From 344867631d2a475ddc000efc64813b2a86131b9e Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Sat, 11 Oct 2025 18:34:54 +0800 Subject: [PATCH 1/8] Update version to 2.7.1 and refactor Dynamics class: enhance input handling and add new properties --- brainpy/__init__.py | 4 +- brainpy/state/_base.py | 447 +++++++++++++++++- brainpy/state/_projection.py | 43 +- docs_state/checkpointing-en.ipynb | 675 --------------------------- docs_state/checkpointing-zh.ipynb | 675 --------------------------- docs_state/snn_simulation-en.ipynb | 680 --------------------------- docs_state/snn_simulation-zh.ipynb | 723 ----------------------------- docs_state/snn_training-en.ipynb | 709 ---------------------------- docs_state/snn_training-zh.ipynb | 709 ---------------------------- 9 files changed, 446 insertions(+), 4219 deletions(-) delete mode 100644 docs_state/checkpointing-en.ipynb delete mode 100644 docs_state/checkpointing-zh.ipynb delete mode 100644 docs_state/snn_simulation-en.ipynb delete mode 100644 docs_state/snn_simulation-zh.ipynb delete mode 100644 docs_state/snn_training-en.ipynb delete mode 100644 docs_state/snn_training-zh.ipynb diff --git a/brainpy/__init__.py b/brainpy/__init__.py index c202ab9c..18f96a33 100644 --- a/brainpy/__init__.py +++ b/brainpy/__init__.py @@ -14,8 +14,8 @@ # limitations under the License. # ============================================================================== -__version__ = "2.7.0" -__version_info__ = (2, 7, 0) +__version__ = "2.7.1" +__version_info__ = (2, 7, 1) from brainpy import _errors as errors diff --git a/brainpy/state/_base.py b/brainpy/state/_base.py index d74737ec..8be13440 100644 --- a/brainpy/state/_base.py +++ b/brainpy/state/_base.py @@ -13,17 +13,414 @@ # limitations under the License. # ============================================================================== -from typing import Callable, Optional + +from typing import Any, Union, TypeVar, Callable, Optional import brainstate import braintools +import numpy as np +from brainstate.mixin import ParamDescriber +from brainstate.typing import ArrayLike, Size + +T = TypeVar('T') __all__ = [ - 'Neuron', 'Synapse', + 'Dynamics', 'Neuron', 'Synapse', ] -class Neuron(brainstate.nn.Dynamics): +def _input_label_start(label: str): + # unify the input label repr. + return f'{label} // ' + + +def _input_label_repr(name: str, label: Optional[str] = None): + # unify the input label repr. + return name if label is None else (_input_label_start(label) + str(name)) + + +class Dynamics(brainstate.nn.Dynamics): + def __init__(self, in_size: Size, name: Optional[str] = None): + # initialize + super().__init__(name=name, in_size=in_size) + + # current inputs + self._current_inputs = None + + # delta inputs + self._delta_inputs = None + + @property + def current_inputs(self): + """ + Get the dictionary of current inputs registered with this dynamics model. + + Current inputs represent direct input currents that flow into the model. + + Returns + ------- + dict or None + A dictionary mapping keys to current input functions or values, + or None if no current inputs have been registered. + + See Also + -------- + add_current_input : Register a new current input + sum_current_inputs : Apply and sum all current inputs + delta_inputs : Dictionary of instantaneous change inputs + """ + return self._current_inputs + + @property + def delta_inputs(self): + """ + Get the dictionary of delta inputs registered with this dynamics model. + + Delta inputs represent instantaneous changes to state variables (dX/dt). + + Returns + ------- + dict or None + A dictionary mapping keys to delta input functions or values, + or None if no delta inputs have been registered. + + See Also + -------- + add_delta_input : Register a new delta input + sum_delta_inputs : Apply and sum all delta inputs + current_inputs : Dictionary of direct current inputs + """ + return self._delta_inputs + + def add_current_input( + self, + key: str, + inp: Union[Callable, ArrayLike], + label: Optional[str] = None + ): + """ + Add a current input function or array to the dynamics model. + + Current inputs represent direct input currents that can be accessed during + model updates through the `sum_current_inputs()` method. + + Parameters + ---------- + key : str + Unique identifier for this current input. Used to retrieve or reference + the input later. + inp : Union[Callable, ArrayLike] + The input data or function that generates input data. + - If callable: Will be called during updates with arguments passed to `sum_current_inputs()` + - If array-like: Will be applied once and then automatically removed from available inputs + label : Optional[str], default=None + Optional grouping label for the input. When provided, allows selective + processing of inputs by label in `sum_current_inputs()`. + + Raises + ------ + ValueError + If the key has already been used for a different current input. + + Notes + ----- + - Inputs with the same label can be processed together using the `label` + parameter in `sum_current_inputs()`. + - Non-callable inputs are consumed when used (removed after first use). + - Callable inputs persist and can be called repeatedly. + + See Also + -------- + sum_current_inputs : Sum all current inputs matching a given label + add_delta_input : Add a delta input function or array + """ + key = _input_label_repr(key, label) + if self._current_inputs is None: + self._current_inputs = dict() + if key in self._current_inputs: + if id(self._current_inputs[key]) != id(inp): + raise ValueError(f'Key "{key}" has been defined and used in the current inputs of {self}.') + self._current_inputs[key] = inp + + def add_delta_input( + self, + key: str, + inp: Union[Callable, ArrayLike], + label: Optional[str] = None + ): + """ + Add a delta input function or array to the dynamics model. + + Delta inputs represent instantaneous changes to the model state (i.e., dX/dt contributions). + This method registers a function or array that provides delta inputs which will be + accessible during model updates through the `sum_delta_inputs()` method. + + Parameters + ---------- + key : str + Unique identifier for this delta input. Used to retrieve or reference + the input later. + inp : Union[Callable, ArrayLike] + The input data or function that generates input data. + - If callable: Will be called during updates with arguments passed to `sum_delta_inputs()` + - If array-like: Will be applied once and then automatically removed from available inputs + label : Optional[str], default=None + Optional grouping label for the input. When provided, allows selective + processing of inputs by label in `sum_delta_inputs()`. + + Raises + ------ + ValueError + If the key has already been used for a different delta input. + + Notes + ----- + - Inputs with the same label can be processed together using the `label` + parameter in `sum_delta_inputs()`. + - Non-callable inputs are consumed when used (removed after first use). + - Callable inputs persist and can be called repeatedly. + + See Also + -------- + sum_delta_inputs : Sum all delta inputs matching a given label + add_current_input : Add a current input function or array + """ + key = _input_label_repr(key, label) + if self._delta_inputs is None: + self._delta_inputs = dict() + if key in self._delta_inputs: + if id(self._delta_inputs[key]) != id(inp): + raise ValueError(f'Key "{key}" has been defined and used.') + self._delta_inputs[key] = inp + + def get_input(self, key: str): + """ + Get a registered input function by its key. + + Retrieves either a current input or a delta input function that was previously + registered with the given key. This method checks both current_inputs and + delta_inputs dictionaries for the specified key. + + Parameters + ---------- + key : str + The unique identifier used when the input function was registered. + + Returns + ------- + Callable or ArrayLike + The input function or array associated with the given key. + + Raises + ------ + ValueError + If no input function is found with the specified key in either + current_inputs or delta_inputs. + + See Also + -------- + add_current_input : Register a current input function + add_delta_input : Register a delta input function + + Examples + -------- + >>> model = Dynamics(10) + >>> model.add_current_input('stimulus', lambda t: np.sin(t)) + >>> input_func = model.get_input('stimulus') + >>> input_func(0.5) # Returns sin(0.5) + """ + if self._current_inputs is not None and key in self._current_inputs: + return self._current_inputs[key] + elif self._delta_inputs is not None and key in self._delta_inputs: + return self._delta_inputs[key] + else: + raise ValueError(f'Input key {key} is not in current/delta inputs of the module {self}.') + + def sum_current_inputs( + self, + init: Any, + *args, + label: Optional[str] = None, + **kwargs + ): + """ + Summarize all current inputs by applying and summing all registered current input functions. + + This method iterates through all registered current input functions (from `.current_inputs`) + and applies them to calculate the total input current for the dynamics model. It adds all results + to the initial value provided. + + Parameters + ---------- + init : Any + The initial value to which all current inputs will be added. + *args + Variable length argument list passed to each current input function. + label : Optional[str], default=None + If provided, only process current inputs with this label prefix. + When None, process all current inputs regardless of label. + **kwargs + Arbitrary keyword arguments passed to each current input function. + + Returns + ------- + Any + The initial value plus all applicable current inputs summed together. + + Notes + ----- + - Non-callable current inputs are applied once and then automatically removed from + the current_inputs dictionary. + - Callable current inputs remain registered for subsequent calls. + - When a label is provided, only current inputs with keys starting with that label + are applied. + """ + if self._current_inputs is None: + return init + if label is None: + filter_fn = lambda k: True + else: + label_repr = _input_label_start(label) + filter_fn = lambda k: k.startswith(label_repr) + for key in tuple(self._current_inputs.keys()): + if filter_fn(key): + out = self._current_inputs[key] + if callable(out): + try: + init = init + out(*args, **kwargs) + except Exception as e: + raise ValueError( + f'Error in current input value {key}: {out}\n' + f'Error: {e}' + ) from e + else: + try: + init = init + out + except Exception as e: + raise ValueError( + f'Error in current input value {key}: {out}\n' + f'Error: {e}' + ) from e + self._current_inputs.pop(key) + return init + + def sum_delta_inputs( + self, + init: Any, + *args, + label: Optional[str] = None, + **kwargs + ): + """ + Summarize all delta inputs by applying and summing all registered delta input functions. + + This method iterates through all registered delta input functions (from `.delta_inputs`) + and applies them to calculate instantaneous changes to model states. It adds all results + to the initial value provided. + + Parameters + ---------- + init : Any + The initial value to which all delta inputs will be added. + *args + Variable length argument list passed to each delta input function. + label : Optional[str], default=None + If provided, only process delta inputs with this label prefix. + When None, process all delta inputs regardless of label. + **kwargs + Arbitrary keyword arguments passed to each delta input function. + + Returns + ------- + Any + The initial value plus all applicable delta inputs summed together. + + Notes + ----- + - Non-callable delta inputs are applied once and then automatically removed from + the delta_inputs dictionary. + - Callable delta inputs remain registered for subsequent calls. + - When a label is provided, only delta inputs with keys starting with that label + are applied. + """ + if self._delta_inputs is None: + return init + if label is None: + filter_fn = lambda k: True + else: + label_repr = _input_label_start(label) + filter_fn = lambda k: k.startswith(label_repr) + for key in tuple(self._delta_inputs.keys()): + if filter_fn(key): + out = self._delta_inputs[key] + if callable(out): + try: + init = init + out(*args, **kwargs) + except Exception as e: + raise ValueError( + f'Error in delta input function {key}: {out}\n' + f'Error: {e}' + ) from e + else: + try: + init = init + out + except Exception as e: + raise ValueError( + f'Error in delta input value {key}: {out}\n' + f'Error: {e}' + ) from e + self._delta_inputs.pop(key) + return init + + def align_pre(self, dyn: Union[ParamDescriber[T], T]) -> T: + """ + Registers a dynamics module to execute after this module. + + This method establishes a sequential execution relationship where the specified + dynamics module will be called after this module completes its update. This + creates a feed-forward connection in the computational graph. + + Parameters + ---------- + dyn : Union[ParamDescriber[T], T] + The dynamics module to be executed after this module. Can be either: + - An instance of Dynamics + - A ParamDescriber that can instantiate a Dynamics object + + Returns + ------- + T + The dynamics module that was registered, allowing for method chaining. + + Raises + ------ + TypeError + If the input is not a Dynamics instance or a ParamDescriber that creates + a Dynamics instance. + + Examples + -------- + >>> import brainstate + >>> n1 = brainstate.nn.LIF(10) + >>> n1.align_pre(brainstate.nn.Expon.desc(n1.varshape)) # n2 will run after n1 + """ + if isinstance(dyn, Dynamics): + self._add_after_update(id(dyn), dyn) + return dyn + elif isinstance(dyn, ParamDescriber): + if not issubclass(dyn.cls, Dynamics): + raise TypeError(f'The input {dyn} should be an instance of {Dynamics}.') + if not self._has_after_update(dyn.identifier): + self._add_after_update( + dyn.identifier, + dyn() if ('in_size' in dyn.kwargs or len(dyn.args) > 0) else dyn(in_size=self.varshape) + ) + return self._get_after_update(dyn.identifier) + else: + raise TypeError(f'The input {dyn} should be an instance of {Dynamics} or a delayed initializer.') + + +class Neuron(Dynamics): r""" Base class for all spiking neuron models. @@ -107,7 +504,7 @@ class Neuron(brainstate.nn.Dynamics): >>> import braintools >>> import brainpy >>> - >>> class SimpleNeuron(brainpy.Neuron): + >>> class SimpleNeuron(brainpy.state.Neuron): ... def __init__(self, in_size, V_th=1.0*u.mV, **kwargs): ... super().__init__(in_size, **kwargs) ... self.V_th = V_th @@ -145,7 +542,7 @@ class Neuron(brainstate.nn.Dynamics): >>> import brainunit as u >>> >>> # Create a LIF neuron layer - >>> neuron = brainpy.LIF( + >>> neuron = brainpy.state.LIF( ... in_size=100, ... tau=10*u.ms, ... V_th=1.0*u.mV, @@ -174,11 +571,11 @@ class Neuron(brainstate.nn.Dynamics): >>> class SpikingNet(brainstate.nn.Module): ... def __init__(self): ... super().__init__() - ... self.layer1 = brainpy.LIF(784, tau=5*u.ms) + ... self.layer1 = brainpy.state.LIF(784, tau=5*u.ms) ... self.fc1 = brainstate.nn.Linear(784, 256) - ... self.layer2 = brainpy.ALIF(256, tau=10*u.ms, tau_a=200*u.ms) + ... self.layer2 = brainpy.state.ALIF(256, tau=10*u.ms, tau_a=200*u.ms) ... self.fc2 = brainstate.nn.Linear(256, 10) - ... self.layer3 = brainpy.LIF(10, tau=8*u.ms) + ... self.layer3 = brainpy.state.LIF(10, tau=8*u.ms) ... ... def __call__(self, x): ... spikes1 = self.layer1.update(x) @@ -239,7 +636,7 @@ def get_spike(self, *args, **kwargs): raise NotImplementedError -class Synapse(brainstate.nn.Dynamics): +class Synapse(Dynamics): r""" Base class for synapse dynamics. @@ -314,12 +711,12 @@ class Synapse(brainstate.nn.Dynamics): .. code-block:: python - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> import braintools >>> - >>> class SimpleSynapse(brainpy.Synapse): + >>> class SimpleSynapse(brainpy.state.Synapse): ... def __init__(self, in_size, tau=5.0*u.ms, **kwargs): ... super().__init__(in_size, **kwargs) ... self.tau = braintools.init.param(tau, self.varshape) @@ -343,13 +740,13 @@ class Synapse(brainstate.nn.Dynamics): .. code-block:: python - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> import jax >>> >>> # Create an exponential synapse - >>> synapse = brainpy.Expon(in_size=100, tau=8.0*u.ms) + >>> synapse = brainpy.state.Expon(in_size=100, tau=8.0*u.ms) >>> >>> # Initialize state >>> synapse.init_state(batch_size=32) @@ -368,7 +765,7 @@ class Synapse(brainstate.nn.Dynamics): .. code-block:: python - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> @@ -376,18 +773,18 @@ class Synapse(brainstate.nn.Dynamics): ... def __init__(self): ... super().__init__() ... # Input layer - ... self.input_neurons = brainpy.LIF(784, tau=5*u.ms) + ... self.input_neurons = brainpy.state.LIF(784, tau=5*u.ms) ... # First hidden layer with synaptic filtering ... self.fc1 = brainstate.nn.Linear(784, 256) - ... self.syn1 = brainpy.Expon(256, tau=8*u.ms) - ... self.hidden1 = brainpy.LIF(256, tau=10*u.ms) + ... self.syn1 = brainpy.state.Expon(256, tau=8*u.ms) + ... self.hidden1 = brainpy.state.LIF(256, tau=10*u.ms) ... # Second hidden layer with AMPA synapse ... self.fc2 = brainstate.nn.Linear(256, 128) - ... self.syn2 = brainpy.AMPA(128) - ... self.hidden2 = brainpy.LIF(128, tau=10*u.ms) + ... self.syn2 = brainpy.state.AMPA(128) + ... self.hidden2 = brainpy.state.LIF(128, tau=10*u.ms) ... # Output layer ... self.fc3 = brainstate.nn.Linear(128, 10) - ... self.output_neurons = brainpy.LIF(10, tau=8*u.ms) + ... self.output_neurons = brainpy.state.LIF(10, tau=8*u.ms) ... ... def __call__(self, x): ... # Input layer @@ -409,7 +806,7 @@ class Synapse(brainstate.nn.Dynamics): .. code-block:: python - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> @@ -417,11 +814,11 @@ class Synapse(brainstate.nn.Dynamics): ... def __init__(self, n_exc=800, n_inh=200): ... super().