJ57A4ODgiHP09fWho6MD2dnZPn2ffPIJjEYjmpubUV5ejtLSUqxevRq5ubloaWnB448/juLiYrhcLgBXf3a5s7MTBw8exC+//ILq6moYjUav98zJycGxY8fGeaeIiMiD2UE0BkJEIiJSW1srer3ep3379u0yb9485bnFYpHk5GQZHh5W2mbPni0LFixQng8NDcnEiRPls88+ExGRPXv2yOzZs8XtditjBgYGRKfTyaFDh0ZcT2trqwCQrq4ur/b8/HzJy8vzmau4uFhp6+npEQDyww8/iIjI008/LevWrbtl/Zs2bZJFixbdcgwREXljdjA7yL8ig3tUIro3zZkzBxER1y5MmkwmpKenK8+1Wi3i4+PhdDoBAA6HA6dPn8bkyZO93ufKlSv47bffRpyjv78fABAbG+vTN3fuXJ+5MjIyvNYDQJm/tLQUzz77LFpaWlBQUICVK1ciNzfX6z11Op3yv2lERDT+mB1Ed46HFaIxiIqK8nqu0WhGbHO73QAAt9uNrKws7N271+e9EhISRpzDc6m9r6/PZ8zt5vfcqcYz/5NPPolz587hq6++wuHDh7FkyRJs3LgRb7/9tvKav//++6ZrISKiu8fsILpz/J0VogCYP38+Tp06halTp+LBBx/0etzsjjGzZs3ClClT0NnZOS5rSEhIwNq1a1FXV4d33nkHNTU1Xv0dHR3IzMwcl7mIiOjuMTuIeFghCoiioiIYjUasWLECx44dw5kzZ2C321FRUYE//vhjxNdERERg6dKlaGpquuv5t23bhv379+P06dM4ceIEvvzyS6SlpSn9LpcLDocDBQUFdz0XERGND2YHEQ8rRAExYcIENDY2IikpCatWrUJaWhrWr1+P/v5+TJky5aav27BhA2w2m3JJfqyio6Px6quvYu7cuVi4cCG0Wi1sNpvSv3//fiQlJWHBggV3NQ8REY0fZgcR/ygkUUgTETzyyCOwWq0oLCz02zw5OTmwWq148cUX/TYHEREFBrOD1IRXVohCmEajQU1NDYaGhvw2h9PpxHPPPefXQCMiosBhdpCa8MoKERERERGFJF5ZISIiIiKikMTDChERERERhSQeVoiIiIiIKCTxsEJERERERCGJhxUiIiIiIgpJPKwQEREREVFI4mGFiIiIiIhCEg8rREREREQUknhYISIiIiKikPQ/O3mTkDOD4VEAAAAASUVORK5CYII=\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_syn_model(AMPASparse)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92903cb0",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We hope this tutorial will help your synapse models be defined efficiently. "
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "encoding": "# -*- coding: utf-8 -*-"
-  },
-  "kernelspec": {
-   "name": "brainpy",
-   "language": "python",
-   "display_name": "brainpy"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
-   "autoclose": false,
-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 1,
-   "hotkeys": {
-    "equation": "Ctrl-E",
-    "itemize": "Ctrl-I"
-   },
-   "labels_anchors": false,
-   "latex_user_defs": false,
-   "report_style_numbering": false,
-   "user_envs_cfg": false
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "279.273px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/docs/tutorial_toolbox/inputs.ipynb b/docs/tutorial_toolbox/inputs.ipynb
index cb99e6400..759668b8d 100644
--- a/docs/tutorial_toolbox/inputs.ipynb
+++ b/docs/tutorial_toolbox/inputs.ipynb
@@ -41,63 +41,6 @@
    "execution_count": 1,
    "outputs": []
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Inputs in ``brainpy.dyn.DSRunner``"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "In brain dynamics simulation, various inpus are usually given to different units of the dynamical system. In BrainPy, `inputs` can be specified to [runners for dynamical systems](runners.ipynb). The aim of ``inputs`` is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording.\n",
-    "\n",
-    "``inputs`` should have the format like ``(target, value, [type, operation])``, where \n",
-    "- ``target`` is the target variable to inject the input.\n",
-    "- ``value`` is the input value. It can be a scalar, a tensor, or a iterable object/function.\n",
-    "- ``type`` is the type of the input value. It support two types of input: ``fix`` and ``iter``. The first one means that the data is static; the second one denotes the data can be iterable, no matter whether the input value is a tensor or a function. The `iter` type must be explicitly stated. \n",
-    "- ``operation`` is the input operation on the target variable. It should be set as one of `{ + , - , * , / , = }`, and if users do not provide this item explicitly, it will be set to '+' by default, which means that the target variable will be updated as ``val = val + input``. "
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Users can also give multiple inputs for different target variables, like:\n",
-    "\n",
-    "```python\n",
-    "\n",
-    "inputs=[(target1, value1, [type1, op1]),  \n",
-    "        (target2, value2, [type2, op2]),\n",
-    "              ... ]\n",
-    "```"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9c7d3ca",
-   "metadata": {},
-   "source": [
-    "The mechanism of ``inputs`` is the same as [``monitors``](monitors.ipynb). BrainPy finds the target variables for input operations through [the absolute or relative path](../tutorial_math/base.ipynb). "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3451b77b",