__init__() ... # Excitatory population - ... self.exc_neurons = brainpy.LIF(n_exc, tau=10*u.ms) - ... self.exc_syn = brainpy.AMPA(n_exc) + ... self.exc_neurons = brainpy.state.LIF(n_exc, tau=10*u.ms) + ... self.exc_syn = brainpy.state.AMPA(n_exc) ... # Inhibitory population - ... self.inh_neurons = brainpy.LIF(n_inh, tau=8*u.ms) - ... self.inh_syn = brainpy.GABAa(n_inh) + ... self.inh_neurons = brainpy.state.LIF(n_inh, tau=8*u.ms) + ... self.inh_syn = brainpy.state.GABAa(n_inh) ... # Connectivity ... self.exc_to_exc = brainstate.nn.Linear(n_exc, n_exc) ... self.exc_to_inh = brainstate.nn.Linear(n_exc, n_inh) diff --git a/brainpy/state/_projection.py b/brainpy/state/_projection.py index f716dc67..5a47d510 100644 --- a/brainpy/state/_projection.py +++ b/brainpy/state/_projection.py @@ -24,6 +24,7 @@ from brainstate.util import get_unique_name from brainpy.mixin import BindCondData, AlignPost +from ._base import Dynamics from ._synouts import SynOut __all__ = [ @@ -116,28 +117,28 @@ def get_post_repr(label, syn, out): def align_post_add_bef_update( syn_desc: ParamDescriber[AlignPost], out_desc: ParamDescriber[BindCondData], - post: brainstate.nn.Dynamics, + post: Dynamics, proj_name: str, label: str, ): # synapse and output initialization _post_repr = get_post_repr(label, syn_desc, out_desc) - if not post._has_before_update(_post_repr): + if not post.has_before_update(_post_repr): syn_cls = syn_desc() out_cls = out_desc() # synapse and output initialization post.add_current_input(proj_name, out_cls, label=label) - post._add_before_update(_post_repr, _AlignPost(syn_cls, out_cls)) - syn = post._get_before_update(_post_repr).syn - out = post._get_before_update(_post_repr).out + post.add_before_update(_post_repr, _AlignPost(syn_cls, out_cls)) + syn = post.get_before_update(_post_repr).syn + out = post.get_before_update(_post_repr).out return syn, out class _AlignPost(brainstate.nn.Module): def __init__( self, - syn: brainstate.nn.Dynamics, + syn: Dynamics, out: BindCondData ): super().__init__() @@ -188,7 +189,7 @@ def __init__( comm: Callable, syn: Union[ParamDescriber[AlignPost], AlignPost], out: Union[ParamDescriber[SynOut], SynOut], - post: brainstate.nn.Dynamics, + post: Dynamics, label: Optional[str] = None, ): super().__init__(name=get_unique_name(self.__class__.__name__)) @@ -229,9 +230,9 @@ def __init__( self.merging = merging # checking post model - if not is_instance(post, brainstate.nn.Dynamics): + if not is_instance(post, Dynamics): raise TypeError( - f'The post should be an instance of {brainstate.nn.Dynamics}, but got {post}.' + f'The post should be an instance of {Dynamics}, but got {post}.' ) if merging: @@ -246,9 +247,9 @@ def __init__( # references self.comm = comm - self.syn: JointTypes[brainstate.nn.Dynamics, AlignPost] = syn + self.syn: JointTypes[Dynamics, AlignPost] = syn self.out: BindCondData = out - self.post: brainstate.nn.Dynamics = post + self.post: Dynamics = post @brainstate.nn.call_order(2) def init_state(self, *args, **kwargs): @@ -308,7 +309,7 @@ def __init__( self, *prefetch, comm: Callable, - post: brainstate.nn.Dynamics, + post: Dynamics, label=None, ): super().__init__(name=get_unique_name(self.__class__.__name__)) @@ -326,9 +327,9 @@ def __init__( self.comm = comm # post model - if not isinstance(post, brainstate.nn.Dynamics): + if not isinstance(post, Dynamics): raise TypeError( - f'The post should be an instance of {brainstate.nn.Dynamics}, but got {post}.' + f'The post should be an instance of {Dynamics}, but got {post}.' ) self.post = post @@ -386,7 +387,7 @@ def __init__( *prefetch, comm: Callable, out: SynOut, - post: brainstate.nn.Dynamics, + post: Dynamics, ): super().__init__(name=get_unique_name(self.__class__.__name__)) @@ -407,7 +408,7 @@ def __init__( self.out = out # check post - if not isinstance(post, brainstate.nn.Dynamics): + if not isinstance(post, Dynamics): raise TypeError(f'The post should be a Dynamics, but got {post}.') self.post = post post.add_current_input(self.name, out) @@ -442,11 +443,11 @@ class align_pre_projection(Projection): def __init__( self, *spike_generator, - syn: brainstate.nn.Dynamics, + syn: Dynamics, comm: Callable, out: SynOut, - post: brainstate.nn.Dynamics, - stp: brainstate.nn.Dynamics = None, + post: Dynamics, + stp: Dynamics = None, ): super().__init__() @@ -492,8 +493,8 @@ def __init__( comm: Callable, syn: Union[AlignPost, ParamDescriber[AlignPost]], out: Union[SynOut, ParamDescriber[SynOut]], - post: brainstate.nn.Dynamics, - stp: brainstate.nn.Dynamics = None, + post: Dynamics, + stp: Dynamics = None, ): super().__init__() diff --git a/docs_state/checkpointing-en.ipynb b/docs_state/checkpointing-en.ipynb deleted file mode 100644 index 6968d398..00000000 --- a/docs_state/checkpointing-en.ipynb +++ /dev/null @@ -1,675 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dbdef1f7bce3a135", - "metadata": { - "collapsed": false - }, - "source": [ - "# Save and Load Checkpoints" - ] - }, - { - "cell_type": "markdown", - "id": "43f961f5", - "metadata": {}, - "source": [ - "In this tutorial, we will explore how to save and load checkpoints in `brainstate` by using the `orbax` library and `braintools` library which provide a more lightweight approach. This is particularly useful for saving the state of your model during training so that you can resume training from where you left off or use the trained model for inference later. The following example demonstrates how to use `orbax` and `braintools`'s checkpointing functionality with a simple MLP model." - ] - }, - { - "cell_type": "markdown", - "id": "343e09cf", - "metadata": {}, - "source": [ - "First you can install the `orbax` library by running the following command:\n", - "\n", - "`pip install orbax-checkpoint`\n", - "\n", - "You may also install directly from GitHub, using the following command. This can be used to obtain the most recent version of Orbax.\n", - "\n", - "`pip install 'git+https://github.com/google/orbax/#subdirectory=checkpoint'`\n", - "\n", - "You can install the `braintools` library by running the following command:\n", - "\n", - "`pip install braintools`" - ] - }, - { - "cell_type": "markdown", - "id": "ee756112", - "metadata": {}, - "source": [ - "First, let's import the necessary libraries." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b7741c32", - "metadata": {}, - "outputs": [], - "source": [ - "import tempfile\n", - "import os\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import orbax.checkpoint as orbax\n", - "import braintools\n", - "\n", - "import brainstate " - ] - }, - { - "cell_type": "markdown", - "id": "a6eb2d76", - "metadata": {}, - "source": [ - "## Define the Model\n", - "We define a simple Multi-Layer Perceptron (MLP) model using `brainstate`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7e020098", - "metadata": {}, - "outputs": [], - "source": [ - "class MLP(brainstate.nn.Module):\n", - " def __init__(self, din: int, dmid: int, dout: int):\n", - " super().__init__()\n", - " self.dense1 = brainstate.nn.Linear(din, dmid)\n", - " self.dense2 = brainstate.nn.Linear(dmid, dout)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " x = self.dense1(x)\n", - " x = jax.nn.relu(x)\n", - " x = self.dense2(x)\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "id": "e5bf157c", - "metadata": {}, - "source": [ - "## Create the Model\n", - "We create an instance of the model with a given seed for reproducibility." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "39619169", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MLP(\n", - " dense1=Linear(\n", - " in_size=(10,),\n", - " out_size=(20,),\n", - " w_mask=None,\n", - " weight=ParamState(\n", - " value={'weight': Array([[ 0.74939334, 0.3148138 , 0.60089725, -0.7131149 , 0.6790908 ,\n", - 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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)}\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SEED = 42\n", - "brainstate.random.seed(SEED) # set seed in brainstate\n", - "model1 = MLP(10, 20, 30) # create model\n", - "model1" - ] - }, - { - "cell_type": "markdown", - "id": "26ded981", - "metadata": {}, - "source": [ - "## Save the Model State\n", - "\n", - "### Save the Model State Using `orbax`\n", - "We save the model's parameters to a checkpoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "14d1d552", - "metadata": {}, - "outputs": [], - "source": [ - "tmpdir = tempfile.mkdtemp() # create temporary directory\n", - "state_tree = brainstate.graph.treefy_states(model1) # convert model to state tree\n", - "checkpointer = orbax.PyTreeCheckpointer() # create checkpointer\n", - "checkpointer.save(os.path.join(tmpdir, 'state'), state_tree) # save state tree" - ] - }, - { - "cell_type": "markdown", - "id": "27209868", - "metadata": {}, - "source": [ - "Now, we've saved the model's parameters to the checkpoint files in `tmpdir/state` by using the `orbax` library." - ] - }, - { - "cell_type": "markdown", - "id": "fb36ffc3", - "metadata": {}, - "source": [ - "### Save the Model State Using `braintools`" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "2b03606b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving checkpoint into C:\\Users\\13107\\AppData\\Local\\Temp\\tmptjdpy0vf\\state.msgpack\n" - ] - } - ], - "source": [ - "checkpoint = brainstate.graph.states(model1).to_nest() # convert model to nest\n", - "braintools.file.msgpack_save(os.path.join(tmpdir, 'state.msgpack'), checkpoint) # save checkpoint" - ] - }, - { - "cell_type": "markdown", - "id": "76030ac1", - "metadata": {}, - "source": [ - "Now, we've saved the model's parameters to the checkpoint files in `tmpdir/state.msgpack` by using the `braintools` library." - ] - }, - { - "cell_type": "markdown", - "id": "6faf01ec", - "metadata": {}, - "source": [ - "## Load the Model State\n", - "\n", - "### Load the Model State Using `orbax`\n", - "Let's load the model's parameters from the checkpoint files." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "26ba3c3e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\routhleck_app\\miniconda\\envs\\brainstate\\lib\\site-packages\\orbax\\checkpoint\\_src\\serialization\\type_handlers.py:1123: UserWarning: Couldn't find sharding info under RestoreArgs. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file instead of directly from RestoreArgs. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# create that model with abstract shapes\n", - "brainstate.random.seed(0)\n", - "model2 = brainstate.augment.abstract_init(lambda: MLP(10, 20, 30))\n", - "state_tree = brainstate.graph.treefy_states(model2)\n", - "\n", - "# Load the parameters from checkpoint files\n", - "checkpointer = orbax.PyTreeCheckpointer()\n", - "state_tree = checkpointer.restore(os.path.join(tmpdir, 'state'), item=state_tree)\n", - "\n", - "# update the model with the loaded state\n", - "brainstate.graph.update_states(model2, state_tree)" - ] - }, - { - "cell_type": "markdown", - "id": "79929f4a", - "metadata": {}, - "source": [ - "### Load the Model State Using `braintools`\n", - "Let's load the model's parameters from the checkpoint files." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7a6d1de0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading checkpoint from C:\\Users\\13107\\AppData\\Local\\Temp\\tmptjdpy0vf\\state.msgpack\n" - ] - }, - { - "data": { - "text/plain": [ - "{'dense1': {'weight': ParamState(\n", - " value={'bias': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0.], dtype=float32), 'weight': array([[ 0.74939334, 0.3148138 , 0.60089725, -0.7131149 , 0.6790908 ,\n", - " -0.44663328, 0.03113358, -0.5250644 , 0.1614144 , -0.39722365,\n", - " -0.23442519, 0.118144 , 0.7669531 , 0.06876656, 0.6045511 ,\n", - " 0.12086334, -0.88447595, -0.19188431, -0.85868365, 0.00500867],\n", - " [ 0.20412642, 0.07092498, 0.37392026, 0.34958398, -0.57214 ,\n", - " 0.71724516, -0.08160591, 0.50068825, -0.17175189, -0.08275215,\n", - " 0.6508336 , 0.28279537, 0.08821856, 0.83949256, 0.49844882,\n", - " -0.04159267, -0.47324428, 0.27084318, -0.58236146, -0.09787997],\n", - " [-0.04382031, -0.20300323, -0.04449642, 0.41578326, 0.5507486 ,\n", - 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" -0.29600585, 0.55229264, 0.26264954, -0.12024187, 0.06554315,\n", - " 0.33039162, -0.4056347 , -0.22326599, -0.20423931, -0.20365807,\n", - " 0.5614395 , -0.33278635, 0.3678192 , -0.38601917, -0.12349749],\n", - " [ 0.21260151, -0.6383393 , -0.04182729, 0.21110533, -0.16549559,\n", - " 0.20241106, 0.42155504, 0.2782736 , -0.5695076 , 0.3197464 ,\n", - " 0.3593777 , 0.15281492, -0.16649725, -0.32258078, -0.19450592,\n", - " -0.5648749 , 0.14112377, -0.08617025, 0.2822599 , 0.65894157,\n", - " 0.06424519, -0.02703291, 0.41351956, -0.06962998, -0.03156902,\n", - " -0.3027034 , -0.15010884, 0.3097132 , -0.01670518, 0.13812247],\n", - " [-0.35231128, -0.06400244, -0.5534636 , 0.08153537, -0.1431605 ,\n", - " 0.19649687, -0.57627857, 0.14731233, -0.5345133 , 0.14830953,\n", - " 0.11090186, -0.5130216 , 0.07951056, 0.042261 , 0.0088584 ,\n", - " 0.0693031 , -0.25705618, 0.07637526, -0.2910843 , 0.26884285,\n", - " -0.3668523 , -0.51732624, 0.32633176, 0.4078384 , 0.07319385,\n", - " 0.24243955, -0.39059573, -0.14434972, -0.20902094, 0.03081408],\n", - " [-0.29074088, -0.340606 , 0.24403909, 0.28382063, 0.57466537,\n", - " 0.24103518, -0.53504395, -0.12040613, -0.21954668, -0.11855581,\n", - " 0.20805535, -0.6497588 , 0.03112273, -0.06355662, 0.22711465,\n", - " -0.00476316, -0.4368407 , -0.26775414, 0.02075309, -0.0473614 ,\n", - " -0.12880138, 0.15983032, 0.18893135, -0.06872427, -0.14535248,\n", - " 0.27104148, -0.31298438, 0.14454837, -0.1837953 , 0.4652801 ]],\n", - " dtype=float32)}\n", - " )}}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 创建一个有着相同结构的模型\n", - "brainstate.random.seed(0)\n", - "model3 = brainstate.augment.abstract_init(lambda: MLP(10, 20, 30))\n", - "checkpoint = brainstate.graph.states(model3).to_nest()\n", - "\n", - "# 从msgpack文件读取模型参数\n", - "braintools.file.msgpack_load(os.path.join(tmpdir, 'state.msgpack'), checkpoint)" - ] - }, - { - "cell_type": "markdown", - "id": "29dc37c9", - "metadata": {}, - "source": [ - "## Demonstrate the Loaded Model\n", - "Let's run the loaded model and check if it produces the same output as the original model." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "dfe032ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "True\n" - ] - } - ], - "source": [ - "y1 = model1(jnp.ones((1, 10)))\n", - "y2 = model2(jnp.ones((1, 10)))\n", - "y3 = model3(jnp.ones((1, 10)))\n", - "print(jnp.allclose(y1, y2)) # True\n", - "print(jnp.allclose(y1, y3)) # True" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainstate", - "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": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs_state/checkpointing-zh.ipynb b/docs_state/checkpointing-zh.ipynb deleted file mode 100644 index cae5420f..00000000 --- a/docs_state/checkpointing-zh.ipynb +++ /dev/null @@ -1,675 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "158db2bb300c7826", - "metadata": { - "collapsed": false - }, - "source": [ - "# 保存和加载检查点" - ] - }, - { - "cell_type": "markdown", - "id": "d775430d", - "metadata": {}, - "source": [ - "在本教程中，我们将探讨如何使用`orbax`库以及`braintools`的轻量级方法在`brainstate`中保存和加载检查点。这对于在训练过程中保存模型状态非常有用，这样您可以从中断的地方继续训练或稍后使用已训练的模型进行推理。以下示例演示了如何将`orbax`和`braintools`的检查点功能与一个简单的多层感知机(MLP)模型结合使用。" - ] - }, - { - "cell_type": "markdown", - "id": "d68c1c72", - "metadata": {}, - "source": [ - "首先，您可以通过运行以下命令安装`orbax`库：\n", - "\n", - "`pip install orbax-checkpoint`\n", - "\n", - "您也可以直接从 GitHub 安装，使用以下命令。这可以用来获取 Orbax 的最新版本。\n", - "\n", - "`pip install 'git+https://github.com/google/orbax/#subdirectory=checkpoint'`\n", - "\n", - "其次，您可以通过运行以下命令安装`braintools`库：\n", - "\n", - "`pip install braintools`" - ] - }, - { - "cell_type": "markdown", - "id": "d9b41392", - "metadata": {}, - "source": [ - "首先，我们将导入所需的库：" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "8142091f", - "metadata": {}, - "outputs": [], - "source": [ - "import tempfile\n", - "import os\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import orbax.checkpoint as orbax\n", - "import braintools\n", - "\n", - "import brainstate" - ] - }, - { - "cell_type": "markdown", - "id": "3dede059", - "metadata": {}, - "source": [ - "## 定义模型\n", - "我们使用`brainstate`来定义一个简单的多层感知机(MLP)模型。" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d090d2e3", - "metadata": {}, - "outputs": [], - "source": [ - "class MLP(brainstate.nn.Module):\n", - " def __init__(self, din: int, dmid: int, dout: int):\n", - " super().__init__()\n", - " self.dense1 = brainstate.nn.Linear(din, dmid)\n", - " self.dense2 = brainstate.nn.Linear(dmid, dout)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " x = self.dense1(x)\n", - " x = jax.nn.relu(x)\n", - " x = self.dense2(x)\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "id": "066cc966", - "metadata": {}, - "source": [ - "## 创建模型\n", - "我们将设置随机数种子来实例化模型。" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "67ca04d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MLP(\n", - " dense1=Linear(\n", - " in_size=(10,),\n", - " out_size=(20,),\n", - " w_mask=None,\n", - " weight=ParamState(\n", - " value={'weight': Array([[ 0.74939334, 0.3148138 , 0.60089725, -0.7131149 , 0.6790908 ,\n", - " -0.44663328, 0.03113358, -0.5250644 , 0.1614144 , -0.39722365,\n", - " -0.23442519, 0.118144 , 0.7669531 , 0.06876656, 0.6045511 ,\n", - " 0.12086334, -0.88447595, -0.19188431, -0.85868365, 0.00500867],\n", - " [ 0.20412642, 0.07092498, 0.37392026, 0.34958398, -0.57214 ,\n", - " 0.71724516, -0.08160591, 0.50068825, -0.17175189, -0.08275215,\n", - " 0.6508336 , 0.28279537, 0.08821856, 0.83949256, 0.49844882,\n", - " -0.04159267, -0.47324428, 0.27084318, -0.58236146, -0.09787997],\n", - " [-0.04382031, -0.20300323, -0.04449642, 0.41578326, 0.5507486 ,\n", - " -0.15913244, -0.8612537 , 0.19072336, -0.16082875, -0.24696219,\n", - 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" -0.00476316, -0.4368407 , -0.26775414, 0.02075309, -0.0473614 ,\n", - " -0.12880138, 0.15983032, 0.18893135, -0.06872427, -0.14535248,\n", - " 0.27104148, -0.31298438, 0.14454837, -0.1837953 , 0.4652801 ]], dtype=float32), 'bias': Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)}\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SEED = 42\n", - "brainstate.random.seed(SEED) # 在brainstate中设置随机种子\n", - "model1 = MLP(10, 20, 30) # 创建模型\n", - "model1" - ] - }, - { - "cell_type": "markdown", - "id": "1d7e2cc4", - "metadata": {}, - "source": [ - "## 保存模型参数\n", - "\n", - "### 使用`orbax`保存检查点\n", - "我们将模型参数保存到检查点文件中。" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c529099d", - "metadata": {}, - "outputs": [], - "source": [ - "tmpdir = tempfile.mkdtemp() # 创建临时目录\n", - "state_tree = brainstate.graph.treefy_states(model1) # 将模型的状态转换为树结构\n", - "checkpointer = orbax.PyTreeCheckpointer() # 创建检查点对象\n", - "checkpointer.save(os.path.join(tmpdir, 'state'), state_tree) # 保存模型的参数" - ] - }, - { - "cell_type": "markdown", - "id": "92448b34", - "metadata": {}, - "source": [ - "现在，我们已经将模型的参数通过`orbax`保存到`tmpdir/state`的检查点文件中。" - ] - }, - { - "cell_type": "markdown", - "id": "a8b032ba", - "metadata": {}, - "source": [ - "### 使用`braintools`保存检查点" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ed7f1c6e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving checkpoint into C:\\Users\\13107\\AppData\\Local\\Temp\\tmp483fc4t1\\state.msgpack\n" - ] - } - ], - "source": [ - "checkpoint = brainstate.graph.states(model1).to_nest() # 将模型的状态转换为nest结构\n", - "braintools.file.msgpack_save(os.path.join(tmpdir, 'state.msgpack'), checkpoint) # 保存模型的参数" - ] - }, - { - "cell_type": "markdown", - "id": "18976368", - "metadata": {}, - "source": [ - "现在，我们已经将模型的参数通过`braintools`保存到`tmpdir/state.msgpack`的检查点文件中。" - ] - }, - { - "cell_type": "markdown", - "id": "2b4033c6", - "metadata": {}, - "source": [ - "## 加载模型参数\n", - "\n", - "### 使用`orbax`加载检查点\n", - "我们将从检查点文件中加载模型的参数。" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "db238309", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\routhleck_app\\miniconda\\envs\\brainstate\\lib\\site-packages\\orbax\\checkpoint\\_src\\serialization\\type_handlers.py:1123: UserWarning: Couldn't find sharding info under RestoreArgs. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file instead of directly from RestoreArgs. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# 创建一个有着相同结构的模型\n", - "brainstate.random.seed(0)\n", - "model2 = brainstate.augment.abstract_init(lambda: MLP(10, 20, 30))\n", - "state_tree = brainstate.graph.treefy_states(model2)\n", - "\n", - "# 从检查点文件读取模型参数\n", - "checkpointer = orbax.PyTreeCheckpointer()\n", - "state_tree = checkpointer.restore(os.path.join(tmpdir, 'state'), item=state_tree)\n", - "\n", - "# 更新模型的状态\n", - "brainstate.graph.update_states(model2, state_tree)" - ] - }, - { - "cell_type": "markdown", - "id": "aa75ceb7", - "metadata": {}, - "source": [ - "### 使用`braintools`加载检查点\n", - "我们将从检查点文件中加载模型的参数。" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b0fbd6f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading checkpoint from C:\\Users\\13107\\AppData\\Local\\Temp\\tmp483fc4t1\\state.msgpack\n" - 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" 0.2710771 , 0.20577058, -0.16118994, 0.03479335, 0.30332327],\n", - " [-0.11540684, -0.21528308, -0.09639532, -0.38324118, 0.08790598,\n", - " -0.05113763, -0.22907412, 0.08176684, -0.13504112, -0.14580515,\n", - " -0.10574839, -0.13816664, 0.25279123, -0.35016036, -0.02811426,\n", - " 0.1878024 , 0.33833987, -0.44787505, 0.05859555, -0.12482259,\n", - " 0.4109398 , -0.3567587 , 0.4436607 , -0.13256377, 0.42250675,\n", - " 0.33017033, 0.28086263, 0.33791474, 0.24015151, -0.23016477],\n", - " [ 0.46682912, -0.63216 , 0.43159592, 0.21971288, -0.07587896,\n", - " -0.25639635, -0.42970398, -0.4962936 , -0.21198583, 0.18351796,\n", - " 0.01911162, -0.3004833 , -0.41785267, -0.04077749, -0.20676233,\n", - " -0.11401828, 0.12992048, 0.03491049, 0.05013497, 0.57222587,\n", - " -0.12001502, -0.17038153, -0.31871405, -0.32121637, 0.66278815,\n", - " 0.61774564, -0.01240813, -0.06011448, 0.29245874, -0.3879291 ],\n", - " [ 0.02741514, 0.31249774, -0.15944321, 0.14222006, 0.611036 ,\n", - " 0.02716783, 0.48367155, -0.59191144, -0.260246 , 0.29856846,\n", - " 0.36217022, 0.26721174, 0.1436277 , 0.2510483 , 0.63455343,\n", - " 0.22804502, 0.21089312, -0.03622444, 0.24770333, 0.12762095,\n", - " -0.11348359, 0.71003526, -0.6399693 , 0.2956937 , -0.40721762,\n", - " 0.07830685, -0.12750737, 0.09320084, -0.37348104, 0.6469367 ],\n", - " [-0.21946031, 0.58491176, 0.6910229 , -0.38729444, -0.22691855,\n", - " 0.09827446, -0.27745098, 0.3286477 , -0.28397417, 0.3331472 ,\n", - " -0.10511833, 0.04856022, 0.6826674 , -0.19410591, -0.03848339,\n", - " 0.2877471 , 0.42053938, -0.3121656 , 0.1115057 , 0.3940428 ,\n", - " 0.22287792, -0.11617415, -0.15520288, -0.17891021, 0.08283449,\n", - " -0.45727572, -0.08755263, -0.30042952, 0.04397725, -0.32858402],\n", - " [-0.04652168, 0.22256051, 0.34796244, -0.57714033, -0.19478762,\n", - " -0.04000793, -0.22230573, -0.1784827 , 0.18552966, 0.3517072 ,\n", - " -0.43350866, 0.3370349 , 0.34543782, -0.25484002, -0.06113737,\n", - " -0.29600585, 0.55229264, 0.26264954, -0.12024187, 0.06554315,\n", - " 0.33039162, -0.4056347 , -0.22326599, -0.20423931, -0.20365807,\n", - " 0.5614395 , -0.33278635, 0.3678192 , -0.38601917, -0.12349749],\n", - " [ 0.21260151, -0.6383393 , -0.04182729, 0.21110533, -0.16549559,\n", - " 0.20241106, 0.42155504, 0.2782736 , -0.5695076 , 0.3197464 ,\n", - " 0.3593777 , 0.15281492, -0.16649725, -0.32258078, -0.19450592,\n", - " -0.5648749 , 0.14112377, -0.08617025, 0.2822599 , 0.65894157,\n", - " 0.06424519, -0.02703291, 0.41351956, -0.06962998, -0.03156902,\n", - " -0.3027034 , -0.15010884, 0.3097132 , -0.01670518, 0.13812247],\n", - " [-0.35231128, -0.06400244, -0.5534636 , 0.08153537, -0.1431605 ,\n", - " 0.19649687, -0.57627857, 0.14731233, -0.5345133 , 0.14830953,\n", - " 0.11090186, -0.5130216 , 0.07951056, 0.042261 , 0.0088584 ,\n", - " 0.0693031 , -0.25705618, 0.07637526, -0.2910843 , 0.26884285,\n", - " -0.3668523 , -0.51732624, 0.32633176, 0.4078384 , 0.07319385,\n", - " 0.24243955, -0.39059573, -0.14434972, -0.20902094, 0.03081408],\n", - " [-0.29074088, -0.340606 , 0.24403909, 0.28382063, 0.57466537,\n", - " 0.24103518, -0.53504395, -0.12040613, -0.21954668, -0.11855581,\n", - " 0.20805535, -0.6497588 , 0.03112273, -0.06355662, 0.22711465,\n", - " -0.00476316, -0.4368407 , -0.26775414, 0.02075309, -0.0473614 ,\n", - " -0.12880138, 0.15983032, 0.18893135, -0.06872427, -0.14535248,\n", - " 0.27104148, -0.31298438, 0.14454837, -0.1837953 , 0.4652801 ]],\n", - " dtype=float32)}\n", - " )}}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 创建一个有着相同结构的模型\n", - "brainstate.random.seed(0)\n", - "model3 = brainstate.augment.abstract_init(lambda: MLP(10, 20, 30))\n", - "checkpoint = brainstate.graph.states(model3).to_nest()\n", - "\n", - "# 从msgpack文件读取模型参数\n", - "braintools.file.msgpack_load(os.path.join(tmpdir, 'state.msgpack'), checkpoint)" - ] - }, - { - "cell_type": "markdown", - "id": "11994171", - "metadata": {}, - "source": [ - "## 验证加载的模型\n", - "让我们运行加载的模型并检查它是否产生与原始模型相同的输出。" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "64810e26", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "True\n" - ] - } - ], - "source": [ - "y1 = model1(jnp.ones((1, 10)))\n", - "y2 = model2(jnp.ones((1, 10)))\n", - "y3 = model3(jnp.ones((1, 10)))\n", - "print(jnp.allclose(y1, y2)) # True\n", - "print(jnp.allclose(y1, y3)) # True" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainstate", - "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": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs_state/snn_simulation-en.ipynb b/docs_state/snn_simulation-en.ipynb deleted file mode 100644 index 244e07ae..00000000 --- a/docs_state/snn_simulation-en.ipynb +++ /dev/null @@ -1,680 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b78f6e1b6cb0dada", - "metadata": { - "collapsed": false - }, - "source": [ - "# Simulating Spiking Neural Networks\n", - "\n", - "Building and simulating brain dynamics models is one of the important methods for studying brain dynamics. In spiking neural network simulations, we specify the model and input parameters, and conduct simulation experiments. During this process, parameter learning and updates (such as synaptic weights) are not involved. The main purpose is to simulate and analyze the designed network.\n", - "\n", - "The spiking neural network models of brain dynamics can be divided into **single neuron models** and **neural network models**. We will demonstrate an example for each of these." - ] - }, - { - "cell_type": "markdown", - "id": "c5622779", - "metadata": {}, - "source": [ - "## Simulation of a Single Neuron Model\n", - "\n", - "The **Hodgkin-Huxley (HH) model** is a mathematical model proposed in 1952 by neurophysiologists Allen Hodgkin (1914-1998) and Andrew Huxley (1917-2012) to describe the generation and propagation of action potentials in neurons. The HH model is based on the classical electrical circuit model and links the dynamic changes of the neuron membrane potential with the biophysical properties of the membrane ion channels. It is one of the most important theoretical models in neuroscience and earned the two researchers the Nobel Prize in Physiology or Medicine in 1963. The mathematical definition of the HH model is:\n", - "\n", - "$$\n", - "\\begin{aligned}C \\frac {dV} {dt} = -(\\bar{g}_{Na} m^3 h (V &-E_{Na}) + \\bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t)\\\\\n", - "\\frac {dx} {dt} &= \\alpha_x (1-x) - \\beta_x, \\quad x\\in {\\rm{\\{m, h, n\\}}}\\\\\n", - "&\\alpha_m(V) = \\frac {0.1(V+40)}{1-\\exp(\\frac{-(V + 40)} {10})}\\\\\n", - "&\\beta_m(V) = 4.0 \\exp(\\frac{-(V + 65)} {18})\\\\\n", - "&\\alpha_h(V) = 0.07 \\exp(\\frac{-(V+65)}{20})\\\\\n", - "&\\beta_h(V) = \\frac 1 {1 + \\exp(\\frac{-(V + 35)} {10})}\\\\\n", - "&\\alpha_n(V) = \\frac {0.01(V+55)}{1-\\exp(-(V+55)/10)}\\\\\n", - "&\\beta_n(V) = 0.125 \\exp(\\frac{-(V + 65)} {80})\\end{aligned}\n", - "$$\n", - "\n", - "In this tutorial, we simulate the HH model as an example of a single neuron model.``brainstate`` can run multiple neuron models in parallel, which saves time. We will simulate a group of HH neurons." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9dd07dd9", - "metadata": {}, - "outputs": [], - "source": [ - "import brainunit as u\n", - "import jax.numpy as jnp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import brainstate" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "051f8f24", - "metadata": {}, - "outputs": [], - "source": "# brainstate.environ.set(platform='gpu')" - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "60058528", - "metadata": {}, - "outputs": [], - "source": "brainstate.random.seed(100)" - }, - { - "cell_type": "markdown", - "id": "a6ba5685", - "metadata": {}, - "source": [ - "## Defining the Single Neuron Model\n", - "\n", - "We can use ``brainstate`` to define custom neuron models. To define a custom neuron model, we need to inherit the base class ``brainstate.nn.Dynamics``.\n", - "\n", - "1. First, define the initialization method ``__init__()``. This method receives the number of neurons running in parallel, ``in_size``, and other model parameters. The base class is initialized with ``in_size``, and the model parameters are set as class attributes for easy access.\n", - "\n", - "2. Then, we can define some common calculations as class methods for later use. Here, we implement functions related to the calculations of m, h, and n. Note that for the drift term function of an ordinary differential equation, the order of the incoming parameters should be, the dynamic variable, the current moment t and the other parameters.\n", - "\n", - "3. Next, define the state initialization method ``init_state()``. Unlike ``__init__()``, this method initializes the model's state, not the model parameters. The state refers to variables that change during the model's operation. In ``brainstate``, all variables that need to change must be encapsulated in a ``State`` object. The hidden state variables, which change during the model's operation, must be encapsulated in a ``HiddenState`` object (a subclass of ``State``).\n", - "\n", - "4. Then, define the method to calculate dV. Similar to the functions for m, h, and n, this method defines some commonly used computations as class methods for easy access. However, in this case, the calculation of dV involves the current I. In this example, the neurons are not connected, but the same process can be used for defining neurons in a network. Therefore, ``I = self.sum_current_inputs(I, V)`` includes both external input currents and currents from other neurons.\n", - "\n", - "5. Finally, define the ``update()`` method, which receives the input for each time step and updates the model variables. ``bst.environ.get('t')`` is used to get the current time t. The ordinary differential equations are solved, and the current values of each variable are obtained using the exponential Euler method ``brainstate.nn.exp_euler_step()`` (where the first argument is the drift term of the ordinary differential equation, and the other arguments are the parameters the equation requires). For neurons in the network, ``V = self.sum_delta_inputs(init=V)`` allows the model to receive inputs from other neurons through delta synaptic transmission. Then, the updated spike information is computed, and the model variables are updated. The output indicates whether the neurons fired an action potential (1 if fired, 0 if not). When using the model, the ``update()`` method is automatically called when the model instance is invoked with input." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f08c361a", - "metadata": {}, - "outputs": [], - "source": [ - "class HH(brainstate.nn.Dynamics):\n", - " def __init__(\n", - " self,\n", - " in_size,\n", - " ENa=50. * u.mV, gNa=120. * u.mS / u.cm ** 2,\n", - " EK=-77. * u.mV, gK=36. * u.mS / u.cm ** 2,\n", - " EL=-54.387 * u.mV, gL=0.03 * u.mS / u.cm ** 2,\n", - " V_th=20. * u.mV,\n", - " C=1.0 * u.uF / u.cm ** 2\n", - " ):\n", - " # Initialization of the neuron model parameters\n", - " super().__init__(in_size)\n", - "\n", - " # Set model parameters based on provided values or defaults\n", - " self.ENa = ENa # Sodium reversal potential (mV)\n", - " self.EK = EK # Potassium reversal potential (mV)\n", - " self.EL = EL # Leak reversal potential (mV)\n", - " self.gNa = gNa # Sodium conductance (mS/cm^2)\n", - " self.gK = gK # Potassium conductance (mS/cm^2)\n", - " self.gL = gL # Leak conductance (mS/cm^2)\n", - " self.C = C # Membrane capacitance (uF/cm^2)\n", - " self.V_th = V_th # Threshold for spike (mV)\n", - "\n", - " # m (sodium activation) channel kinetics\n", - " m_alpha = lambda self, V: 1. / u.math.exprel(-(V / u.mV + 40) / 10) # Alpha function for m\n", - " m_beta = lambda self, V: 4.0 * jnp.exp(-(V / u.mV + 65) / 18) # Beta function for m\n", - " m_inf = lambda self, V: self.m_alpha(V) / (self.m_alpha(V) + self.m_beta(V)) # Steady-state value for m\n", - " dm = lambda self, m, t, V: (self.m_alpha(V) * (1 - m) - self.m_beta(V) * m) / u.ms # Rate of change of m\n", - "\n", - " # h (sodium inactivation) channel kinetics\n", - " h_alpha = lambda self, V: 0.07 * jnp.exp(-(V / u.mV + 65) / 20.) # Alpha function for h\n", - " h_beta = lambda self, V: 1 / (1 + jnp.exp(-(V / u.mV + 35) / 10)) # Beta function for h\n", - " h_inf = lambda self, V: self.h_alpha(V) / (self.h_alpha(V) + self.h_beta(V)) # Steady-state value for h\n", - " dh = lambda self, h, t, V: (self.h_alpha(V) * (1 - h) - self.h_beta(V) * h) / u.ms # Rate of change of h\n", - "\n", - " # n (potassium activation) channel kinetics\n", - " n_alpha = lambda self, V: 0.1 / u.math.exprel(-(V / u.mV + 55) / 10) # Alpha function for n\n", - " n_beta = lambda self, V: 0.125 * jnp.exp(-(V / u.mV + 65) / 80) # Beta function for n\n", - " n_inf = lambda self, V: self.n_alpha(V) / (self.n_alpha(V) + self.n_beta(V)) # Steady-state value for n\n", - " dn = lambda self, n, t, V: (self.n_alpha(V) * (1 - n) - self.n_beta(V) * n) / u.ms # Rate of change of n\n", - "\n", - " def init_state(self, batch_size=None):\n", - " # Initialize the state variables for membrane potential (V) and gating variables (m, h, n)\n", - " self.V = brainstate.HiddenState(\n", - " jnp.ones(self.varshape, brainstate.environ.dftype()) * -65. * u.mV) # Resting potential (mV)\n", - " self.m = brainstate.HiddenState(self.m_inf(self.V.value)) # Sodium activation variable\n", - " self.h = brainstate.HiddenState(self.h_inf(self.V.value)) # Sodium inactivation variable\n", - " self.n = brainstate.HiddenState(self.n_inf(self.V.value)) # Potassium activation variable\n", - "\n", - " def dV(self, V, t, m, h, n, I):\n", - " # Compute the derivative of membrane potential (V) based on the currents and model parameters\n", - " I = self.sum_current_inputs(I, V) # Sum of all incoming currents\n", - " I_Na = (self.gNa * m * m * m * h) * (V - self.ENa) # Sodium current (I_Na)\n", - " n2 = n * n # Squared potassium activation variable\n", - " I_K = (self.gK * n2 * n2) * (V - self.EK) # Potassium current (I_K)\n", - " I_leak = self.gL * (V - self.EL) # Leak current (I_leak)\n", - " dVdt = (- I_Na - I_K - I_leak + I) / self.C # Membrane potential change rate (dV/dt)\n", - " return dVdt\n", - "\n", - " def update(self, x=0. * u.mA / u.cm ** 2):\n", - " # Update the state of the neuron based on current inputs and time\n", - " t = brainstate.environ.get('t') # Retrieve the current time\n", - " V = brainstate.nn.exp_euler_step(self.dV, self.V.value, t, self.m.value, self.h.value, self.n.value,\n", - " x) # Update membrane potential\n", - " m = brainstate.nn.exp_euler_step(self.dm, self.m.value, t, self.V.value) # Update m variable (activation)\n", - " h = brainstate.nn.exp_euler_step(self.dh, self.h.value, t, self.V.value) # Update h variable (inactivation)\n", - " n = brainstate.nn.exp_euler_step(self.dn, self.n.value, t, self.V.value) # Update n variable (activation)\n", - " V = self.sum_delta_inputs(init=V) # Sum the inputs for membrane potential\n", - " spike = jnp.logical_and(self.V.value < self.V_th, V >= self.V_th) # Check if a spike occurs\n", - " self.V.value = V # Update membrane potential\n", - " self.m.value = m # Update m variable\n", - " self.h.value = h # Update h variable\n", - " self.n.value = n # Update n variable\n", - " return spike # Return the spike event (True/False)" - ] - }, - { - "cell_type": "markdown", - "id": "4bd51608", - "metadata": {}, - "source": [ - "## Running the Model Simulation\n", - "\n", - "After instantiating the defined model, we need to initialize the instance with ``bst.nn.init_all_states()``.\n", - "\n", - "Define the model’s time step ``dt``." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "85dca7b9", - "metadata": {}, - "outputs": [], - "source": [ - "hh = HH(10)\n", - "brainstate.nn.init_all_states(hh)\n", - "dt = 0.01 * u.ms" - ] - }, - { - "cell_type": "markdown", - "id": "aab91f0f", - "metadata": {}, - "source": [ - "Define the function ``run()`` for running the model one step at a time.\n", - "\n", - "``with bst.environ.context(t=t, dt=dt):``is used to define environment variables within a code block, and variables can be accessed using ``bst.environ.get()`` (e.g., ``bst.environ.get('t')``). This is necessary because we use ``t = bst.environ.get('t')`` inside the ``update()`` method." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ef0d23e0", - "metadata": {}, - "outputs": [], - "source": [ - "def run(t, inp):\n", - " # Run the simulation for a given time 't' and input current 'inp'\n", - " # `brainstate.environ.context` sets the environment context for this simulation step\n", - " with brainstate.environ.context(t=t, dt=dt):\n", - " hh(inp) # Update the Hodgkin-Huxley model using the input current at time 't'\n", - "\n", - " # Return the membrane potential at the current time step\n", - " return hh.V.value" - ] - }, - { - "cell_type": "markdown", - "id": "3602f2a4", - "metadata": {}, - "source": [ - "Use ``bst.compile.for_loop()`` to iterate the function and run the simulation for a period of time. The first argument is the function to iterate, followed by the parameters the function needs. You can also display a progress bar during the iteration.