From 417c1edddca3a11dd2e0b887a6adbbb83cbd30ce Mon Sep 17 00:00:00 2001
From: Brandon Zhang <ztqakita@163.com>
Date: Thu, 11 Aug 2022 22:01:10 +0800
Subject: [PATCH 6/6] Update biological_models.py

---
 brainpy/dyn/neurons/biological_models.py | 80 +++++++++++++++---------
 1 file changed, 49 insertions(+), 31 deletions(-)

diff --git a/brainpy/dyn/neurons/biological_models.py b/brainpy/dyn/neurons/biological_models.py
index 566e5024d..86c7a7b2e 100644
--- a/brainpy/dyn/neurons/biological_models.py
+++ b/brainpy/dyn/neurons/biological_models.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-from typing import Union, Callable
+from typing import Union, Callable, Optional
 
 import brainpy.math as bm
 from brainpy.dyn.base import NeuGroup
@@ -204,9 +204,9 @@ def __init__(
       V_th: Union[float, Array, Initializer, Callable] = 20.,
       C: Union[float, Array, Initializer, Callable] = 1.0,
       V_initializer: Union[Initializer, Callable, Array] = Uniform(-70, -60.),
-      m_initializer: Union[Initializer, Callable, Array] = OneInit(0.5),
-      h_initializer: Union[Initializer, Callable, Array] = OneInit(0.6),
-      n_initializer: Union[Initializer, Callable, Array] = OneInit(0.32),
+      m_initializer: Optional[Union[Initializer, Callable, Array]] = None,
+      h_initializer: Optional[Union[Initializer, Callable, Array]] = None,
+      n_initializer: Optional[Union[Initializer, Callable, Array]] = None,
       noise: Union[float, Array, Initializer, Callable] = None,
       method: str = 'exp_auto',
       name: str = None,
@@ -233,9 +233,9 @@ def __init__(
     self.noise = init_noise(noise, self.varshape, num_vars=4)
 
     # initializers
-    check_initializer(m_initializer, 'm_initializer', allow_none=False)
-    check_initializer(h_initializer, 'h_initializer', allow_none=False)
-    check_initializer(n_initializer, 'n_initializer', allow_none=False)
+    check_initializer(m_initializer, 'm_initializer', allow_none=True)
+    check_initializer(h_initializer, 'h_initializer', allow_none=True)
+    check_initializer(n_initializer, 'n_initializer', allow_none=True)
     check_initializer(V_initializer, 'V_initializer', allow_none=False)
     self._m_initializer = m_initializer
     self._h_initializer = h_initializer
@@ -243,10 +243,19 @@ def __init__(
     self._V_initializer = V_initializer
 