\n", - "\n", - "This completes the simulation of the single neuron model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "da2ea460", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-12-15 18:46:31.392242: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n", - "2024-12-15 18:46:31.392310: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n", - "2024-12-15 18:46:31.392340: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3e6597ac990046f3b6986029f3c87476", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define the simulation times, from 0 to 100 ms with a time step of 'dt'\n", - "times = u.math.arange(0. * u.ms, 100. * u.ms, dt)\n", - "\n", - "# Run the simulation using `brainstate.transform.for_loop`:\n", - "# - `run` function is called iteratively with each time step and random input current\n", - "# - Random input current between 1 and 10 uA/cm² is generated at each time step\n", - "# - `pbar` is used to show a progress bar during the simulation\n", - "vs = brainstate.transform.for_loop(\n", - " run,\n", - " times, # Time steps as input\n", - " brainstate.random.uniform(1., 10., times.shape) * u.uA / u.cm ** 2, # Random input current (1 to 10 uA/cm²)\n", - " pbar=brainstate.transform.ProgressBar(count=10)\n", - ") # Show progress bar with 10 steps\n", - "\n", - "# Plot the membrane potential over time\n", - "plt.plot(times, vs)\n", - "plt.show() # Display the plot" - ] - }, - { - "cell_type": "markdown", - "id": "c3f5e7c3", - "metadata": {}, - "source": [ - "# Simulation of Spiking Neural Network Models\n", - "\n", - "One of the goals of neuroscience research is to uncover the possible principles by which the brain encodes information. As a potential encoding rule, we naturally expect neurons to produce the same response to the same stimulus. However, in the 1980s and 1990s, numerous experiments found that when the same external stimulus is presented repeatedly, the spike sequences produced by neurons in the cerebral cortex are different each time, and the spike sequences exhibit highly irregular statistical behaviors. Van Vreeswijk and Haim Sompolinsky proposed the **Excitatory-Inhibitory Balanced Network (E-I balanced network)**. They suggested that there should be both excitatory and inhibitory neurons in the network, and the inputs to both types of neurons must be balanced and counteracting. In this case, the mean input received by the neurons remains very small, and the variance (fluctuation) is large enough to induce irregular firing of neurons. Furthermore, the following conditions must also hold for the network:\n", - "+ Neuron connections are random and sparse, which reduces the statistical correlation between the internal inputs received by different neurons, leading to stronger macroscopic irregularity.\n", - "+ Statistically, the excitatory inputs and inhibitory inputs received by a neuron should approximately cancel each other out, meaning that the excitation and inhibition transmitted within the network are balanced.\n", - "+ The connection strength between neurons within the network is relatively strong, so the activity of the entire network is dominated not by external inputs but by synaptic currents generated by the internal network connections. The random fluctuations in synaptic currents determine the irregular firing of neurons.\n", - "\n", - "
\n", - " \"EI-balance\"\n", - "
\n", - "\n", - "Here, we simulate the Excitatory-Inhibitory Balanced Network model as an example of simulating a spiking neural network model.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "60bc3e45", - "metadata": {}, - "outputs": [], - "source": [ - "import brainunit as u\n", - "import brainstate as brainstate\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "2b06bb4b", - "metadata": {}, - "source": [ - "## Defining Spiking Neural Network Model\n", - "\n", - "We can use ``brainstate`` to define custom neuron models. To define a custom neuron model, we need to inherit the base class ``brainstate.nn.DynamicsGroup``.\n", - "\n", - "1. First, define the initialization method ``__init__()``, which receives model parameters and initializes the model. Note that we need to first call ``super().__init__()`` to initialize the base class. The model initialization mainly includes initializing neurons and synapses:\n", - " - **Initializing Neurons**: Neurons in the network can either use the pre-defined neuron models in ``brainstate.nn`` or use the custom neurons defined in the **Single Neuron Model Definition** section.\n", - " - **Initializing Synapses**: Here, we use ``brainstate.nn.AlignPostProj``, which is suitable for the align-post projection model. In the align-post projection, the dimensions of the synaptic variables and the postsynaptic neuron group are the same. The update order of align-post projection models is: action potential → synaptic communication → synaptic dynamics → output. The update order of align-pre projection models is: action potential → synaptic dynamics → synaptic communication → output. Several parameters need to be set:\n", - " - ``comm``: Describes the connections between the neuron groups.\n", - " - ``syn``: Specifies which synapse model is used.\n", - " - ``out``: Indicates whether the output is based on conductance or current.\n", - " - ``post``: Specifies the postsynaptic neuron group.\n", - "\n", - "2. Next, define the ``update()`` method, which receives the input for each time step and updates the model's current state. As a neuron network, neurons need to receive inputs not only from external sources but also from other neurons. Therefore, in this model, we first compute the inputs received from other neurons, then calculate the external inputs. Finally, we output the firing state of each neuron in the entire network." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "61efc944", - "metadata": {}, - "outputs": [], - "source": [ - "class EINet(brainstate.nn.DynamicsGroup):\n", - " def __init__(self, n_exc, n_inh, prob, JE, JI):\n", - " # Initialize the network with the following parameters:\n", - " # - n_exc: number of excitatory neurons\n", - " # - n_inh: number of inhibitory neurons\n", - " # - prob: connection probability between neurons\n", - " # - JE: synaptic weight for excitatory connections\n", - " # - JI: synaptic weight for inhibitory connections\n", - " super().__init__()\n", - "\n", - " self.n_exc = n_exc # Number of excitatory neurons\n", - " self.n_inh = n_inh # Number of inhibitory neurons\n", - " self.num = n_exc + n_inh # Total number of neurons (excitatory + inhibitory)\n", - "\n", - " # Initialize the neurons as LIF (Leaky Integrate-and-Fire) neurons\n", - " self.N = brainstate.nn.LIF(\n", - " n_exc + n_inh, # Total number of neurons\n", - " V_rest=-52. * u.mV, # Resting potential (mV)\n", - " V_th=-50. * u.mV, # Threshold potential for firing (mV)\n", - " V_reset=-60. * u.mV, # Reset potential after spike (mV)\n", - " tau=10. * u.ms, # Membrane time constant (ms)\n", - " V_initializer=brainstate.nn.Normal(-60., 10., unit=u.mV),\n", - " # Initialize membrane potential with a normal distribution\n", - " spk_reset='soft' # Soft reset for spiking (reset without forcing a specific value)\n", - " )\n", - "\n", - " # Synapse connections from excitatory neurons to all neurons\n", - " self.E = brainstate.nn.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, self.num, prob, JE),\n", - " # Fixed probability of synaptic connection with strength JE\n", - " syn=brainstate.nn.Expon.desc(self.num, tau=2. * u.ms), # Exponential decay of synaptic weight\n", - " out=brainstate.nn.CUBA.desc(), # CUBA (Conductance-based) synaptic model\n", - " post=self.N, # Target neurons for these excitatory synapses\n", - " )\n", - "\n", - " # Synapse connections from inhibitory neurons to all neurons\n", - " self.I = brainstate.nn.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, self.num, prob, JI),\n", - " # Fixed probability of synaptic connection with strength JI\n", - " syn=brainstate.nn.Expon.desc(self.num, tau=2. * u.ms), # Exponential decay of synaptic weight\n", - " out=brainstate.nn.CUBA.desc(), # CUBA (Conductance-based) synaptic model\n", - " post=self.N, # Target neurons for these inhibitory synapses\n", - " )\n", - "\n", - " def update(self, inp):\n", - " # Get the spike states of the neurons\n", - " spks = self.N.get_spike() != 0. # Non-zero spikes (spike detection)\n", - "\n", - " # Update the synaptic currents for excitatory and inhibitory neurons\n", - " self.E(spks[:self.n_exc]) # Apply excitatory synaptic input based on the excitatory neuron spikes\n", - " self.I(spks[self.n_exc:]) # Apply inhibitory synaptic input based on the inhibitory neuron spikes\n", - "\n", - " # Update the neurons with the provided input current (inp)\n", - " self.N(inp)\n", - "\n", - " # Return the spike states of the neurons (whether each neuron spiked)\n", - " return self.N.get_spike()" - ] - }, - { - "cell_type": "markdown", - "id": "3320eacc", - "metadata": {}, - "source": [ - "## Running the Simulation Experiment\n", - "\n", - "Set some model parameters. In this example, we use the sign (positive or negative) of the connection strength to set the excitatory or inhibitory nature of the neurons." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3e1569f5", - "metadata": {}, - "outputs": [], - "source": [ - "# connectivity\n", - "num_exc = 500\n", - "num_inh = 500\n", - "prob = 0.1\n", - "# external current\n", - "Ib = 3. * u.mA\n", - "# excitatory and inhibitory synaptic weights\n", - "JE = 1 / u.math.sqrt(prob * num_exc) * u.mS\n", - "JI = -1 / u.math.sqrt(prob * num_inh) * u.mS" - ] - }, - { - "cell_type": "markdown", - "id": "f2c2db9e", - "metadata": {}, - "source": [ - "Define the time step ``dt`` for the simulation.\n", - "\n", - "After instantiating the defined model, initialize the instance with ``bst.nn.init_all_states()``." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3aed3747", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "EINet(\n", - " layers_tuple=(),\n", - " layers_dict={},\n", - " n_exc=500,\n", - " n_inh=500,\n", - " num=1000,\n", - " N=LIF(\n", - " in_size=(1000,),\n", - " out_size=(1000,),\n", - " current_inputs={'AlignPostProj0': CUBA(\n", - " scale=volt\n", - " )},\n", - " before_updates={\"(, (1000,), {'tau': 2. * msecond}) // (, (), {})\": _AlignPost(\n", - " syn=Expon(\n", - " in_size=(1000,),\n", - " out_size=(1000,),\n", - " tau=2. * msecond,\n", - " g_initializer=ZeroInit(\n", - " unit=msiemens\n", - " ),\n", - " g=HiddenState(\n", - " value=ArrayImpl([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0.... * msiemens\n", - " )\n", - " ),\n", - " out=CUBA(...)\n", - " )},\n", - " spk_reset='soft',\n", - " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", - " R=1. * ohm,\n", - " tau=10. * msecond,\n", - " V_th=-50. * mvolt,\n", - " V_rest=-52. * mvolt,\n", - " V_reset=-60. * mvolt,\n", - " V_initializer=Normal(\n", - " scale=10.0,\n", - " mean=-60.0,\n", - " rng=RandomState([2647944946 1939377294]),\n", - " unit=mvolt\n", - " ),\n", - " V=HiddenState(\n", - " value=ArrayImpl([-44.94350815, -49.09746552, -54.77877045, -62.51665115,\n", - " -49.72640991, -53.0278... * mvolt\n", - " )\n", - " ),\n", - " E=AlignPostProj(\n", - " name='AlignPostProj0',\n", - " modules=(),\n", - " merging=True,\n", - " comm=FixedProb(\n", - " in_size=(500,),\n", - " out_size=(1000,),\n", - " n_conn=100,\n", - " float_as_event=True,\n", - " block_size=None,\n", - " indices=Array([[584, 311, 322, ..., 857, 171, 213],\n", - " [502, 87, 501, ..., 176, 239, 808],\n", - " [336, 860, 686, ..., 629, 932, 434],\n", - " ...,\n", - " [838, 631, 745, ..., 767, 427, 536],\n", - " [154, 597, 111, ..., 914, 601, 805],\n", - " [215, 279, 117, ..., 917, 335, 690]], dtype=int32),\n", - " weight=ParamState(\n", - " value=0.14142136 * msiemens\n", - " )\n", - " ),\n", - " syn=Expon(...),\n", - " out=CUBA(...),\n", - " post=LIF(...)\n", - " ),\n", - " I=AlignPostProj(\n", - " name='AlignPostProj1',\n", - " modules=(),\n", - " merging=True,\n", - " comm=FixedProb(\n", - " in_size=(500,),\n", - " out_size=(1000,),\n", - " n_conn=100,\n", - " float_as_event=True,\n", - " block_size=None,\n", - " indices=Array([[257, 901, 935, ..., 722, 965, 139],\n", - " [924, 131, 887, ..., 389, 554, 905],\n", - " [699, 799, 935, ..., 196, 311, 278],\n", - " ...,\n", - " [210, 74, 426, ..., 129, 101, 732],\n", - " [839, 371, 605, ..., 418, 668, 419],\n", - " [924, 822, 688, ..., 137, 877, 855]], dtype=int32),\n", - " weight=ParamState(\n", - " value=-0.14142136 * msiemens\n", - " )\n", - " ),\n", - " syn=Expon(...),\n", - " out=CUBA(...),\n", - " post=LIF(...)\n", - " )\n", - ")" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# network\n", - "brainstate.environ.set(dt=0.1 * u.ms)\n", - "net = EINet(num_exc, num_inh, prob=prob, JE=JE, JI=JI)\n", - "_ = brainstate.nn.init_all_states(net)" - ] - }, - { - "cell_type": "markdown", - "id": "0a52aac5", - "metadata": {}, - "source": [ - "The instantiated network model uses the ``update()`` method to input the current for each time step.\n", - "\n", - "Use ``bst.compile.for_loop()`` to iterate the function and run the simulation for a certain period of time. The first argument is the function to iterate, followed by the parameters that the function requires. You can also display a progress bar during the iteration.\n", - "\n", - "This completes the simulation of the spiking neural network model." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "32e8e8ee", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-12-15 18:46:36.710433: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n", - "2024-12-15 18:46:36.710492: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n", - "2024-12-15 18:46:36.710519: W external/xla/xla/service/gpu/ir_emitter_unnested.cc:1171] Unable to parse backend config for custom call: Could not convert JSON string to proto: : Root element must be a message.\n", - "Fall back to parse the raw backend config str.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "634f59000ce2482bbb528d119d41b4b5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualization\n", - "times = times.to_decimal(u.ms)\n", - "t_indices, n_indices = u.math.where(spikes)\n", - "plt.plot(times[t_indices], n_indices, 'k.', markersize=1)\n", - "plt.xlabel('Time (ms)')\n", - "plt.ylabel('Neuron index')\n", - "plt.show()" - ] - } - ], - "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": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs_state/snn_simulation-zh.ipynb b/docs_state/snn_simulation-zh.ipynb deleted file mode 100644 index c5141c9d..00000000 --- a/docs_state/snn_simulation-zh.ipynb +++ /dev/null @@ -1,723 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b78f6e1b6cb0dada", - "metadata": { - "collapsed": false - }, - "source": [ - "# 仿真脉冲神经网络\n", - "\n", - "建立脑动力学模型并进行仿真是研究脑动力学的重要方法之一。在进行脉冲神经网络仿真时，我们给定模型和输入的各项参数，进行仿真实验。在这个过程中，不涉及参数（如连接权重）的学习与更新。主要应用于对设计好的网络进行仿真与分析。\n", - "\n", - "脑动力学的脉冲神经网络模型可以分为**单个脉冲神经元模型**和**脉冲神经元网络模型**，我们将分别举一个例子进行演示。" - ] - }, - { - "cell_type": "markdown", - "id": "c5622779", - "metadata": {}, - "source": [ - "## 单个脉冲神经元模型的仿真\n", - "\n", - "**Hodgkin-Huxley模型（HH模型）** 是由神经生理学家艾伦·霍奇金（Allen Hodgkin，1914-1998）和安德鲁·赫胥黎（Andrew Huxley，1917-2012）于1952年提出的数学模型，用以描述神经元动作电位的产生和传播过程。HH模型以经典电路模型为基础，将神经元膜电位的动态变化与膜离子通道的生物物理特性联系起来，是神经科学中最重要的理论模型之一，曾为二人赢得1963年的诺贝尔生理学或医学奖。HH模型的数学定义是：\n", - "\n", - "$$\n", - "\\begin{aligned}C \\frac {dV} {dt} = -(\\bar{g}_{Na} m^3 h (V &-E_{Na}) + \\bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t)\\\\\n", - "\\frac {dx} {dt} &= \\alpha_x (1-x) - \\beta_x, \\quad x\\in {\\rm{\\{m, h, n\\}}}\\\\\n", - "&\\alpha_m(V) = \\frac {0.1(V+40)}{1-\\exp(\\frac{-(V + 40)} {10})}\\\\\n", - "&\\beta_m(V) = 4.0 \\exp(\\frac{-(V + 65)} {18})\\\\\n", - "&\\alpha_h(V) = 0.07 \\exp(\\frac{-(V+65)}{20})\\\\\n", - "&\\beta_h(V) = \\frac 1 {1 + \\exp(\\frac{-(V + 35)} {10})}\\\\&\\alpha_n(V) = \\frac {0.01(V+55)}{1-\\exp(-(V+55)/10)}\\\\\n", - "&\\beta_n(V) = 0.125 \\exp(\\frac{-(V + 65)} {80})\\end{aligned}\n", - "$$\n", - "\n", - "在这里我们对HH模型进行仿真，作为单个脉冲神经元模型仿真的示例。``brainstate``可以同时运行多个神经元模型，并行运行节省时间。我们对一群HH神经元进行仿真。" - ] - }, - { - "cell_type": "code", - "id": "9dd07dd9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:18.527525Z", - "start_time": "2025-05-11T02:51:16.423057Z" - } - }, - "source": [ - "import brainunit as u\n", - "import jax.numpy as jnp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import brainstate " - ], - "outputs": [], - "execution_count": 1 - }, - { - "cell_type": "code", - "id": "051f8f24", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:18.542843Z", - "start_time": "2025-05-11T02:51:18.537056Z" - } - }, - "source": "# brainstate.environ.set(platform='gpu')", - "outputs": [], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "60058528", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:18.815210Z", - "start_time": "2025-05-11T02:51:18.567003Z" - } - }, - "source": "brainstate.random.seed(100)", - "outputs": [], - "execution_count": 3 - }, - { - "cell_type": "markdown", - "id": "a6ba5685", - "metadata": {}, - "source": [ - "## 单神经元模型的定义\n", - "\n", - "我们可以使用``brainstate``自定义神经元模型。自定义神经元模型需要继承模型基类``brainstate.nn.Dynamics``。\n", - "\n", - "1. 首先定义初始化类方法``__init__()``，接收并行运行的神经元个数``in_size``，和其他模型参数。用``in_size``初始化基类，将模型参数设置为模型类属性，便于后续调用。\n", - "\n", - "2. 然后，可以设定一些模型常用计算为类方法，便于后续调用。在这里我们实现了一些计算m、h和n涉及的函数。注意常微分方程的漂移项函数，传入参数的顺序应为，动态变量，当前时刻t和其他参数。\n", - "\n", - "3. 接着，定义模型状态初始化方法``init_state()``。与``__init__()``不同，这里初始化的不是模型参数，而是模型状态，主要是模型运行中会改变的变量的初始化。在``brainstate``中，所有需要改变的量都需封装在 ``State`` 对象中。模型运行时会发生改变的隐变量需封装在``HiddenState``（是``State`` 的子类）对象中。\n", - "\n", - "4. 然后定义dV的计算方法。本质上和上文提到的计算m、h和n涉及的函数一样，都是设定一些模型常用计算为类方法，便于后续调用。但需要注意的地方是，dV的计算涉及到电流I。在这个例子中，我们仿真的神经元是相互之间没有连接的，但这套定义单神经元模型的流程也适用于，定义网络中的神经元。因此，``I = self.sum_current_inputs(I, V)``，I包括外界输入电流和来自其他神经元的电流。\n", - "\n", - "5. 最后定义``update()``方法，接收每个时间步模型的input，把模型中各个变量进行更新。``bst.environ.get('t')``获取当前时刻t。解常微分方程，求得每个变量当前时间步的值，这里使用了指数欧拉法``brainstate.nn.exp_euler_step()``求解方程（接收第一个参数是常微分方程的漂移项，其他参数是方程函数需要接收的参数）。对于网络中的神经元，``V = self.sum_delta_inputs(init=V)``使得模型接收其他神经元通过delta突触传导的输入。然后计算这步更新后哪些神经元产生了动作电位。最后用计算出的值更新模型变量的值。返回值输出神经元是否发放了动作电位，有发放为1，无发放为0。使用时，通过调用模型实例并传入输入，会自动调用``update()``方法。" - ] - }, - { - "cell_type": "code", - "id": "f08c361a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:19.416821Z", - "start_time": "2025-05-11T02:51:19.404729Z" - } - }, - "source": [ - "class HH(brainstate.nn.Dynamics):\n", - " def __init__(\n", - " self,\n", - " in_size,\n", - " ENa=50. * u.mV, gNa=120. * u.mS / u.cm ** 2,\n", - " EK=-77. * u.mV, gK=36. * u.mS / u.cm ** 2,\n", - " EL=-54.387 * u.mV, gL=0.03 * u.mS / u.cm ** 2,\n", - " V_th=20. * u.mV,\n", - " C=1.0 * u.uF / u.cm ** 2\n", - " ):\n", - " # Initialization of the neuron model parameters\n", - " super().__init__(in_size)\n", - "\n", - " # Set model parameters based on provided values or defaults\n", - " self.ENa = ENa # Sodium reversal potential (mV)\n", - " self.EK = EK # Potassium reversal potential (mV)\n", - " self.EL = EL # Leak reversal potential (mV)\n", - " self.gNa = gNa # Sodium conductance (mS/cm^2)\n", - " self.gK = gK # Potassium conductance (mS/cm^2)\n", - " self.gL = gL # Leak conductance (mS/cm^2)\n", - " self.C = C # Membrane capacitance (uF/cm^2)\n", - " self.V_th = V_th # Threshold for spike (mV)\n", - "\n", - " # m (sodium activation) channel kinetics\n", - " m_alpha = lambda self, V: 1. / u.math.exprel(-(V / u.mV + 40) / 10) # Alpha function for m\n", - " m_beta = lambda self, V: 4.0 * jnp.exp(-(V / u.mV + 65) / 18) # Beta function for m\n", - " m_inf = lambda self, V: self.m_alpha(V) / (self.m_alpha(V) + self.m_beta(V)) # Steady-state value for m\n", - " dm = lambda self, m, t, V: (self.m_alpha(V) * (1 - m) - self.m_beta(V) * m) / u.ms # Rate of change of m\n", - "\n", - " # h (sodium inactivation) channel kinetics\n", - " h_alpha = lambda self, V: 0.07 * jnp.exp(-(V / u.mV + 65) / 20.) # Alpha function for h\n", - " h_beta = lambda self, V: 1 / (1 + jnp.exp(-(V / u.mV + 35) / 10)) # Beta function for h\n", - " h_inf = lambda self, V: self.h_alpha(V) / (self.h_alpha(V) + self.h_beta(V)) # Steady-state value for h\n", - " dh = lambda self, h, t, V: (self.h_alpha(V) * (1 - h) - self.h_beta(V) * h) / u.ms # Rate of change of h\n", - "\n", - " # n (potassium activation) channel kinetics\n", - " n_alpha = lambda self, V: 0.1 / u.math.exprel(-(V / u.mV + 55) / 10) # Alpha function for n\n", - " n_beta = lambda self, V: 0.125 * jnp.exp(-(V / u.mV + 65) / 80) # Beta function for n\n", - " n_inf = lambda self, V: self.n_alpha(V) / (self.n_alpha(V) + self.n_beta(V)) # Steady-state value for n\n", - " dn = lambda self, n, t, V: (self.n_alpha(V) * (1 - n) - self.n_beta(V) * n) / u.ms # Rate of change of n\n", - "\n", - " def init_state(self, batch_size=None):\n", - " # Initialize the state variables for membrane potential (V) and gating variables (m, h, n)\n", - " self.V = brainstate.HiddenState(jnp.ones(self.varshape, brainstate.environ.dftype()) * -65. * u.mV) # Resting potential (mV)\n", - " self.m = brainstate.HiddenState(self.m_inf(self.V.value)) # Sodium activation variable\n", - " self.h = brainstate.HiddenState(self.h_inf(self.V.value)) # Sodium inactivation variable\n", - " self.n = brainstate.HiddenState(self.n_inf(self.V.value)) # Potassium activation variable\n", - "\n", - " def dV(self, V, t, m, h, n, I):\n", - " # Compute the derivative of membrane potential (V) based on the currents and model parameters\n", - " I = self.sum_current_inputs(I, V) # Sum of all incoming currents\n", - " I_Na = (self.gNa * m * m * m * h) * (V - self.ENa) # Sodium current (I_Na)\n", - " n2 = n * n # Squared potassium activation variable\n", - " I_K = (self.gK * n2 * n2) * (V - self.EK) # Potassium current (I_K)\n", - " I_leak = self.gL * (V - self.EL) # Leak current (I_leak)\n", - " dVdt = (- I_Na - I_K - I_leak + I) / self.C # Membrane potential change rate (dV/dt)\n", - " return dVdt\n", - "\n", - " def update(self, x=0. * u.mA / u.cm ** 2):\n", - " # Update the state of the neuron based on current inputs and time\n", - " t = brainstate.environ.get('t') # Retrieve the current time\n", - " V = brainstate.nn.exp_euler_step(self.dV, self.V.value, t, self.m.value, self.h.value, self.n.value, x) # Update membrane potential\n", - " m = brainstate.nn.exp_euler_step(self.dm, self.m.value, t, self.V.value) # Update m variable (activation)\n", - " h = brainstate.nn.exp_euler_step(self.dh, self.h.value, t, self.V.value) # Update h variable (inactivation)\n", - " n = brainstate.nn.exp_euler_step(self.dn, self.n.value, t, self.V.value) # Update n variable (activation)\n", - " V = self.sum_delta_inputs(init=V) # Sum the inputs for membrane potential\n", - " spike = jnp.logical_and(self.V.value < self.V_th, V >= self.V_th) # Check if a spike occurs\n", - " self.V.value = V # Update membrane potential\n", - " self.m.value = m # Update m variable\n", - " self.h.value = h # Update h variable\n", - " self.n.value = n # Update n variable\n", - " return spike # Return the spike event (True/False)" - ], - "outputs": [], - "execution_count": 4 - }, - { - "cell_type": "markdown", - "id": "4bd51608", - "metadata": {}, - "source": [ - "## 模型仿真实验运行\n", - "\n", - "实例化定义好的模型后，要先``bst.nn.init_all_states()``初始化这个实例。\n", - "\n", - "定义模型``dt``对应的时间。" - ] - }, - { - "cell_type": "code", - "id": "85dca7b9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:19.757507Z", - "start_time": "2025-05-11T02:51:19.474202Z" - } - }, - "source": [ - "hh = HH(10)\n", - "brainstate.nn.init_all_states(hh)\n", - "dt = 0.01 * u.ms" - ], - "outputs": [], - "execution_count": 5 - }, - { - "cell_type": "markdown", - "id": "aab91f0f", - "metadata": {}, - "source": [ - "定义模型单步运行函数``run()``。\n", - "\n", - "``with bst.environ.context(t=t, dt=dt):``可以定义代码块内的环境变量，代码块内都可以通过``bst.environ.get()``获取变量值（eg. ``bst.environ.get('t')``）。在这里需要用到是因为我们定义的模型``update()``方法中使用了``t = bst.environ.get('t')``。" - ] - }, - { - "cell_type": "code", - "id": "ef0d23e0", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:19.795400Z", - "start_time": "2025-05-11T02:51:19.790918Z" - } - }, - "source": [ - "def run(t, inp):\n", - " # Run the simulation for a given time 't' and input current 'inp'\n", - " # `brainstate.environ.context` sets the environment context for this simulation step\n", - " with brainstate.environ.context(t=t, dt=dt):\n", - " hh(inp) # Update the Hodgkin-Huxley model using the input current at time 't'\n", - " \n", - " # Return the membrane potential at the current time step\n", - " return hh.V.value" - ], - "outputs": [], - "execution_count": 6 - }, - { - "cell_type": "markdown", - "id": "3602f2a4", - "metadata": {}, - "source": [ - "使用``bst.compile.for_loop()``迭代运行函数，进行一段时间的仿真，第一个参数是要迭代的函数，随后是此函数所需要的参数。可以选择绘制迭代进度条。\n", - "\n", - "这样就完成了单个脉冲神经元模型的仿真。" - ] - }, - { - "cell_type": "code", - "id": "da2ea460", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:20.472914Z", - "start_time": "2025-05-11T02:51:19.840946Z" - } - }, - "source": [ - "# Define the simulation times, from 0 to 100 ms with a time step of 'dt'\n", - "times = u.math.arange(0. * u.ms, 100. * u.ms, dt)\n", - "\n", - "# Run the simulation using `brainstate.transform.for_loop`:\n", - "# - `run` function is called iteratively with each time step and random input current\n", - "# - Random input current between 1 and 10 uA/cm² is generated at each time step\n", - "# - `pbar` is used to show a progress bar during the simulation\n", - "vs = brainstate.transform.for_loop(run,\n", - " times, # Time steps as input\n", - " brainstate.random.uniform(1., 10., times.shape) * u.uA / u.cm ** 2, # Random input current (1 to 10 uA/cm²)\n", - " pbar=brainstate.transform.ProgressBar(count=10)) # Show progress bar with 10 steps\n", - "\n", - "# Plot the membrane potential over time\n", - "plt.plot(times, vs)\n", - "plt.show() # Display the plot" - ], - "outputs": [ - { - "data": { - "text/plain": [ - " 0%| | 0/10000 [00:00" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 - }, - { - "cell_type": "markdown", - "id": "f4b57d2a", - "metadata": {}, - "source": [ - "## 脉冲神经元网络模型的仿真" - ] - }, - { - "cell_type": "markdown", - "id": "c3f5e7c3", - "metadata": {}, - "source": [ - "神经科学研究的目的之一是要解开大脑编码信息的可能法则。作为一种编码法则，我们很自然指望神经元在相同刺激下产生相同的反应。但上世纪 80 至 90 年代，大量实验发现，同样的外部刺激重复呈现，大脑皮层中的神经元每次产生的脉冲序列都不同，且单次脉冲序列表现出极不规律的统计行为。范·弗雷斯维克（Van Vreeswijk）和海姆·索姆林斯基（Haim Sompolinsky）提出了**兴奋-抑制平衡网络（E-I balanced network）**。他们提出网络中应同时存在兴奋性神经元和抑制性神经元，且两种神经元的输入必须是平衡的、相互抵消的，此时神经元接收到输入的均值维持在一个很小的值，方差（波动）才足够显著，从而促使神经元无规律发放。除此以外，对网络还有以下要求：\n", - "+ 神经元之间的连接是随机且稀疏的，这使得不同神经元接收到的内部输入的统计相关性很小，宏观上表\n", - "现出更强的不规律性；\n", - "+ 统计上，一个神经元接收到的兴奋性输入和抑制性输入应该能大致抵消，即网络中传递的兴奋和抑制是平衡的；\n", - "+ 网络内部神经元之间的连接强度相对较强，这使得整个网络的活动不是被外部输入而是被网络内部突触\n", - "连接产生的电流主导，突触电流的随机起伏决定了神经元的无规律发放。\n", - "\n", - "
\n", - " \"EI-balance\"\n", - "
\n", - "\n", - "在这里我们对兴奋-抑制平衡网络模型进行仿真，作为脉冲神经元网络模型仿真的示例。" - ] - }, - { - "cell_type": "code", - "id": "60bc3e45", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:20.607864Z", - "start_time": "2025-05-11T02:51:20.604365Z" - } - }, - "source": [ - "import brainunit as u\n", - "import brainstate as brainstate\n", - "import matplotlib.pyplot as plt" - ], - "outputs": [], - "execution_count": 8 - }, - { - "cell_type": "markdown", - "id": "2b06bb4b", - "metadata": {}, - "source": [ - "## 脉冲神经网络模型的定义\n", - "\n", - "我们可以使用``brainstate``自定义脉冲神经网络模型。自定义脉冲神经网络模型需要继承模型基类``brainstate.nn.DynamicsGroup``。\n", - "\n", - "1. 首先定义初始化类方法``__init__()``，接收模型参数,初始化模型。注意要先``super().__init__()``初始化基类。初始化模型主要包括初始化神经元和突触:\n", - " - **初始化神经元**：网络中神经元可以选择``brainstate.nn``中已经实现好的各种神经元，也可以选用我们在**单神经元模型的定义**部分自定义的神经元。\n", - " - **初始化突触**：这里使用了``brainstate.nn.AlignPostProj``，适用于align-post投射模型。align-post投射意味着突触变量和突触后神经元群的维度一致。align-post和align-pre模型的更新顺序不同，align-post投射模型更新顺序是动作电位 -> 突触通讯 -> 突触动力学 -> 输出；align-pre投射模型更新顺序是动作电位 -> 突触动力学 -> 突触通讯 -> 输出。它需要设置的几个参数分别是：\n", - " - ``comm``：神经元群之间的连接是怎样的\n", - " - ``syn``：使用哪种突触模型\n", - " - ``out``：设置输出是基于电导还是基于电流的\n", - " - ``post``：指出突触后神经元群。\n", - "\n", - "\n", - "2. 然后定义``update()``方法，接收每个时间步模型的input，更新模型当前状态。作为神经元群，神经元除了外界输入还要接收其它神经元的输入。因此在这个模型中，先计算神经元群接收神经元的输入，再计算接收外界输入。最后输出整个网络每个神经元的发放情况。" - ] - }, - { - "cell_type": "code", - "id": "61efc944", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:20.648750Z", - "start_time": "2025-05-11T02:51:20.641335Z" - } - }, - "source": [ - "class EINet(brainstate.nn.DynamicsGroup):\n", - " def __init__(self, n_exc, n_inh, prob, JE, JI):\n", - " # Initialize the network with the following parameters:\n", - " # - n_exc: number of excitatory neurons\n", - " # - n_inh: number of inhibitory neurons\n", - " # - prob: connection probability between neurons\n", - " # - JE: synaptic weight for excitatory connections\n", - " # - JI: synaptic weight for inhibitory connections\n", - " super().__init__()\n", - "\n", - " self.n_exc = n_exc # Number of excitatory neurons\n", - " self.n_inh = n_inh # Number of inhibitory neurons\n", - " self.num = n_exc + n_inh # Total number of neurons (excitatory + inhibitory)\n", - "\n", - " # Initialize the neurons as LIF (Leaky Integrate-and-Fire) neurons\n", - " self.N = brainstate.nn.LIF(\n", - " n_exc + n_inh, # Total number of neurons\n", - " V_rest=-52. * u.mV, # Resting potential (mV)\n", - " V_th=-50. * u.mV, # Threshold potential for firing (mV)\n", - " V_reset=-60. * u.mV, # Reset potential after spike (mV)\n", - " tau=10. * u.ms, # Membrane time constant (ms)\n", - " V_initializer=brainstate.nn.Normal(-60., 10., unit=u.mV), # Initialize membrane potential with a normal distribution\n", - " spk_reset='soft' # Soft reset for spiking (reset without forcing a specific value)\n", - " )\n", - "\n", - " # Synapse connections from excitatory neurons to all neurons\n", - " self.E = brainstate.nn.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, self.num, prob, JE), # Fixed probability of synaptic connection with strength JE\n", - " syn=brainstate.nn.Expon.desc(self.num, tau=2. * u.ms), # Exponential decay of synaptic weight\n", - " out=brainstate.nn.CUBA.desc(), # CUBA (Conductance-based) synaptic model\n", - " post=self.N, # Target neurons for these excitatory synapses\n", - " )\n", - "\n", - " # Synapse connections from inhibitory neurons to all neurons\n", - " self.I = brainstate.nn.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, self.num, prob, JI), # Fixed probability of synaptic connection with strength JI\n", - " syn=brainstate.nn.Expon.desc(self.num, tau=2. * u.ms), # Exponential decay of synaptic weight\n", - " out=brainstate.nn.CUBA.desc(), # CUBA (Conductance-based) synaptic model\n", - " post=self.N, # Target neurons for these inhibitory synapses\n", - " )\n", - "\n", - " def update(self, inp):\n", - " # Get the spike states of the neurons\n", - " spks = self.N.get_spike() != 0. # Non-zero spikes (spike detection)\n", - "\n", - " # Update the synaptic currents for excitatory and inhibitory neurons\n", - " self.E(spks[:self.n_exc]) # Apply excitatory synaptic input based on the excitatory neuron spikes\n", - " self.I(spks[self.n_exc:]) # Apply inhibitory synaptic input based on the inhibitory neuron spikes\n", - "\n", - " # Update the neurons with the provided input current (inp)\n", - " self.N(inp)\n", - "\n", - " # Return the spike states of the neurons (whether each neuron spiked)\n", - " return self.N.get_spike()" - ], - "outputs": [], - "execution_count": 9 - }, - { - "cell_type": "markdown", - "id": "3320eacc", - "metadata": {}, - "source": [ - "## 模型仿真实验运行\n", - "\n", - "设置一些模型参数。在这个例子中，我们用连接强度的正负来设置神经元的兴奋抑制性。" - ] - }, - { - "cell_type": "code", - "id": "3e1569f5", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:20.704820Z", - "start_time": "2025-05-11T02:51:20.678783Z" - } - }, - "source": [ - "# connectivity\n", - "num_exc = 500\n", - "num_inh = 500\n", - "prob = 0.1\n", - "# external current\n", - "Ib = 3. * u.mA\n", - "# excitatory and inhibitory synaptic weights\n", - "JE = 1 / u.math.sqrt(prob * num_exc) * u.mS\n", - "JI = -1 / u.math.sqrt(prob * num_inh) * u.mS" - ], - "outputs": [], - "execution_count": 10 - }, - { - "cell_type": "markdown", - "id": "f2c2db9e", - "metadata": {}, - "source": [ - "定义仿真实验中dt对应的时间。\n", - "\n", - "实例化定义好的模型后，要先``bst.nn.init_all_states()``初始化这个实例。" - ] - }, - { - "cell_type": "code", - "id": "3aed3747", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:20.892699Z", - "start_time": "2025-05-11T02:51:20.762936Z" - } - }, - "source": [ - "# network\n", - "brainstate.environ.set(dt=0.1 * u.ms)\n", - "net = EINet(num_exc, num_inh, prob=prob, JE=JE, JI=JI)\n", - "brainstate.nn.init_all_states(net)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "EINet(\n", - " layers_tuple=(),\n", - " layers_dict={},\n", - " n_exc=500,\n", - " n_inh=500,\n", - " num=1000,\n", - " N=LIF(\n", - " _name=None,\n", - " in_size=(1000,),\n", - " out_size=(1000,),\n", - " current_inputs={\n", - " 'AlignPostProj0': CUBA(\n", - " _conductance=None,\n", - " scale=volt\n", - " )\n", - " },\n", - " _delta_inputs=None,\n", - " before_updates={\n", - " \"(, (1000,), {'tau': 2. * msecond}) // (, (), {})\": _AlignPost(\n", - " syn=Expon(\n", - " _name=None,\n", - " in_size=(1000,),\n", - " out_size=(1000,),\n", - " _current_inputs=None,\n", - " _delta_inputs=None,\n", - " _before_updates=None,\n", - " _after_updates=None,\n", - " tau=2. * msecond,\n", - " g_initializer=ZeroInit(\n", - " unit=msiemens\n", - " ),\n", - " g=HiddenState(\n", - " value=ShapedArray(float32[1000]) * msiemens\n", - " )\n", - " ),\n", - " out=CUBA(...)\n", - " )\n", - " },\n", - " _after_updates=None,\n", - " spk_reset=soft,\n", - " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", - " R=1. * ohm,\n", - " tau=10. * msecond,\n", - " V_th=-50. * mvolt,\n", - " V_rest=-52. * mvolt,\n", - " V_reset=-60. * mvolt,\n", - " V_initializer=Normal(\n", - " scale=10.0,\n", - " mean=-60.0,\n", - " rng=RandomState([3549270403 1145628597]),\n", - " unit=mvolt\n", - " ),\n", - " V=HiddenState(\n", - " value=ShapedArray(float32[1000]) * mvolt\n", - " )\n", - " ),\n", - " E=AlignPostProj(\n", - " name=AlignPostProj0,\n", - " modules=(),\n", - " merging=True,\n", - " comm=EventFixedNumConn(\n", - " in_size=(500,),\n", - " out_size=(1000,),\n", - " conn_target=post,\n", - " conn_num=100,\n", - " seed=None,\n", - " allow_multi_conn=True,\n", - " weight=ParamState(\n", - " value=ShapedArray(float32[], weak_type=True) * msiemens\n", - " ),\n", - " conn=FixedPostNumConn(float32[500, 1000], nse=50000)\n", - " ),\n", - " syn=Expon(...),\n", - " out=CUBA(...),\n", - " post=LIF(...)\n", - " ),\n", - " I=AlignPostProj(\n", - " name=AlignPostProj1,\n", - " modules=(),\n", - " merging=True,\n", - " comm=EventFixedNumConn(\n", - " in_size=(500,),\n", - " out_size=(1000,),\n", - " conn_target=post,\n", - " conn_num=100,\n", - " seed=None,\n", - " allow_multi_conn=True,\n", - " weight=ParamState(\n", - " value=ShapedArray(float32[], weak_type=True) * msiemens\n", - " ),\n", - " conn=FixedPostNumConn(float32[500, 1000], nse=50000)\n", - " ),\n", - " syn=Expon(...),\n", - " out=CUBA(...),\n", - " post=LIF(...)\n", - " )\n", - ")" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 11 - }, - { - "cell_type": "markdown", - "id": "0a52aac5", - "metadata": {}, - "source": [ - "实例化的网络模型使用``update()``方法输入每步的输入电流。\n", - "\n", - "使用``bst.compile.for_loop()``迭代运行函数，进行一段时间的仿真，第一个参数是要迭代的函数，随后是此函数所需要的参数。可以选择绘制迭代进度条。\n", - "\n", - "这样就完成了脉冲神经元网络模型的仿真。" - ] - }, - { - "cell_type": "code", - "id": "32e8e8ee", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-11T02:51:22.295610Z", - "start_time": "2025-05-11T02:51:20.949200Z" - } - }, - "source": [ - "# Simulation\n", - "# Define the time array from 0 to 1000 ms with a step size of dt\n", - "times = u.math.arange(0. * u.ms, 1000. * u.ms, brainstate.environ.get_dt())\n", - "\n", - "# Run the simulation using `brainstate.transform.for_loop`, iterating over each time step\n", - "# The `lambda t: net.update(Ib)` applies the `update` method of the network `net`\n", - "# for each time step, with `Ib` as the input current at each time step.\n", - "spikes = brainstate.transform.for_loop(\n", - " lambda t: net.update(Ib), # Call net.update with input current Ib\n", - " times, # Time steps\n", - " pbar=brainstate.transform.ProgressBar(10) # Show a progress bar with 10 steps\n", - ")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - " 0%| | 0/10000 [00:00" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 13 - } - ], - "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": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs_state/snn_training-en.ipynb b/docs_state/snn_training-en.ipynb deleted file mode 100644 index c21f22ed..00000000 --- a/docs_state/snn_training-en.ipynb +++ /dev/null @@ -1,709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "de646938e4e80791", - "metadata": { - "collapsed": false - }, - "source": [ - "# Training Spiking Neural Networks" - ] - }, - { - "cell_type": "markdown", - "id": "e863dc68", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In recent years, there has been a surge of interest in training Spiking Neural Networks (SNNs) for meaningful computation. On one hand, this surge is driven by the limited achievements of more traditional, often considered more biologically plausible, learning paradigms in creating functional neural networks that solve interesting computational problems. Deep Neural Networks have undeniably succeeded in solving a variety of challenging computational problems, bridging this limitation. This success has both raised the bar and posed the question of how this progress translates to Spiking Neural Networks.