     # variables
-    self.m = variable(self._m_initializer, mode, self.varshape)
-    self.h = variable(self._h_initializer, mode, self.varshape)
-    self.n = variable(self._n_initializer, mode, self.varshape)
     self.V = variable(self._V_initializer, mode, self.varshape)
+    if self._m_initializer is None:
+      self.m = bm.Variable(self.m_inf(self.V.value))
+    else:
+      self.m = variable(self._m_initializer, mode, self.varshape)
+    if self._h_initializer is None:
+      self.h = bm.Variable(self.h_inf(self.V.value))
+    else:
+      self.h = variable(self._h_initializer, mode, self.varshape)
+    if self._n_initializer is None:
+      self.n = bm.Variable(self.n_inf(self.V.value))
+    else:
+      self.n = variable(self._n_initializer, mode, self.varshape)
     self.input = variable(bm.zeros, mode, self.varshape)
     self.spike = variable(lambda s: bm.zeros(s, dtype=bool), mode, self.varshape)
 
@@ -256,32 +265,41 @@ def __init__(
     else:
       self.integral = sdeint(method=method, f=self.derivative, g=self.noise)
 
+  # m channel
+  m_alpha = lambda self, V: 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))
+  m_beta = lambda self, V: 4.0 * bm.exp(-(V + 65) / 18)
+  m_inf = lambda self, V: self.m_alpha(V) / (self.m_alpha(V) + self.m_beta(V))
+  dm = lambda self, m, t, V: self.m_alpha(V) * (1 - m) - self.m_beta(V) * m
+
+  # h channel
+  h_alpha = lambda self, V: 0.07 * bm.exp(-(V + 65) / 20.)
+  h_beta = lambda self, V: 1 / (1 + bm.exp(-(V + 35) / 10))
+  h_inf = lambda self, V: self.h_alpha(V) / (self.h_alpha(V) + self.h_beta(V))
+  dh = lambda self, h, t, V: self.h_alpha(V) * (1 - h) - self.h_beta(V) * h
+
+  # n channel
+  n_alpha = lambda self, V: 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))
+  n_beta = lambda self, V: 0.125 * bm.exp(-(V + 65) / 80)
+  n_inf = lambda self, V: self.n_alpha(V) / (self.n_alpha(V) + self.n_beta(V))
+  dn = lambda self, n, t, V: self.n_alpha(V) * (1 - n) - self.n_beta(V) * n
+
   def reset_state(self, batch_size=None):
-    self.m.value = variable(self._m_initializer, batch_size, self.varshape)
-    self.h.value = variable(self._h_initializer, batch_size, self.varshape)
-    self.n.value = variable(self._n_initializer, batch_size, self.varshape)
     self.V.value = variable(self._V_initializer, batch_size, self.varshape)
+    if self._m_initializer is None:
+      self.m.value = self.m_inf(self.V.value)
+    else:
+      self.m.value = variable(self._m_initializer, batch_size, self.varshape)
+    if self._h_initializer is None:
+      self.h.value = self.h_inf(self.V.value)
+    else:
+      self.h.value = variable(self._h_initializer, batch_size, self.varshape)
+    if self._n_initializer is None:
+      self.n.value = self.n_inf(self.V.value)
+    else:
+      self.n.value = variable(self._n_initializer, batch_size, self.varshape)
     self.input.value = variable(bm.zeros, batch_size, self.varshape)
     self.spike.value = variable(lambda s: bm.zeros(s, dtype=bool), batch_size, self.varshape)
 
-  def dm(self, m, t, V):
-    alpha = 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))
-    beta = 4.0 * bm.exp(-(V + 65) / 18)
-    dmdt = alpha * (1 - m) - beta * m
-    return dmdt
-
-  def dh(self, h, t, V):
-    alpha = 0.07 * bm.exp(-(V + 65) / 20.)
-    beta = 1 / (1 + bm.exp(-(V + 35) / 10))
-    dhdt = alpha * (1 - h) - beta * h
-    return dhdt
-
-  def dn(self, n, t, V):
-    alpha = 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))
-    beta = 0.125 * bm.exp(-(V + 65) / 80)
-    dndt = alpha * (1 - n) - beta * n
-    return dndt
-
   def dV(self, V, t, m, h, n, I_ext):
     I_Na = (self.gNa * m ** 3.0 * h) * (V - self.ENa)
     I_K = (self.gK * n ** 4.0) * (V - self.EK)