\n", - "\n", - "The rise of deep learning over the past decade is largely attributed to the advancements in GPUs and their computational power, the expansion of training datasets, and perhaps most importantly, the improved understanding of the characteristics and requirements of the backpropagation of error algorithm. For instance, we now know that we must avoid the vanishing and exploding gradient problems, an achievement that can be realized by choosing reasonable nonlinear functions, appropriate weight initialization, and suitable optimizers. Powerful software packages supporting automatic differentiation have made handling deep neural networks easier than ever before. This development raises the question: how much can we learn from deep learning and its tools and apply it to training Spiking Neural Networks? Although these questions cannot be fully answered at present, it seems that we can learn a lot.\n", - "\n", - "In this tutorial, we use [`brainstate`](https://brainstate.readthedocs.io/en/latest/) along with tools from the [Brain Dynamics Programming Ecosystem](https://ecosystem-for-brain-dynamics.readthedocs.io/en/latest/) to build a Spiking Neural Network step by step. To be clear, our goal is to build networks that solve (simple) real-world problems. To this end, we focus on classification problems and use supervised learning in conjunction with the backpropagation algorithm mentioned above. To do so, we must overcome the vanishing gradient problem caused by the binary nature of spikes themselves.\n", - "\n", - "In this tutorial, we will first show how a simple feedforward Leaky Integrate-and-Fire (LIF) neuron-based and conductance-based synaptic Spiking Neural Network (SNN) can be formally mapped to a discrete-time Recurrent Neural Network (RNN). We will leverage this formulation to explain why gradients vanish at the time of spikes and demonstrate a method to mitigate this issue. Specifically, we will introduce surrogate gradients and provide practical examples of how to implement them in Brainstate." - ] - }, - { - "cell_type": "markdown", - "id": "f5e4070f", - "metadata": {}, - "source": [ - "## Mapping LIF Neurons to RNN Dynamics\n", - "\n", - "The de facto standard neuron model in network simulations in computational neuroscience is the Leaky Integrate-and-Fire (LIF) neuron model, which is typically written formally as a time-continuous dynamical system in differential form:\n", - "\n", - "$$\\tau_\\mathrm{mem} \\frac{\\mathrm{d}U_i^{(l)}}{\\mathrm{d}t} = -(U_i^{(l)}-U_\\mathrm{rest}) + RI_i^{(l)}$$\n", - "\n", - "where $U_i$ is the membrane potential of neuron $i$ in layer $l$, $U_\\mathrm{rest}$ is the resting potential, $\\tau_\\mathrm{mem}$ is the membrane time constant, $R$ is the input resistance, and $I_i$ is the input current. The membrane potential $U_i$ characterizes the hidden state of each neuron and, importantly, it is not directly passed to downstream neurons. However, when the membrane voltage of a neuron exceeds a threshold $\\vartheta$, the neuron fires an action potential or spike at time $t$. After firing a spike, the neuron's membrane voltage is reset $U_i \\rightarrow U_\\mathrm{rest}$. We write\n", - "\n", - "$$S_i^{(l)}(t)=\\sum_{k \\in C_i^l} \\delta(t-t_j^k)$$ \n", - "\n", - "to denote the spike train (i.e., the sum of all spikes $C_i^l$ fired by neuron $i$ in layer $l$). Here, $\\delta$ is the Dirac delta function, and $t_i^k$ are the relevant firing times of the neuron.\n", - "\n", - "Spikes propagate along axons and generate postsynaptic currents in connected neurons. Using the above formalism, we can write\n", - "\n", - "$$\\frac{\\mathrm{d}I_i}{\\mathrm{d}t}= -\\frac{I_i(t)}{\\tau_\\mathrm{syn}} + \\sum_j W_{ij} S_j^{(0)}(t) + \\sum_j V_{ij} S_j^{(1)}(t)$$\n", - "\n", - "where we have introduced the synaptic weight matrices $W_{ij}$ (feedforward), $V_{ij}$ (recurrent), and the synaptic decay time constant $\\tau_\\mathrm{syn}$.\n", - "\n", - "To make an explicit connection with RNNs, we now express the above equations in discrete-time form. For brevity, we switch to natural units $U_\\mathrm{rest}=0$, $R=1$, and $\\vartheta=1$. Our argument is not affected by this choice, and all results can be rescaled to physical units. To highlight the nonlinearity of spikes, we first note that we can set\n", - "\n", - "$$S_i^{(l)}(t)=\\Theta(U_i^{(l)}(t)-\\vartheta)$$\n", - "\n", - "where $\\Theta$ denotes the Heaviside step function.\n", - "\n", - "Assuming a small simulation time step $\\Delta_t>0$, we can approximate the synaptic dynamics as follows:\n", - "\n", - "$$I_i^{(l)}(t+1) = \\alpha I_i^{(l)}(t) + \\sum_j W_{ij} S_j^{(l-1)}(t) +\\sum_j V_{ij} S_j^{(l)}(t)$$\n", - "\n", - "where the constant $\\alpha=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{syn}} \\right)$. Additionally, the membrane dynamics can be written as\n", - "\n", - "$$U_i^{(l)}(t+1) = \\underbrace{\\beta U_i^{(l)}(t)}_{\\mathrm{leak}} + \\underbrace{I_i^{(l)}(t)}_{\\mathrm{input}} -\\underbrace{S_i^{(l)}(t)}_{\\mathrm{reset}}$$\n", - "\n", - "where the output $S_i(t) = \\Theta(U_i(t)-1)$ and the constant $\\beta=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{mem}}\\right)$. Note the different terms on the right-hand side of the equation, which are responsible for: i) leakage, ii) synaptic input, and iii) spike reset.\n", - "\n", - "These equations can be succinctly summarized as a computational graph of an RNN with specific connectivity structures.\n", - "\n", - "

\n", - " \"snn_graph\"/\n", - "

\n", - "\n", - "Time flows from left to right. Inputs enter the network at each time step from the bottom of the graph ($S_i^{(0)}$). These inputs successively affect the synaptic currents $I_i^{(1)}$, the membrane potentials $U_i^{(1)}$, and finally the spike outputs $S_i^{(1)}$. Additionally, dynamic quantities are fed directly into future time steps. For clarity, the index $i$ is omitted in the figure.\n", - "\n", - "The computational graph showcases a concept called time unfolding, which emphasizes the duality between deep neural networks and recurrent neural networks, the latter being nothing but deep networks in time (with bound weights). Due to this fact, we can use backpropagation through time (BPTT) to train RNNs. Let us first implement the above dynamics in a three-layer spiking neural network in Brainstate." - ] - }, - { - "cell_type": "markdown", - "id": "ea0355a4", - "metadata": {}, - "source": [ - "## Example of Building and Training a Spiking Neural Network\n", - "\n", - "We start by importing the required libraries." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "efa4ca7d", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "import braintools as bts\n", - "import brainunit as u\n", - "import jax.numpy as jnp\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "import brainstate " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "023064f2", - "metadata": {}, - "outputs": [], - "source": [ - "num_inputs = 100 # Number of input neurons\n", - "num_hidden = 4 # Number of hidden neurons\n", - "num_outputs = 2 # Number of output neurons" - ] - }, - { - "cell_type": "markdown", - "id": "fd204d11", - "metadata": {}, - "source": [ - "As we have seen above, we are technically simulating an RNN. Thus, we have to simulate our neurons for a certain number of timesteps. We will use 1ms timesteps, and we want to simulate our network for 200 timesteps." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1ca98cde", - "metadata": {}, - "outputs": [], - "source": [ - "time_step = 1 * u.ms\n", - "brainstate.environ.set(dt=time_step) # Set the time step for the simulation\n", - "num_steps = 200" - ] - }, - { - "cell_type": "markdown", - "id": "64a3face", - "metadata": {}, - "source": [ - "To take advantage of parallelism, we will set up our code to work on batches of data like this is usually done for neural networks that are trained in a supervised manner.\n", - "To that end, we specify a batch size here." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "50f08663", - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 256" - ] - }, - { - "cell_type": "markdown", - "id": "cfea1a1c", - "metadata": {}, - "source": [ - "With these basic design choices made, we can now start building the actual network." - ] - }, - { - "cell_type": "markdown", - "id": "be04ea73", - "metadata": {}, - "source": [ - "### Defining the Spiking Neural Network (SNN)\n", - "\n", - "- **The class inherits from `bst.nn.DynamicsGroup`**:\n", - " - `bst.nn.DynamicsGroup` is a class that contains dynamic neural network components, used to simulate the dynamical behavior of neurons over time.\n", - " - By inheriting from `DynamicsGroup`, the `SNN` class can leverage the tools provided by the framework to manage state updates and activity simulations of the spiking neural network.\n", - "\n", - "- **`__init__` constructor method**:\n", - " - The `__init__` method initializes the network structure, including connections from the input to the recurrent layer, the spiking neuron model of the recurrent layer, connections from the recurrent layer to the output layer, and the processing units of the output layer.\n", - "\n", - "#### Connections from Input Layer to Recurrent Layer (`self.i2r`)\n", - "\n", - "- **`bst.nn.Sequential`**: Used to sequentially combine multiple layers. This contains two layers: a linear layer and an exponential decay layer.\n", - " \n", - "- **Linear Layer (`bst.nn.Linear`)**:\n", - " - **Function**: Transfers signals from the input layer to the recurrent layer.\n", - " - **Parameters**:\n", - " - `num_in`: Number of neurons in the input layer.\n", - " - `num_rec`: Number of neurons in the recurrent layer.\n", - " - `w_init`: Weight initialization method, using Kaiming Normal initialization, suitable for activation functions like ReLU.\n", - " - `b_init`: Bias initialization, set to zero initialization here.\n", - "\n", - "- **Exponential Decay Layer (`bst.nn.Expon`)**:\n", - " - **Function**: Simulates the exponential decay characteristics of input signals over time, making the input signals more consistent with the dynamical characteristics of biological neurons.\n", - " - **Parameters**:\n", - " - `num_rec`: Specifies the number of neurons in the recurrent layer.\n", - " - `tau`: Time constant, controlling the rate of exponential decay, set to `10 ms` here.\n", - " - `g_initializer`: Initializes the value of parameter `g`, set to 0 here, representing zero initial input current.\n", - "\n", - "#### Recurrent Layer (`self.r`)\n", - "\n", - "- **LIF Neuron Model (`bst.nn.LIF`)**:\n", - " - **Function**: The recurrent layer uses the Leaky Integrate-and-Fire (LIF) neuron model, a widely applied biological neuron model for spiking activity.\n", - " - **Parameters**:\n", - " - `num_rec`: Number of neurons in the recurrent layer.\n", - " - `tau`: Time constant, controlling the rate of potential leakage, set to `20 ms` here.\n", - " - `V_reset`: Reset value of the membrane potential after spiking, set to `0 mV` here.\n", - " - `V_rest`: Resting membrane potential value of the neuron, also `0 mV`.\n", - " - `V_th`: Threshold of the membrane potential, set to `1 mV`, exceeding which the neuron will fire a spike.\n", - " - `spk_fun`: Defines the activation function for spiking, using `bst.surrogate.ReluGrad()` as the approximate derivative method for the spike function.\n", - "\n", - "#### Connections from Recurrent Layer to Output Layer (`self.r2o`)\n", - "\n", - "- **Linear Layer**:\n", - " - **Function**: Transfers output signals from the recurrent layer to the output layer.\n", - " - **Parameters**:\n", - " - `num_rec`: Number of neurons in the recurrent layer.\n", - " - `num_out`: Number of neurons in the output layer.\n", - " - `w_init`: Weight initialization method, also using Kaiming Normal initialization.\n", - "\n", - "#### Output Layer (`self.o`)\n", - "\n", - "- **Exponential Decay Layer**:\n", - " - **Function**: Simulates the decay behavior of output layer signals over time.\n", - " - **Parameters**:\n", - " - `num_out`: Number of neurons in the output layer.\n", - " - `tau`: Time constant for decay, set to `10 ms` here.\n", - " - `g_initializer`: Initial value for output current set to zero." - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "class SNN(brainstate.nn.DynamicsGroup):\n", - " def __init__(self, num_in, num_rec, num_out):\n", - " # Initialize the parent class DynamicsGroup\n", - " super(SNN, self).__init__()\n", - "\n", - " # Parameter definitions\n", - " self.num_in = num_in # Number of neurons in the input layer\n", - " self.num_rec = num_rec # Number of neurons in the recurrent layer\n", - " self.num_out = num_out # Number of neurons in the output layer\n", - "\n", - " # Define connections from the input layer to the recurrent layer (synapse: i->r)\n", - " # Use Sequential to connect the linear layer and the exponential decay layer together\n", - " self.i2r = brainstate.nn.Sequential(\n", - " # Linear layer: used to map input signals to the recurrent layer\n", - " brainstate.nn.Linear(\n", - " num_in, num_rec, # Connections from the input layer to the recurrent layer\n", - " w_init=brainstate.nn.KaimingNormal(scale=7 * (1 - (u.math.exp(-brainstate.environ.get_dt() / (1 * u.ms)))), unit=u.mA), # Use Kaiming Normal initialization for weights\n", - " b_init=brainstate.nn.ZeroInit(unit=u.mA) # Bias initialized to zero\n", - " ),\n", - " # Exponential decay layer: decays the signal over time to match biological neuron dynamics\n", - " brainstate.nn.Expon(num_rec, tau=10. * u.ms, g_initializer=brainstate.nn.Constant(0. * u.mA))\n", - " )\n", - "\n", - " # Define the recurrent layer (r), using the LIF neuron model\n", - " self.r = brainstate.nn.LIF(\n", - " num_rec, # Number of neurons in the recurrent layer\n", - " tau=20 * u.ms, # Time constant, controlling the rate of membrane potential decay\n", - " V_reset=0 * u.mV, # Reset value of the membrane potential\n", - " V_rest=0 * u.mV, # Resting membrane potential\n", - " V_th=1. * u.mV, # Threshold of the membrane potential, exceeding which the neuron fires a spike\n", - " spk_fun=braintools.surrogate.ReluGrad() # Approximate derivative function for spike implementation\n", - " )\n", - "\n", - " # Define connections from the recurrent layer to the output layer (synapse: r->o), using a linear layer\n", - " self.r2o = brainstate.nn.Linear(\n", - " num_rec, num_out, # Connections from the recurrent layer to the output layer\n", - " w_init=brainstate.nn.KaimingNormal() # Use Kaiming Normal initialization for weights\n", - " )\n", - "\n", - " # Define the output layer (o), using an exponential decay layer to simulate the time decay of output signals\n", - " self.o = brainstate.nn.Expon(\n", - " num_out, # Number of neurons in the output layer\n", - " tau=10. * u.ms, # Time constant, controlling the rate of output signal decay\n", - " g_initializer=brainstate.nn.Constant(0.) # Initialize current to zero\n", - " )\n", - "\n", - " # update method: used to perform one update of the network, returning the output of the output layer\n", - " def update(self, spike):\n", - " # Sequentially compute through i2r, r, r2o, and o\n", - " return self.o(self.r2o(self.r(self.i2r(spike))))\n", - "\n", - " # predict method: used to predict and obtain the membrane potential values of the recurrent layer, spike outputs, and final output\n", - " def predict(self, spike):\n", - " # Compute the spike outputs of the recurrent layer\n", - " rec_spikes = self.r(self.i2r(spike))\n", - " # Compute the final output\n", - " out = self.o(self.r2o(rec_spikes))\n", - " # Return the membrane potential values of the recurrent layer, recurrent layer spike outputs, and final output\n", - " return self.r.V.value, rec_spikes, out" - ], - "id": "b1f06d6671e6547" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "net = SNN(num_inputs, num_hidden, num_outputs)", - "id": "fa555b53ff2ebc08" - }, - { - "cell_type": "markdown", - "id": "7b2967cf", - "metadata": {}, - "source": [ - "### Simple Synthetic Dataset\n", - "\n", - "We start by generating some random spiking dataset, which we will use as input to our network. Initially, we will work with a single batch of data.\n", - "\n", - "Suppose we want our network to classify a set of different sparse input spike trains into two categories.\n", - "\n", - "To generate some synthetic data, we fill a tensor of shape (batch_size x num_steps x num_inputs) with random uniform numbers between 0 and 1 and use this to generate our input dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "dba0ea3e", - "metadata": {}, - "outputs": [], - "source": [ - "freq = 5 * u.Hz\n", - "x_data = brainstate.random.rand(num_steps, batch_size, net.num_in) < freq * brainstate.environ.get_dt()\n", - "y_data = u.math.asarray(brainstate.random.rand(batch_size) < 0.5, dtype=int)" - ] - }, - { - "cell_type": "markdown", - "id": "ecd09023", - "metadata": {}, - "source": [ - "Note that there is no structure in the data (because it is entirely random). Thus, we won't worry about generalization now and only care about our ability to overfit these data with the spiking neural network we are going to build in a jiffy." - ] - }, - { - "cell_type": "markdown", - "id": "fab42d61", - "metadata": {}, - "source": [ - "If we plot the spike raster of the first input pattern, this synthetic dataset looks as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4023235e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_id = 0\n", - "plt.imshow(x_data.swapaxes(0, 1)[data_id].transpose(), cmap=plt.cm.gray_r, aspect=\"auto\")\n", - "plt.xlabel(\"Time (ms)\")\n", - "plt.ylabel(\"Unit\")\n", - "sns.despine()" - ] - }, - { - "cell_type": "markdown", - "id": "cb32724d", - "metadata": {}, - "source": [ - "### Visualizing Neuron Membrane Potentials\n", - "\n", - "Define a helper function `plot_voltage_traces` to plot the membrane potentials and spike activities of neurons." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ebb6771f", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_voltage_traces(mem, spk=None, dim=(3, 5), spike_height=5, show=True):\n", - " fig, gs = bts.visualize.get_figure(*dim, 3, 3)\n", - " if spk is not None:\n", - " mem[spk > 0.0] = spike_height\n", - " if isinstance(mem, u.Quantity):\n", - " mem = mem.to_decimal(u.mV)\n", - " for i in range(np.prod(dim)):\n", - " if i == 0:\n", - " a0 = ax = plt.subplot(gs[i])\n", - " else:\n", - " ax = plt.subplot(gs[i], sharey=a0)\n", - " ax.plot(mem[:, i])\n", - " if show:\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d619b651", - "metadata": {}, - "source": [ - "### Testing the Untrained Network Performance\n", - "\n", - "Test the network before training and visualize the changes in membrane potentials using `plot_voltage_traces`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "62367233", - "metadata": {}, - "outputs": [], - "source": [ - "def print_classification_accuracy(output, target):\n", - " \"\"\" Dirty little helper function to compute classification accuracy. \"\"\"\n", - " m = u.math.max(output, axis=0) # max over time\n", - " am = u.math.argmax(m, axis=1) # argmax over output units\n", - " acc = u.math.mean(target == am) # compare to labels\n", - " print(\"Accuracy %.3f\" % acc)\n", - "\n", - "def predict_and_visualize_net_activity(net):\n", - " brainstate.nn.init_all_states(net, batch_size=batch_size)\n", - " vs, spikes, outs = brainstate.transform.for_loop(net.predict, x_data, pbar=brainstate.transform.ProgressBar(10))\n", - " plot_voltage_traces(vs, spikes, spike_height=5 * u.mV, show=False)\n", - " plot_voltage_traces(outs)\n", - " print_classification_accuracy(outs, y_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8d6a6bdb", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0cffa64d16a04a5c8a9e8bbf31ac7681", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy 0.496\n" - ] - } - ], - "source": [ - "predict_and_visualize_net_activity(net)" - ] - }, - { - "cell_type": "markdown", - "id": "a8c2b98d", - "metadata": {}, - "source": [ - "As you can see, our random initialization gives us some sporadic spikes. And calculate the classification accuracy of this random network. We will see that this accuracy is around 50%, as it should be since that corresponds to the chance level of our synthetic task." - ] - }, - { - "cell_type": "markdown", - "id": "757151c6", - "metadata": {}, - "source": [ - "### Defining the Optimizer and Loss Function\n", - "\n", - "Use the Adam optimizer and define the loss function as cross-entropy loss." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "166e0c28", - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = brainstate.optim.Adam(lr=3e-3, beta1=0.9, beta2=0.999)\n", - "optimizer.register_trainable_weights(net.states(brainstate.ParamState))\n", - "\n", - "def loss_fn():\n", - " predictions = brainstate.transform.for_loop(net.update, x_data)\n", - " predictions = u.math.mean(predictions, axis=0)\n", - " return bts.metric.softmax_cross_entropy_with_integer_labels(predictions, y_data).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "22456aa4", - "metadata": {}, - "source": [ - "### Training the Network\n", - "\n", - "Define the training function and train the network." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8e4a0a93", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 100, Loss = 0.5864\n", - "Epoch 200, Loss = 0.5374\n", - "Epoch 300, Loss = 0.5120\n", - "Epoch 400, Loss = 0.4886\n", - "Epoch 500, Loss = 0.4733\n", - "Epoch 600, Loss = 0.4521\n", - "Epoch 700, Loss = 0.4292\n", - "Epoch 800, Loss = 0.4113\n", - "Epoch 900, Loss = 0.3859\n", - "Epoch 1000, Loss = 0.3626\n", - "Epoch 1100, Loss = 0.3427\n", - "Epoch 1200, Loss = 0.3142\n", - "Epoch 1300, Loss = 0.2934\n", - "Epoch 1400, Loss = 0.2753\n", - "Epoch 1500, Loss = 0.2541\n", - "Epoch 1600, Loss = 0.2364\n", - "Epoch 1700, Loss = 0.2169\n", - "Epoch 1800, Loss = 0.2026\n", - "Epoch 1900, Loss = 0.1876\n", - "Epoch 2000, Loss = 0.1705\n", - "Epoch 2100, Loss = 0.1524\n", - "Epoch 2200, Loss = 0.1412\n", - "Epoch 2300, Loss = 0.1283\n", - "Epoch 2400, Loss = 0.1178\n", - "Epoch 2500, Loss = 0.1072\n", - "Epoch 2600, Loss = 0.0983\n", - "Epoch 2700, Loss = 0.0881\n", - "Epoch 2800, Loss = 0.0861\n", - "Epoch 2900, Loss = 0.0772\n", - "Epoch 3000, Loss = 0.0709\n" - ] - } - ], - "source": [ - "@brainstate.transform.jit\n", - "def train_fn():\n", - " brainstate.nn.init_all_states(net, batch_size=batch_size)\n", - " grads, l = brainstate.augment.grad(loss_fn, net.states(brainstate.ParamState), return_value=True)()\n", - " optimizer.update(grads)\n", - " return l\n", - "\n", - "train_losses = []\n", - "for i in range(1, 3001):\n", - " loss = train_fn()\n", - " train_losses.append(loss)\n", - " if i % 100 == 0:\n", - " print(f'Epoch {i}, Loss = {loss:.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "682c4cf0", - "metadata": {}, - "source": [ - "### Visualizing Training Loss" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "98a6781f", - "metadata": {}, - "outputs": [ - { - "data": { - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(np.asarray(jnp.asarray(train_losses)))\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Training Loss\")\n", - "plt.title(\"Training Loss vs Epoch\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f8bcdcdd", - "metadata": {}, - "source": [ - "### Testing Network Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "dfeacdd6", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1b6460c4d972456488da81e00f0684dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy 0.852\n" - ] - } - ], - "source": [ - "predict_and_visualize_net_activity(net)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainpy-dev", - "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": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs_state/snn_training-zh.ipynb b/docs_state/snn_training-zh.ipynb deleted file mode 100644 index 0c03cc46..00000000 --- a/docs_state/snn_training-zh.ipynb +++ /dev/null @@ -1,709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "de646938e4e80791", - "metadata": { - "collapsed": false - }, - "source": [ - "# 训练脉冲神经网络" - ] - }, - { - "cell_type": "markdown", - "id": "576ef80e", - "metadata": {}, - "source": [ - "## 介绍\n", - "近几年，人们对训练脉冲神经网络（SNN）进行有意义的计算的兴趣激增。一方面，这种激增是由于更传统的、通常被认为在生物学上更合理的学习范式在创建解决有趣计算问题的功能神经网络方面取得的有限成就所推动的。深度神经网络在解决各种具有挑战性的计算问题方面取得了不可否认的成功，弥补了这一限制。这一成功既提高了标准，也提出了这一进展如何转化为脉冲神经网络的问题。\n", - "\n", - "过去十年深度学习的兴起在很大程度上归功于GPU及其计算能力的提升、训练数据集的扩大，以及——或许最重要的是——对误差反向传播算法的特点和需求的理解进步。例如，我们现在知道必须避免梯度消失和爆炸问题，这一成就可以通过选择合理的非线性函数、适当的权重初始化和合适的优化器来实现。支持自动微分的强大软件包使得处理深度神经网络变得比以往更加轻松。这一发展提出了一个问题：我们能从深度学习和其工具中获得多少知识，并将其用于训练脉冲神经网络。尽管目前无法完全回答这些问题，但似乎我们可以从中学习很多。\n", - "\n", - "在本教程中，我们使用[`brainstate`](https://brainstate.readthedocs.io/en/latest/)以及[Brain Dynamics Programming Ecosystem](https://ecosystem-for-brain-dynamics.readthedocs.io/en/latest/)中的工具，逐步构建一个脉冲神经网络。明确地说，我们的目标是构建解决（简单）现实世界问题的网络。为此，我们专注于分类问题，并结合上述的反向传播算法使用监督学习。为此，我们必须克服由脉冲本身的二元性质引起的梯度消失问题。\n", - "\n", - "在本教程中，我们将首先展示如何将一个简单的前向传播的基于泄露整合发放（LIF）神经元和基于电导的突触脉冲神经网络（SNN）形式化地映射到离散时间循环神经网络 (RNN)。我们将利用这一公式来解释为什么在脉冲时梯度会消失，并展示一种缓解该问题的方法。具体来说，我们将引入代理梯度，并提供在Brainstate中如何实现它们的实际示例。" - ] - }, - { - "cell_type": "markdown", - "id": "d23db264", - "metadata": {}, - "source": [ - "## 将LIF神经元映射到RNN动力学\n", - "\n", - "计算神经科学中网络模拟的事实上的标准神经元模型是LIF神经元模型，它通常被正式写成微分形式的时间连续动力系统：\n", - "\n", - "$$\\tau_\\mathrm{mem} \\frac{\\mathrm{d}U_i^{(l)}}{\\mathrm{d}t} = -(U_i^{(l)}-U_\\mathrm{rest}) + RI_i^{(l)}$$\n", - "\n", - "其中 $U_i$ 是第 $l$ 层神经元 $i$ 的膜电位，$U_\\mathrm{rest}$ 是静息电位，$\\tau_\\mathrm{mem}$ 是膜时间常数，$R$ 是输入电阻，$I_i$ 是输入电流。膜电位 $U_i$ 表征每个神经元的隐藏状态，并且重要的是，它不会直接传递给下游神经元。然而，当神经元的膜电压超过阈值 $\\vartheta$ 时，神经元会在时间 $t$ 发射动作电位或脉冲。发射脉冲后，神经元的膜电压被重置 $U_i \\rightarrow U_\\mathrm{rest}$。我们写作\n", - "\n", - "$$S_i^{(l)}(t)=\\sum_{k \\in C_i^l} \\delta(t-t_j^k)$$ \n", - "\n", - "表示脉冲序列（即神经元 $i$ 在第 $l$ 层发射的所有脉冲 $C_i^l$ 的总和）。这里 $\\delta$ 是狄拉克δ函数，$t_i^k$ 是神经元的相关发射时间。\n", - "\n", - "脉冲沿着轴突传播并在连接的神经元中产生突触后电流。使用上述形式，我们可以写作\n", - "\n", - "$$\\frac{\\mathrm{d}I_i}{\\mathrm{d}t}= -\\frac{I_i(t)}{\\tau_\\mathrm{syn}} + \\sum_j W_{ij} S_j^{(0)}(t) + \\sum_j V_{ij} S_j^{(1)}(t)$$\n", - "\n", - "其中我们引入了突触权重矩阵 $W_{ij}$（前馈），$V_{ij}$（递归），以及突触衰减时间常数 $\\tau_\\mathrm{syn}$。\n", - "\n", - "为了与RNN明显联系起来，我们现在将上述方程表达为离散时间形式。为了简洁起见，我们切换到自然单位 $U_\\mathrm{rest}=0$，$R=1$，和 $\\vartheta=1$。我们的论点不受此选择的影响，所有结果都可以重新缩放到物理单位。为了突出脉冲的非线性特征，我们首先注意到可以设置\n", - "\n", - "$$S_i^{(l)}(t)=\\Theta(U_i^{(l)}(t)-\\vartheta)$$\n", - "\n", - "其中 $\\Theta$ 表示赫维赛德阶跃函数。\n", - "\n", - "假设一个小的模拟时间步长 $\\Delta_t>0$，我们可以通过以下方式近似突触动力学：\n", - "\n", - "$$I_i^{(l)}(t+1) = \\alpha I_i^{(l)}(t) + \\sum_j W_{ij} S_j^{(l-1)}(t) +\\sum_j V_{ij} S_j^{(l)}(t)$$\n", - "\n", - "其中常数 $\\alpha=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{syn}} \\right)$。此外，膜动力学可以写成\n", - "\n", - "$$U_i^{(l)}(t+1) = \\underbrace{\\beta U_i^{(l)}(t)}_{\\mathrm{leak}} + \\underbrace{I_i^{(l)}(t)}_{\\mathrm{input}} -\\underbrace{S_i^{(l)}(t)}_{\\mathrm{reset}}$$\n", - "\n", - "其中输出 $S_i(t) = \\Theta(U_i(t)-1)$ 和常数 $\\beta=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{mem}}\\right)$。注意方程右侧的不同项，它们分别负责：i) 泄漏，ii) 突触输入，和 iii) 脉冲重置。\n", - "\n", - "这些方程可以简洁地总结为具有特定连接结构的RNN的计算图。\n", - "\n", - "

\n", - " \"snn_graph\"/\n", - "

\n", - "\n", - "\n", - "时间从左到右流动。输入在每个时间步从图的底部进入网络（$S_i^{(0)}$）。这些输入依次影响突触电流 $I_i^{(1)}$，膜电位 $U_i^{(1)}$，最后是脉冲输出 $S_i^{(1)}$。此外，动态量直接输入到未来的时间步。为了清晰起见，在图中省略了索引 $i$。\n", - "\n", - "计算图展示了一个称为时间展开的概念，它强调了深度神经网络和循环神经网络之间的对偶性，后者只不过是时间上的深度网络（具有绑定权重）。由于这一事实，我们可以使用时间反向传播（BPTT）来训练RNN。让我们首先在Brainstate中实现上述动力学的三层脉冲神经网络。" - ] - }, - { - "cell_type": "markdown", - "id": "89169346", - "metadata": {}, - "source": [ - "## 脉冲神经网络构建与训练示例\n", - "\n", - "我们首先导入所需的库。" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5b5143f3", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "import braintools as bts\n", - "import brainunit as u\n", - "import jax.numpy as jnp\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "import brainstate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bce4a582", - "metadata": {}, - "outputs": [], - "source": [ - "num_inputs = 100 # 输入层神经元个数\n", - "num_hidden = 4 # 隐藏层神经元个数\n", - "num_outputs = 2 # 输出层神经元个数" - ] - }, - { - "cell_type": "markdown", - "id": "10251302", - "metadata": {}, - "source": [ - "正如我们上面所看到的，我们实际上是在模拟一个RNN。因此，我们必须为一定数量的时间步长模拟我们的神经元。我们将使用1毫秒的时间步长，并且我们希望模拟我们的网络200个时间步长。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72c128c1", - "metadata": {}, - "outputs": [], - "source": [ - "time_step = 1 * u.ms\n", - "brainstate.environ.set(dt=time_step) # 设置仿真时间步长\n", - "num_steps = 200" - ] - }, - { - "cell_type": "markdown", - "id": "f42b8e71", - "metadata": {}, - "source": [ - "为了利用并行性，我们将设置代码以处理数据批次，就像通常对以监督方式训练的神经网络所做的那样。\n", - "为此，我们在这里指定一个批次大小。" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0592f1f0", - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 256" - ] - }, - { - "cell_type": "markdown", - "id": "7556e61a", - "metadata": {}, - "source": [ - "在做出这些基本设计选择之后，我们现在可以开始构建实际的网络了" - ] - }, - { - "cell_type": "markdown", - "id": "f7ffda5a", - "metadata": {}, - "source": [ - "### 定义脉冲神经网络(SNN)\n", - "\n", - "- **该类继承自`bst.nn.DynamicsGroup`**：\n", - " - `bst.nn.DynamicsGroup` 是一个包含动态神经网络组件的类，用于在时间维度上模拟神经元的动力学行为。\n", - " - 通过继承`DynamicsGroup`，`SNN`类可以利用框架提供的工具来管理脉冲神经网络的状态更新和活动模拟。\n", - "\n", - "- **`__init__`构造方法**：\n", - " - `__init__`方法初始化网络结构，包括输入到递归层的连接、递归层的脉冲神经元模型、递归层到输出层的连接，以及输出层的处理单元。\n", - "\n", - "#### 输入层到递归层的连接 (`self.i2r`)\n", - "\n", - "- **`bst.nn.Sequential`**：用于顺序地组合多个层。这里包含两个层：一个线性层和一个指数衰减层。\n", - " \n", - "- **线性层 (`bst.nn.Linear`)**：\n", - " - 作用：将输入层的信号传递到递归层。\n", - " - 参数：\n", - " - `num_in`: 输入层的神经元数量。\n", - " - `num_rec`: 递归层的神经元数量。\n", - " - `w_init`：权重初始化方法，采用Kaiming Normal初始化，适用于ReLU等激活函数。\n", - " - `b_init`：偏置初始化，这里设为零初始化。\n", - "\n", - "- **指数衰减层 (`bst.nn.Expon`)**：\n", - " - 作用：模拟输入信号随时间的指数衰减特性，使输入信号更符合生物神经元的动力学特性。\n", - " - 参数：\n", - " - `num_rec`: 指定递归层的神经元数量。\n", - " - `tau`: 时间常数，控制指数衰减的速率，这里设置为`10 ms`。\n", - " - `g_initializer`：初始化参数`g`的值，这里设为0，代表初始输入电流为零。\n", - "\n", - "#### 递归层 (`self.r`)\n", - "\n", - "- **LIF神经元模型 (`bst.nn.LIF`)**：\n", - " - 作用：递归层使用脉冲发放的Leaky Integrate-and-Fire（LIF）神经元模型，这是一种广泛应用的生物神经元模型。\n", - " - 参数：\n", - " - `num_rec`: 递归层的神经元数量。\n", - " - `tau`: 时间常数，控制电位的泄露速度，设为`20 ms`。\n", - " - `V_reset`：脉冲发放后膜电位的复位值，这里设为`0 mV`。\n", - " - `V_rest`：神经元静息膜电位值，也是`0 mV`。\n", - " - `V_th`：膜电位的阈值，设为`1 mV`，超过此值神经元会发放脉冲。\n", - " - `spk_fun`：定义脉冲发放的激活函数，这里使用`bst.surrogate.ReluGrad()`作为脉冲函数的近似求导方法。\n", - "\n", - "#### 递归层到输出层的连接 (`self.r2o`)\n", - "\n", - "- **线性层**：\n", - " - 作用：将递归层的输出信号传递到输出层。\n", - " - 参数：\n", - " - `num_rec`: 递归层神经元数。\n", - " - `num_out`: 输出层神经元数。\n", - " - `w_init`：权重初始化方法，同样使用Kaiming Normal初始化。\n", - "\n", - "#### 输出层 (`self.o`)\n", - "\n", - "- **指数衰减层**：\n", - " - 作用：模拟输出层信号随时间的衰减行为。\n", - " - 参数：\n", - " - `num_out`: 输出层神经元数。\n", - " - `tau`: 衰减的时间常数，这里为`10 ms`。\n", - " - `g_initializer`：输出电流初始化值设为零。" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "class SNN(brainstate.nn.DynamicsGroup):\n", - " def __init__(self, num_in, num_rec, num_out):\n", - " # 初始化父类DynamicsGroup\n", - " super(SNN, self).__init__()\n", - "\n", - " # 参数定义\n", - " self.num_in = num_in # 输入层神经元数量\n", - " self.num_rec = num_rec # 递归层神经元数量\n", - " self.num_out = num_out # 输出层神经元数量\n", - "\n", - " # 定义从输入层到递归层的连接（突触: i->r）\n", - " # 使用Sequential将线性层和指数衰减层连接在一起\n", - " self.i2r = brainstate.nn.Sequential(\n", - " # 线性层：用于将输入信号映射到递归层\n", - " brainstate.nn.Linear(\n", - " num_in, num_rec, # 从输入层到递归层的连接数\n", - " w_init=brainstate.nn.KaimingNormal(scale=7 * (1 - (u.math.exp(-brainstate.environ.get_dt() / (1 * u.ms)))), unit=u.mA), # 使用Kaiming Normal初始化权重\n", - " b_init=brainstate.nn.ZeroInit(unit=u.mA) # 偏置初始化为零\n", - " ),\n", - " # 指数衰减层：对信号进行时间上的衰减，使其符合生物神经元动力学\n", - " brainstate.nn.Expon(num_rec, tau=10. * u.ms, g_initializer=brainstate.nn.Constant(0. * u.mA))\n", - " )\n", - "\n", - " # 定义递归层（r），采用LIF神经元模型\n", - " self.r = brainstate.nn.LIF(\n", - " num_rec, # 递归层神经元数量\n", - " tau=20 * u.ms, # 时间常数，控制膜电位衰减速率\n", - " V_reset=0 * u.mV, # 膜电位复位值\n", - " V_rest=0 * u.mV, # 静息膜电位\n", - " V_th=1. * u.mV, # 膜电位阈值，超过此值时神经元发放脉冲\n", - " spk_fun=braintools.surrogate.ReluGrad() # 近似求导函数，用于实现脉冲发放\n", - " )\n", - "\n", - " # 定义从递归层到输出层的连接（突触: r->o），采用线性层\n", - " self.r2o = brainstate.nn.Linear(\n", - " num_rec, num_out, # 从递归层到输出层的连接数\n", - " w_init=brainstate.nn.KaimingNormal() # 使用Kaiming Normal初始化权重\n", - " )\n", - "\n", - " # 定义输出层（o），使用指数衰减层模拟输出信号的时间衰减\n", - " self.o = brainstate.nn.Expon(\n", - " num_out, # 输出层神经元数量\n", - " tau=10. * u.ms, # 时间常数，控制输出信号的衰减速率\n", - " g_initializer=brainstate.nn.Constant(0.) # 初始化电流为零\n", - " )\n", - "\n", - " # update方法：用于执行网络的一次更新，返回输出层的输出\n", - " def update(self, spike):\n", - " # 依次通过 i2r、r、r2o 和 o 计算输出\n", - " return self.o(self.r2o(self.r(self.i2r(spike))))\n", - "\n", - " # predict方法：用于预测并获取递归层的膜电位值、脉冲输出和最终输出\n", - " def predict(self, spike):\n", - " # 计算递归层的脉冲输出\n", - " rec_spikes = self.r(self.i2r(spike))\n", - " # 计算最终输出\n", - " out = self.o(self.r2o(rec_spikes))\n", - " # 返回递归层的膜电位值、递归层脉冲输出和最终输出\n", - " return self.r.V.value, rec_spikes, out" - ], - "id": "6e64b8fa9effc5fa" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "net = SNN(num_inputs, num_hidden, num_outputs)", - "id": "57595eb27e88376b" - }, - { - "cell_type": "markdown", - "id": "b150b27d", - "metadata": {}, - "source": [ - "### 简单的合成数据集\n", - "\n", - "我们首先生成一些随机的脉冲数据集，我们将用它作为网络的输入。最初，我们将使用单个批次的数据。\n", - "\n", - "假设我们希望网络将一组不同的稀疏输入脉冲序列分类为两个类别。\n", - "\n", - "为了生成一些合成数据，我们用0到1之间的随机均匀数填充一个形状为 (batch_size x num_steps x num_inputs) 的张量，并使用它来生成我们的输入数据集：" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "309544fc", - "metadata": {}, - "outputs": [], - "source": [ - "freq = 5 * u.Hz\n", - "x_data = brainstate.random.rand(num_steps, batch_size, net.num_in) < freq * brainstate.environ.get_dt()\n", - "y_data = u.math.asarray(brainstate.random.rand(batch_size) < 0.5, dtype=int)" - ] - }, - { - "cell_type": "markdown", - "id": "ac38ce7e", - "metadata": {}, - "source": [ - "请注意，数据中没有结构（因为它是完全随机的）。因此，我们现在不会担心泛化问题，只关心我们能否用即将构建的脉冲神经网络过度拟合这些数据。" - ] - }, - { - "cell_type": "markdown", - "id": "8a19dbff", - "metadata": {}, - "source": [ - "如果绘制第一个输入模式的脉冲光栅图，这个合成数据集看起来如下所示。" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4ff6cfeb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_id = 0\n", - "plt.imshow(x_data.swapaxes(0, 1)[data_id].transpose(), cmap=plt.cm.gray_r, aspect=\"auto\")\n", - "plt.xlabel(\"Time (ms)\")\n", - "plt.ylabel(\"Unit\")\n", - "sns.despine()" - ] - }, - { - "cell_type": "markdown", - "id": "9ed7f8c4", - "metadata": {}, - "source": [ - "### 可视化神经元膜电位\n", - "\n", - "定义一个辅助函数`plot_voltage_traces`，用于绘制神经元的膜电位和脉冲活动。" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0796f03d", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_voltage_traces(mem, spk=None, dim=(3, 5), spike_height=5, show=True):\n", - " fig, gs = bts.visualize.get_figure(*dim, 3, 3)\n", - " if spk is not None:\n", - " mem[spk > 0.0] = spike_height\n", - " if isinstance(mem, u.Quantity):\n", - " mem = mem.to_decimal(u.mV)\n", - " for i in range(np.prod(dim)):\n", - " if i == 0:\n", - " a0 = ax = plt.subplot(gs[i])\n", - " else:\n", - " ax = plt.subplot(gs[i], sharey=a0)\n", - " ax.plot(mem[:, i])\n", - " if show:\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e31fd03c", - "metadata": {}, - "source": [ - "### 测试未训练的网络性能\n", - "\n", - "在训练前测试网络，并用`plot_voltage_traces`可视化膜电位变化。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd6ed2fb", - "metadata": {}, - "outputs": [], - "source": [ - "def print_classification_accuracy(output, target):\n", - " \"\"\"一个简易的小工具函数，用于计算分类准确率\"\"\"\n", - " m = u.math.max(output, axis=0) # 获取最大值\n", - " am = u.math.argmax(m, axis=1) # 获取最大值的索引\n", - " acc = u.math.mean(target == am) # 与目标值比较\n", - " print(\"准确率 %.3f\" % acc)\n", - "\n", - "def predict_and_visualize_net_activity(net):\n", - " brainstate.nn.init_all_states(net, batch_size=batch_size)\n", - " vs, spikes, outs = brainstate.transform.for_loop(net.predict, x_data, pbar=brainstate.transform.ProgressBar(10))\n", - " plot_voltage_traces(vs, spikes, spike_height=5 * u.mV, show=False)\n", - " plot_voltage_traces(outs)\n", - " print_classification_accuracy(outs, y_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7923aa08", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0b6e7e32acf74ca7a0d6259ba3e97bc2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "准确率 0.488\n" - ] - } - ], - "source": [ - "predict_and_visualize_net_activity(net)" - ] - }, - { - "cell_type": "markdown", - "id": "6c1489e2", - "metadata": {}, - "source": [ - "如您所见，我们的随机初始化给了我们一些零星的脉冲。并且计算出这个随机网络的分类准确率。我们将看到这个准确率大约在50%左右，因为这对应于我们合成任务的随机水平。" - ] - }, - { - "cell_type": "markdown", - "id": "8b6ada2f", - "metadata": {}, - "source": [ - "### 定义优化器和损失函数\n", - "\n", - "使用Adam优化器，并定义损失函数为交叉熵损失。" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3e2f0329", - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = brainstate.optim.Adam(lr=3e-3, beta1=0.9, beta2=0.999)\n", - "optimizer.register_trainable_weights(net.states(brainstate.ParamState))\n", - "\n", - "def loss_fn():\n", - " predictions = brainstate.transform.for_loop(net.update, x_data)\n", - " predictions = u.math.mean(predictions, axis=0)\n", - " return bts.metric.softmax_cross_entropy_with_integer_labels(predictions, y_data).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "9beeb216", - "metadata": {}, - "source": [ - "### 训练网络\n", - "\n", - "定义训练函数并训练网络。" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0f8d8238", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 100, Loss = 0.5674\n", - "Epoch 200, Loss = 0.5032\n", - "Epoch 300, Loss = 0.4646\n", - "Epoch 400, Loss = 0.4378\n", - "Epoch 500, Loss = 0.4075\n", - "Epoch 600, Loss = 0.3852\n", - "Epoch 700, Loss = 0.3579\n", - "Epoch 800, Loss = 0.3289\n", - "Epoch 900, Loss = 0.3047\n", - "Epoch 1000, Loss = 0.2796\n", - "Epoch 1100, Loss = 0.2526\n", - "Epoch 1200, Loss = 0.2323\n", - "Epoch 1300, Loss = 0.2134\n", - "Epoch 1400, Loss = 0.1925\n", - "Epoch 1500, Loss = 0.1792\n", - "Epoch 1600, Loss = 0.1626\n", - "Epoch 1700, Loss = 0.1468\n", - "Epoch 1800, Loss = 0.1360\n", - "Epoch 1900, Loss = 0.1211\n", - "Epoch 2000, Loss = 0.1116\n", - "Epoch 2100, Loss = 0.1035\n", - "Epoch 2200, Loss = 0.0945\n", - "Epoch 2300, Loss = 0.0868\n", - "Epoch 2400, Loss = 0.0770\n", - "Epoch 2500, Loss = 0.0700\n", - "Epoch 2600, Loss = 0.0657\n", - "Epoch 2700, Loss = 0.0593\n", - "Epoch 2800, Loss = 0.0555\n", - "Epoch 2900, Loss = 0.0504\n", - "Epoch 3000, Loss = 0.0465\n" - ] - } - ], - "source": [ - "@brainstate.transform.jit\n", - "def train_fn():\n", - " brainstate.nn.init_all_states(net, batch_size=batch_size)\n", - " grads, l = brainstate.augment.grad(loss_fn, net.states(brainstate.ParamState), return_value=True)()\n", - " optimizer.update(grads)\n", - " return l\n", - "\n", - "train_losses = []\n", - "for i in range(1, 3001):\n", - " loss = train_fn()\n", - " train_losses.append(loss)\n", - " if i % 100 == 0:\n", - " print(f'Epoch {i}, Loss = {loss:.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "a65a495e", - "metadata": {}, - "source": [ - "### 可视化训练损失" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "79b0c8fd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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iInMle7hZuXIlYmNjMW3aNBw+fBitW7dGdHQ0MjIyKuy/Zs0aXL9+XXqcPHkSNjY2eOKJJ0xcOREREZkj2cPNvHnzMHr0aIwaNQrNmjXD4sWL4eDggKVLl1bY38PDAz4+PtJj69atcHBwYLghIiIiADKHm5KSEhw6dAhRUVFSm1KpRFRUFBISEqq1jq+//hpPPfUUHB0dK5xfXFyM3NxcvQcRERFZL1nDTVZWFrRaLby9vfXavb29kZaWds/l9+/fj5MnT+KFF16otE9cXBxcXV2lB2+aSUREZN1kPyz1IL7++mu0bNkSHTp0qLTPpEmTkJOTIz0uX75swgqJiIjI1GS9caanpydsbGyQnp6u156eng4fH58qly0oKMCKFSswc+bMKvup1Wqo1eoHrrU69iRnIcTLCe6OKtjZWHRuJCIisliyfgOrVCqEh4cjPj5eatPpdIiPj0dkZGSVy/7www8oLi7Gs88+a+wyq+XQpZt45qt96PBhPBpN3oic26Vyl0RERFQryb57ITY2FkuWLME333yDxMREjBkzBgUFBRg1ahQAYMSIEZg0aVK55b7++msMHjwYderUMXXJFbJVKuCi+WtH2Pf7U2WshoiIqPaS9bAUAAwdOhSZmZmYOnUq0tLSEBYWhk2bNkmDjFNTU6FU6mewpKQk7Nq1C1u2bJGj5Aq19nfDoSmP4I2VR/Hr8es4knpT7pKIiIhqJYUQQshdhCnl5ubC1dUVOTk5cHFxMfj695zPwjNL9sHfwx5/vP2wwddPRERUG9Xk+1v2w1LWppnvnQ1+Ofs2x90QERHJgOHGwNwcVGjg4QAAOHr5lrzFEBER1UIMN0bQrqE7AOBQSrbMlRAREdU+DDdG0LyeKwAgOTNf5kqIiIhqH4YbIwioc+ew1MWsQpkrISIiqn0YbowgwPPOTTwv3ShALTsZjYiISHYMN0bg7+4ApQIoLNEiM69Y7nKIiIhqFYYbI1DZKlHP3R4AkHKDh6aIiIhMieHGSALq3Dk0lZJVIHMlREREtQvDjZE0/HNQccoNhhsiIiJTYrgxkrt7bi7xsBQREZFJMdwYyd1wc5GHpYiIiEyK4cZIeDo4ERGRPBhujMTfwx52NgoUlGhxPpN7b4iIiEyF4cZI1LY2aPHnbRiS0vJkroaIiKj2YLgxosC7p4PzjCkiIiKTYbgxorvjbnitGyIiItNhuDEiXuuGiIjI9BhujCjw7p4bXuuGiIjIZBhujKjhn2NuMvOKkV9cJnM1REREtQPDjRG52tvBw1EF4M71boiIiMj4GG6MTBp3k8VDU0RERKbAcGNkPB2ciIjItBhujKzBn3tuDl+6KXMlREREtQPDjZHVd78TbuLPZKBMq5O5GiIiIuvHcGNkvZp7S895aIqIiMj4GG6MzEVjh9b+bgCAM7zHFBERkdEx3JhAUx9nAEDi9VyZKyEiIrJ+DDcm0NzPBQBw6hrDDRERkbEx3JhA83quAICTV3MghJC5GiIiIuvGcGMCTX1coFQAWfklyMgrlrscIiIiq8ZwYwL2Khs08roz7uZgCq93Q0REZEwMNybSPtAdALArOUvmSoiIiKwbw42JRATWAcAzpoiIiIyN4cZEmvreOSx19PItXqmYiIjIiBhuTCSgjiNUtnc2929nM2WuhoiIyHox3JiIrY1Sut4ND00REREZD8ONCT0c6gUAuHSjUOZKiIiIrBfDjQk19HQEwHBDRERkTAw3JhT0Z7hJTMuFVscrFRMRERkDw40JNfFxhtpWibyiMqRmc+8NERGRMcgebhYuXIiAgABoNBpERERg//79Vfa/desWxo4dC19fX6jVajRu3BgbNmwwUbUPxtZGieC6TgCAs+l5MldDRERknWQNNytXrkRsbCymTZuGw4cPo3Xr1oiOjkZGRkaF/UtKSvDII48gJSUFq1evRlJSEpYsWYJ69eqZuPL719j7TrhJzsiXuRIiIiLrZCvni8+bNw+jR4/GqFGjAACLFy/G+vXrsXTpUkycOLFc/6VLlyI7Oxt79uyBnZ0dACAgIMCUJT+wRt53LuZ3+hpPByciIjIG2fbclJSU4NChQ4iKivqrGKUSUVFRSEhIqHCZn3/+GZGRkRg7diy8vb3RokULfPjhh9BqtZW+TnFxMXJzc/Uecrp7rZvd57Og46BiIiIig5Mt3GRlZUGr1cLb21uv3dvbG2lpaRUuc+HCBaxevRparRYbNmzAlClT8Mknn+CDDz6o9HXi4uLg6uoqPfz9/Q36PmqqU7AnbJUK3Cos5aBiIiIiI5B9QHFN6HQ6eHl54csvv0R4eDiGDh2KyZMnY/HixZUuM2nSJOTk5EiPy5cvm7Di8lS2f12p+DSvVExERGRwsoUbT09P2NjYID09Xa89PT0dPj4+FS7j6+uLxo0bw8bGRmpr2rQp0tLSUFJSUuEyarUaLi4ueg+5Nfsz3Jy6liNzJURERNZHtnCjUqkQHh6O+Ph4qU2n0yE+Ph6RkZEVLtO5c2ckJydDp/vrrtpnz56Fr68vVCqV0Ws2lGa+f+654aBiIiIig5P1sFRsbCyWLFmCb775BomJiRgzZgwKCgqks6dGjBiBSZMmSf3HjBmD7OxsvPbaazh79izWr1+PDz/8EGPHjpXrLdyXZn6uAIATV3MhBAcVExERGZKsp4IPHToUmZmZmDp1KtLS0hAWFoZNmzZJg4xTU1OhVP6Vv/z9/bF582a88cYbaNWqFerVq4fXXnsN77zzjlxv4b4083WBjVKBrPxipGYXomEdR7lLIiIishoKUct2HeTm5sLV1RU5OTmyjr8ZtGAXjl3JwetRjfB6VGPZ6iAiIrIENfn+tqizpazJ0PYNAAAbT1R82jsRERHdH4YbmTzcxAsAkJyZj1Kt7h69iYiIqLoYbmTi7aKGvZ0NtDqBPedvyF0OERGR1WC4kYlCoYCbw537Y/2WlClzNURERNaD4UZGo7sGAQBSbhTIXAkREZH1YLiRUROfO3cIv5jFcENERGQoDDcyauzjDIXiTrhhwCEiIjIMhhsZeTqp8VDonbOmvvz9gszVEBERWQeGG5kNbe8PAPh+fyp2JGXIXA0REZHlY7iRWcfAOtLzl749JGMlRERE1oHhRmauDnYY2NoPAKCx5Y+DiIjoQfHb1Ay83TsUAJBbVIbiMq3M1RAREVk2hhszUM/NXno+/efTMlZCRERk+RhuzIBCoZCef78/FUWl3HtDRER0vxhuzMSiYW2l5yOX7ZexEiIiIsvGcGMmerfwkZ6fScuTsRIiIiLLxnBjJhQKBX4d3wUAUKYV0OmEzBURERFZJoYbM9LExxlqWyXyi8uQml0odzlEREQWieHGjNjaKKUzpy7yTuFERET3heHGzLSq7woA2HmGt2IgIiK6Hww3ZqZziCcA4JuES7hZUCJzNURERJaH4cbMdGtcV3r+5R+8UzgREVFNMdyYGW8XDcb0CAYALNp5HvnFZTJXREREZFkYbszQ2IdCpOdT156UsRIiIiLLw3BjhpzUthjVOQAAsOHkdQjBa94QERFVF8ONmXqndxPYKBUoKtXhWk6R3OUQERFZDIYbM6Wxs0HbBm4AgG8TLslbDBERkQVhuDFjT7TzBwAs/u088opKZa6GiIjIMjDcmLEBrfyk5z0/+U3GSoiIiCwHw40Zs1fZ4P3BLQAAGXnFSOLdwomIiO6J4cbMDe/YEHY2CgBA9PzfkXqDN9QkIiKqCsONBZj9WCvpedzGRBkrISIiMn8MNxbgsbb10KKeCwDgj3NZKCzhVYuJiIgqw3BjARQKBX4Z1wUBdRyQX1yG9cevy10SERGR2WK4sRAKhQJPtr9zavh3+1JlroaIiMh8MdxYkMfD6wMAjl2+hZsFJTJXQ0REZJ4YbiyIl7MGDes4AABOXsuRuRoiIiLzxHBjYVrUcwUADP96PzJyec8pIiKif2K4sTD+7g7S87d/PC5jJUREROaJ4cbCjH0oWHq+MykTtwo59oaIiOjvGG4sjLPGDn+8/ZA0HTZzq4zVEBERmR+zCDcLFy5EQEAANBoNIiIisH///kr7Ll++HAqFQu+h0WhMWK38/D0c8Egzb2l634UbMlZDRERkXmQPNytXrkRsbCymTZuGw4cPo3Xr1oiOjkZGRkaly7i4uOD69evS49KlSyas2DwsGtZWej70y7344eBlGashIiIyH7KHm3nz5mH06NEYNWoUmjVrhsWLF8PBwQFLly6tdBmFQgEfHx/p4e3tXWlfa2Vro8SnQ1tL02+tPo7T13JlrIiIiMg8yBpuSkpKcOjQIURFRUltSqUSUVFRSEhIqHS5/Px8NGzYEP7+/hg0aBBOnTpVad/i4mLk5ubqPazFo23qY+NrXaXpuZvPyFgNERGReZA13GRlZUGr1Zbb8+Lt7Y20tLQKlwkNDcXSpUuxbt06fPfdd9DpdOjUqROuXLlSYf+4uDi4urpKD39/f4O/Dzk19XXBvCfv7MHZkZSJPclZMldEREQkL9kPS9VUZGQkRowYgbCwMHTv3h1r1qxB3bp18e9//7vC/pMmTUJOTo70uHzZ+samPNqmnvT8ma/2oahUK2M1RERE8pI13Hh6esLGxgbp6el67enp6fDx8anWOuzs7NCmTRskJydXOF+tVsPFxUXvYW0UCgWm9m8mTc/dnCRjNURERPKSNdyoVCqEh4cjPj5eatPpdIiPj0dkZGS11qHVanHixAn4+voaq0yL0L/VX+9/86mKD+kRERHVBrIfloqNjcWSJUvwzTffIDExEWPGjEFBQQFGjRoFABgxYgQmTZok9Z85cya2bNmCCxcu4PDhw3j22Wdx6dIlvPDCC3K9BbPg5aLB6K6BAIArN28jYOJ6bDxxXeaqiIiITM9W7gKGDh2KzMxMTJ06FWlpaQgLC8OmTZukQcapqalQKv/KYDdv3sTo0aORlpYGd3d3hIeHY8+ePWjWrFllL1FrTO7XDKVageV7UgAAb6w6il7NfWCjVMhbGBERkQkphBBC7iJMKTc3F66ursjJybHK8TelWh1mbzyDr3ddlNqSPugNta2NjFURERE9mJp8f8t+WIoMy85GiSn9m+G5zoFS29S1lV8HiIiIyNow3FipSX2bSM9XHryMzLxiGashIiIyHYYbK2Vno8SFD/tCbXvnR9x+1jak5xbJXBUREZHxMdxYMaVSgbeiQ6XpR+b9Bq2uVg2xIiKiWojhxso91zkQzpo7J8XlFpWh6ZRN0DHgEBGRFWO4sXJKpQLHp/WCn6sGAFCi1aHf57tQptXJXBkREZFxMNzUAgqFAhtf6yZNJ17PRcjkjbicXShjVURERMbBcFNLuDrYYc6QVnptL3xzUKZqiIiIjIfhphZ5PLw+3uzVWJpOSs/j3hsiIrI6DDe1iFKpwLiHG+E/z3WQ2rrO2YFrt27LWBUREZFhMdzUQt0a10Xflj7SdKfZ2/HBr6dRy+7EQUREVorhppb67Kk2iGrqLU1/tesiYlcdk7EiIiIiw2C4qaVsbZT49/BwvbafjlxFwMT1OHElR6aqiIiIHhzDTS1mo1Tgwod9EeLlpNc+aOEuHqIiIiKLxXBTyymVCmyL7a53mwadAF789hBKeaE/IiKyQAw3BAB4pUcwtk/ojo5BHgCArafT0WjyRszZdAZFpVqZqyMiIqo+hhsCcOcqxkF1nTChV6he+xc7z6PJlE0oLmPAISIiy8BwQ3raB3jg53Gdy7W3mLYZe5KzZKiIiIioZmocbr755husX79emn777bfh5uaGTp064dKlSwYtjuTRqr4bujeuq9dWqhWYvPakTBURERFVX43DzYcffgh7e3sAQEJCAhYuXIg5c+bA09MTb7zxhsELJHl8Mawtlo9qj2ciGkhtF7MKkJSWJ2NVRERE92Zb0wUuX76MkJAQAMDatWsxZMgQvPjii+jcuTN69Ohh6PpIJo5qW/QI9ULHoDoIruuEhTuSkV1Qgnd/OoFVL0VCqbgzToeIiMjc1HjPjZOTE27cuAEA2LJlCx555BEAgEajwe3bvEeRtdHY2eD5LoH4dGgYAODQpZsIfncDAidtQBlPFSciIjNU43DzyCOP4IUXXsALL7yAs2fPom/fvgCAU6dOISAgwND1kZno1sizXNvDn/yGwpIy3MgvlqEiIiKiitU43CxcuBCRkZHIzMzEjz/+iDp16gAADh06hKefftrgBZJ5UCgUWP1ypF5banYhmk3djPAPtuHKzUKZKiMiItKnELXsOvu5ublwdXVFTk4OXFxc5C7H4uQUluJazm0M/XcCcovKpPap/ZvhuS6BMlZGRETWrCbf3zXec7Np0ybs2rVLml64cCHCwsLwzDPP4ObNmzWvliyKq4Mdmvq6YPzDjfTaD13iz56IiMxDjcPNW2+9hdzcXADAiRMnMGHCBPTt2xcXL15EbGyswQsk8zSqcwCim3tL0+tPXMfbq49h+e6LHGhMRESyqvFhKScnJ5w8eRIBAQGYPn06Tp48idWrV+Pw4cPo27cv0tLSjFWrQfCwlGGVlOnQ+L2N5dpTZveToRoiIrJWRj0spVKpUFh4Z/Dotm3b0KtXLwCAh4eHtEeHag+VrRLd/nE1YwA4k8bPAhERyaPG4aZLly6IjY3F+++/j/3796Nfvzv/oZ89exb169c3eIFk/v7zXAfsfLMH5j3ZWmob/78jMlZERES1WY3DzYIFC2Bra4vVq1dj0aJFqFevHgBg48aN6N27t8ELJMsQ4OmIx9rWx+AwPwDAuYx8BExcz9s1EBGRyfFUcDKoG/nFCP9gm17bgclRqOuslqkiIiKyBjX5/q7xvaUAQKvVYu3atUhMTAQANG/eHAMHDoSNjc39rI6sSB0nNd7r1xQfrE+U2l5feQT/faGjjFUREVFtUuM9N8nJyejbty+uXr2K0NBQAEBSUhL8/f2xfv16BAcHG6VQQ+GeG9O4mFWAuZvPYMOJO2fPrX45Eu0CPGSuioiILJVRz5Z69dVXERwcjMuXL+Pw4cM4fPgwUlNTERgYiFdfffW+iybrEujpiC+GhUvTczcnobhMK2NFRERUW9R4z42joyP27t2Lli1b6rUfO3YMnTt3Rn5+vkELNDTuuTGt+MR0PP/NQWn6xzGRCG/IPThERFQzRt1zo1arkZdX/gyY/Px8qFSqmq6OrFzPpt6IDKojTccsPYBaNoadiIhMrMbhpn///njxxRexb98+CCEghMDevXvx8ssvY+DAgcaokSzcm9GNpef5xWUInLQBS3ddlLEiIiKyZjUON5999hmCg4MRGRkJjUYDjUaDzp07IyQkBPPnzzdCiWTpwht64PTMaL22mb+exu0SjsEhIiLDu+/r3CQnJ0ungjdt2hQhISEGLcxYOOZGPtdu3Uan2dul6fEPhyCgjiMGt6kHG6VCxsqIiMjc1eT722AX8Tt+/DjatWuHkpISQ6zOaBhu5LXrXBbeWn0M13OKpLb3B7fA8I4NZayKiIjMnVEHFFdGCAGt9v4OMyxcuBABAQHQaDSIiIjA/v37q7XcihUroFAoMHjw4Pt6XTK9Lo08seqlSL22KWtPoqiUh6iIiMgwDBZu7tfKlSsRGxuLadOm4fDhw2jdujWio6ORkZFR5XIpKSl488030bVrVxNVSobi7+GAfz0VptfWZMomDP96H3Q6nklFREQPRvZwM2/ePIwePRqjRo1Cs2bNsHjxYjg4OGDp0qWVLqPVajFs2DDMmDEDQUFBJqyWDGVQWD2kzO6n1/bHuSw8tWQvTxUnIqIHUu1wk5ubW+Wjomvf3EtJSQkOHTqEqKiovwpSKhEVFYWEhIRKl5s5cya8vLzw/PPP3/M1iouLy9VK5uP9wS30pvdfzMZTX+7F5exCmSoiIiJLV+0bZ7q5uUGhqPyMFiFElfMrkpWVBa1WC29vb712b29vnDlzpsJldu3aha+//hpHjx6t1mvExcVhxowZNaqLTOfZiAYY3rEhDl26iSGL9gAA9l3MRtc5O/DTK53QpoG7zBUSEZGlqXa42bFjhzHrqJa8vDwMHz4cS5YsgaenZ7WWmTRpEmJjY6Xp3Nxc+Pv7G6tEqqG7gTi8oTvWvNIJj32xR5r36Bd78PnTbTCgtZ9c5RERkQWqdrjp3r27wV/c09MTNjY2SE9P12tPT0+Hj49Puf7nz59HSkoKBgwYILXpdDoAgK2tLZKSksrdlVytVkOtVhu8djK8tg3cMW1AM8z45bTUNv77I3DW2CLM3w1uDry9BxER3ZusA4pVKhXCw8MRHx8vtel0OsTHxyMyMrJc/yZNmuDEiRM4evSo9Bg4cCAeeughHD16lHtkrMCwiIaYPqCZXtvIZQfwwt9uvklERFSVau+5MZbY2FjExMSgXbt26NChA+bPn4+CggKMGjUKADBixAjUq1cPcXFx0Gg0aNFCfwCqm5sbAJRrJ8ukslViZOdAjOwciICJ66X2g5duImDierzasxHeiGpU4/FdRERUe8geboYOHYrMzExMnToVaWlpCAsLw6ZNm6RBxqmpqVAqZT9jnWRwfHovDFuyDyeu5khtn8WfQ1JaLv49vJ2MlRERkTkz2O0XLAVvv2B5Um8Uottc/QHtwXUdsXxUB/h7OMhUFRERmZIst18gMpYGdRxwMa4v9r3bU2o7n1mAyWtPylgVERGZqxoflnr00UcrHO+gUCig0WgQEhKCZ555BqGhoQYpkAi48/nydtFgVOcALNudAgD4/WwmZvxyCtMGNJe3OCIiMis13nPj6uqK7du34/Dhw1AoFFAoFDhy5Ai2b9+OsrIyrFy5Eq1bt8bu3buNUS/Vcm9HN8GyUe2l6WW7U1Cq1clYERERmZsahxsfHx8888wzuHDhAn788Uf8+OOPOH/+PJ599lkEBwcjMTERMTExeOedd4xRL9Vy9iobPBTqhW+e6yC1NZq8kTfcJCIiSY0HFNetWxe7d+9G48aN9drPnj2LTp06ISsrCydOnEDXrl1x69YtQ9ZqEBxQbD3+fqo4ALwVHYrRXYOgsuVQMiIia2PUAcVlZWUV3vfpzJkz0Gq1AACNRsPrkJDRHZvWS2967uYk9P/8D5mqISIic1HjcDN8+HA8//zz+PTTT7Fr1y7s2rULn376KZ5//nmMGDECAPDbb7+heXMO8iTjcrW3w6JhbfXazqbn40wa7/xORFSb1fhsqU8//RTe3t6YM2eOdE8ob29vvPHGG9I4m169eqF3796GrZSoAn1a+mL2Yy0xcc0JqS0+MQNNfHjIkYiotnqgi/jl5t75D9mSxq5wzI31mvnLaSzdfVGantK/GZ7vEihjRUREZCgmu4ifi4sLAwKZjcjgOnrT7/96GsO/3oe9F27IVBEREcmhxuEmPT0dw4cPh5+fH2xtbWFjY6P3IJJLVFMvhPm76bX9cS4LT325F7+fzZSnKCIiMrkaH5bq06cPUlNTMW7cOPj6+pY7K2rQoEEGLdDQeFjK+ul0Amm5Reg0e7te+6H3olDHSS1TVURE9CBq8v1d43Dj7OyMP/74A2FhYQ9So2wYbmqPKzcL0fOT31Bc9tcVjEd2CsD0gTyTj4jI0hh1zI2/vz9q2Y3EyULVd3dA4kz9s/aW70nBxawCmSoiIiJTqHG4mT9/PiZOnIiUlBQjlENkWEqlAsmz+qBb47pS20Mf70TAxPVYtvsiktLyZKyOiIiMocaHpdzd3VFYWIiysjI4ODjAzs5Ob352drZBCzQ0HpaqvX48dAUTfjhWrn3vpJ7wcdXIUBEREVVXTb6/a3wRv/nz599vXUSyGhJeHwDKBZyOcfH4cUwnhDd0l6MsIiIysAe6iJ8l4p4bKirV4ps9KYjbqH+PtMNTHoGHo0qmqoiIqCoGH1B890rEd59X9SAydxo7G7zUPRj/Gx2h1972/a3YkZQhU1VERGQo1dpzY2Njg+vXr8PLywtKpbLCO34LIaBQKKQ7g5sr7rmhvzufmY9PtiRhw4k0qe3UjGg4qmt8xJaIiIzI4GNutm/fDg8PDwDAjh07HrxCIjMRXNcJC59pi8BJG6S2xxcnYONrXWWsioiIHgTH3BAB2HwqDS99e0iajolsiBmDWshYERER/Z1Rr1AMALdu3cL+/fuRkZEBnU6nN2/EiBE1XZ1JMdxQZW6XaNF06iZpetPrXdHEh58RIiJzYNRw88svv2DYsGHIz8+Hi4uL3vgbhULB69yQRTucehOPfbFHr23/uz3h5cLr4BARycmot1+YMGECnnvuOeTn5+PWrVu4efOm9DD3YEN0L20buKNTcB29tv8kXJKpGiIiuh81DjdXr17Fq6++CgcHB2PUQyS7RcPC9aYPp95EUal5nwVIRER/qXG4iY6OxsGDB41RC5FZcHWwQ4t6f+3y3HP+BppM2YQLmfkyVkVERNVV44t59OvXD2+99RZOnz6Nli1blru31MCBAw1WHJFcfh3fFSev5qD/57uktoc/+Q0fDG6BTsF1EFTXScbqiIioKjUeUKxUVr6zhxfxI2vzbUIKpqw7Va79l3Fd0LK+qwwVERHVTkYdUKzT6Sp9mHuwIaqp4ZEBuPBh33LtKw+mylANERFVR43DDVFto1Qqyl2x+Lu9qZi39SzKtLpKliIiIrlU67DUZ599hhdffBEajQafffZZlX1fffVVgxVnDDwsRfdLqxMoKdPpXegPAP7zXAd0a1xXpqqIiGoHg1/ELzAwEAcPHkSdOnUQGBhY+coUCly4cKHmFZsQww09qE+2JOHz7cnl2pNn9YGtDXeGEhEZg9Fvv2DJGG7IEG7kF2Pc/44g4cINvfa3e4filR4hMlVFRGS9jDqgmIiAOk5q/G90BDydVHrtczYl4ePNSTJVRUREwH1c5wYArly5gp9//hmpqakoKSnRmzdv3jyDFEZk7hQKBQ6+9wiu59xGZNx2qX3BjmQ4qm0xpkewjNUREdVeNT4sFR8fj4EDByIoKAhnzpxBixYtkJKSAiEE2rZti+3bt997JTLiYSkyBiEEzqTloc+//pDa6rnZY9c7D+ndXJaIiO6PUQ9LTZo0CW+++SZOnDgBjUaDH3/8EZcvX0b37t3xxBNP3HfRRJZMoVCgqa/+L9vVW7d5000iIhnUONwkJiZixIgRAABbW1vcvn0bTk5OmDlzJj766CODF0hkSQ5PeURvetrPp3AmLVemaoiIaqcahxtHR0dpnI2vry/Onz8vzcvKyjJcZUQWyMNRhZTZ/bDp9b8u+td7/h946ssEFBSXyVgZEVHtUeNw07FjR+zadedmgn379sWECRMwa9YsPPfcc+jYsaPBCySyRE18XPB271Bpeu+FbMzlWVRERCZR43Azb948REREAABmzJiBnj17YuXKlQgICMDXX399X0UsXLgQAQEB0Gg0iIiIwP79+yvtu2bNGrRr1w5ubm5wdHREWFgYvv322/t6XSJjeqVHCBp4OEjTy/ekIGDieqw9clXGqoiIrF+NzpbSarXYvXs3WrVqBTc3N4MUsHLlSowYMQKLFy9GREQE5s+fjx9++AFJSUnw8vIq13/nzp24efMmmjRpApVKhV9//RUTJkzA+vXrER0dfc/X49lSZGqpNwrRbe4OvbYjUx6Bu6OqkiWIiOifjHqFYo1Gg8TExCpvw1ATERERaN++PRYsWADgzl3H/f39MX78eEycOLFa62jbti369euH999//559GW5IDk2mbERR6V832XysbT1M698crg52MlZFRGQ5jHoqeIsWLQx2/6iSkhIcOnQIUVFRfxWkVCIqKgoJCQn3XF4Igfj4eCQlJaFbt24V9ikuLkZubq7eg8jUTs/ojf+9ECFNrzl8Fa1nbkF2QUkVSxER0f2ocbj54IMP8Oabb+LXX3/F9evXHyg4ZGVlQavVwtvbW6/d29sbaWlplS6Xk5MDJycnqFQq9OvXD59//jkeeeSRCvvGxcXB1dVVevj7+9eoRiJDUCoV6BTiiUFhfnrtbd/fivOZ+TJVRURknaodbmbOnImCggL07dsXx44dw8CBA1G/fn24u7vD3d0dbm5ucHd3N2atEmdnZxw9ehQHDhzArFmzEBsbi507d1bYd9KkScjJyZEely9fNkmNRBWJbu5Tru355Qeg1dWq+9cSERlVte8tNWPGDLz88svYsWPHvTtXk6enJ2xsbJCenq7Xnp6eDh+f8l8CdymVSoSE3LnzclhYGBITExEXF4cePXqU66tWq6FWqw1WM9GD6NXsr72U3RvXxW9nM5FyoxC/n8tEj8Z1easGIiIDqHa4uTvuuHv37gZ7cZVKhfDwcMTHx2Pw4MEA7gwojo+Px7hx46q9Hp1Oh+LiYoPVRWQstjZKpMzuJ03HLN2P385mYtSyA3BU2WDnWw+hrjPDOBHRg6jRmBtj/FcZGxuLJUuW4JtvvkFiYiLGjBmDgoICjBo1CgAwYsQITJo0SeofFxeHrVu34sKFC0hMTMQnn3yCb7/9Fs8++6zBayMytpe7/3Xn8IISLdrP2oZ1R6+ipExXxVJERFSVau+5AYDGjRvfM+BkZ2fXqIChQ4ciMzMTU6dORVpaGsLCwrBp0yZpkHFqaiqUyr8yWEFBAV555RVcuXIF9vb2aNKkCb777jsMHTq0Rq9LZA4ig+vghS6B+GrXRanttRVHsb7ZdXw5op2MlRERWa5qX+dGqVRi/vz5cHV1rbJfTEyMQQozFl7nhszRL8euYfz3R8q175n4MPzc7GWoiIjIvBjlIn5KpRJpaWkVXjXYkjDckDkSQmDMd4cBAJtO6V8GYeag5hgRGSBDVURE5qMm39/VPizFsziIjEehUGDx8HAAQE5hKVrP3CLNm7ruFAa08uPtGoiIqqnaA4preJcGIrpPrg52+Oa5Dnptbd7fioCJ6zFpzXH+LhIR3UO1w41Op7P4Q1JElqJ747pY/Gw4VLb6v6Lf77+MOZuTZKqKiMgy1PjGmZaOY27IklzIzMfDn/xWrr2emz1a1HPBwmfawtamxndRISKyOEa9cSYRmU5QXSf8PK4zZj3aQq/96q3b2HwqHasPXZGpMiIi88VwQ2TmWtV3w7CIhrgY1xfDOzbUm7fiAO+VRkT0Tww3RBZCoVBgcr+mem3HrtzCzYISmSoiIjJPDDdEFkRjZ4OLcX1x4cO+aOTlBCGAZXtS5C6LiMisMNwQWRiFQgGlUoHOIZ4AgM/iz+HxRXt4ijgR0Z8Ybogs1CsP/XXTzYOXbuKnI1dlrIaIyHww3BBZKC9nDVa/HClNx646hozcIhkrIiIyDww3RBasXYAHfhzzV8CZsu6kjNUQEZkHhhsiCxfe0APTBjQDAGw+lY6AiesRNnMLz6IiolqL4YbICozqHIgeoXWl6VuFpfj+QKqMFRERyYfhhshK/GtoG73pOZuSsPlUGsq0OpkqIiKSB8MNkZVwdbDD2L+dQQUAL317CM2nbUZhSZlMVRERmR5vnElkZXQ6gRm/nMI3CZfKzWvm64J14zrDjjfbJCILwxtnEtViSqUCMwa1wNY3upWbd/p6LtYdvYYec3fgqS8TUMpDVkRkhRhuiKxUI29n6Syqv3vzh2NIuVGIvReycSAlW4bKiIiMi+GGyIqN6hyIbbHd4KKxrXD++78mmrgiIiLj45gbolpCpxMIendDuXZbpQKrXo5E2wbuMlRFRFQ9HHNDROUolQqsHdsZAFDPzV5qL9MJPPbFHszdfEau0oiIDIp7bohqqfXHr2Ps/w7rtZ15vzc0djYyVUREVDnuuSGie+rXyhfHpvXCspHtpbYmUzbh7dXHUFSqlbEyIqIHw3BDVIu52tvhoSZeeKl7kNS26uAVNJmyCbdLGHCIyDIx3BAR3ohqXK7tf/t5byoiskwMN0QEjZ0N9kx8GI+1rSe1vf/raQRMXI9Dl3gtHCKyLBxQTER6svKL0WHWNuj+9pfBWW2LA+9FcbAxEcmGA4qJ6L55OqnxVUw7vba84jI0mbIJMUv3o5b9P0REFojhhojKebiJN1a+2BEDWvvptf92NhOBkzZgR1IGPt6chN3JWTJVSERUOR6WIqIq5RSWInr+70jLLapw/vHpveCisTNxVURU2/CwFBEZjKuDHfa+2xPv9Wta4fxW07fw7uJEZFYYboioWl7oGoSU2f3waJt65eY9t/wAx+IQkdngYSkiqpFSrQ55RWVw0dgiZPLGcvN3T3xY795VRESGwMNSRGQ0djZKeDiqYGujxNkP+pSbP3jhbpxJy8Wy3Rd5GwcikgX33BDRA8kuKMFzyw/g6OVbFc5PntUHtjb8P4qIHgz33BCRyXg4qrB2bGccmBxV4fyNJ9NMXBER1XYMN0RkEHWd1fjw0Zbl2sd/fwQbTlyXoSIiqq0YbojIYJ6JaID/vhCBqKbe6NXMW2p/5b+HsXBHMrS6WnUUnIhkwjE3RGQUJWU6NH5P/2wqB5UNjkx9BGpb3qOKiGrG4sbcLFy4EAEBAdBoNIiIiMD+/fsr7btkyRJ07doV7u7ucHd3R1RUVJX9iUgeKlslkmf1wU+vdJLaCku0CH1vE1bsT5WxMiKydrKHm5UrVyI2NhbTpk3D4cOH0bp1a0RHRyMjI6PC/jt37sTTTz+NHTt2ICEhAf7+/ujVqxeuXr1q4sqJ6F5sbZRo08C93CnjE9ecwNe7LuJydiEuZxfKVB0RWSvZD0tFRESgffv2WLBgAQBAp9PB398f48ePx8SJE++5vFarhbu7OxYsWIARI0bcsz8PSxHJ4+TVHPT/fFe5dns7G2x/szt8XXnhPyKqnMUcliopKcGhQ4cQFfXXKaRKpRJRUVFISEio1joKCwtRWloKDw+PCucXFxcjNzdX70FEpteinivWje2M4R0b6rXfLtViT/INmaoiImska7jJysqCVquFt7e3Xru3tzfS0qp3bYx33nkHfn5+egHp7+Li4uDq6io9/P39H7huIro/rf3d8P7gFnh/cAu99gk/HMO8LUm8ojERGYTsY24exOzZs7FixQr89NNP0Gg0FfaZNGkScnJypMfly5dNXCUR/dPwjg3xvxci8HL3YKnts+3J6DZnB+8wTkQPzFbOF/f09ISNjQ3S09P12tPT0+Hj41Plsh9//DFmz56Nbdu2oVWrVpX2U6vVUKvVBqmXiAynU4gnOoV44ujlm9h7IRsAkJFXjEaTN6JHaF38e3i43injRaVa2CoVvJUDEd2TrOFGpVIhPDwc8fHxGDx4MIA7A4rj4+Mxbty4SpebM2cOZs2ahc2bN6Ndu3YmqpaIjGHFi5G4kJmPqHm/4e41/nYmZSL0vU3l+nYI9MCqlyJNXCERWRrZ/wWKjY3FkiVL8M033yAxMRFjxoxBQUEBRo0aBQAYMWIEJk2aJPX/6KOPMGXKFCxduhQBAQFIS0tDWloa8vPz5XoLRPSAguo64ewHffBMRIMq++2/mI331p7g2BwiqpLsp4IDwIIFCzB37lykpaUhLCwMn332GSIiIgAAPXr0QEBAAJYvXw4ACAgIwKVLl8qtY9q0aZg+ffo9X4unghOZvxNXcjBgQfnTxv/ukydaY0h4fRNVRERyq8n3t1mEG1NiuCGyDLo/j1EplQoAwIr9qZi45kS5fj+90gltGribtDYiMj2Luc4NEVFllEqFFGwA4KkODbD59W7l+j36xR6UlPEMKyL6C8MNEVmMUB9npMzuhyFt9Q9HNX5vI345dk2mqojI3PCwFBFZnJIyHa7n3MaSPy7gu71/3YQzuK4jnmznj5hOAdDY8c7jRNaEY26qwHBDZF0W7kjG3M1J5doPT3kEHo4qGSoiImPgmBsiqjXGPhSC/e/2RMt6rnrtbd/fKg1KJqLaheGGiCyel4sGv4zvgt0TH9ZrD3p3AxZsPydTVUQkFx6WIiKrE7vqKNYcvipNeziq0LaBG+q7O2D8wyGo48RbshBZGo65qQLDDVHtMHLZfuxMyqxwXo/Qugjzd4O3iwaDwvzgoJL1TjREVA0MN1VguCGqPUq1OrScvhlFpZVfB6dDoAdWvtgRCoWi0j5EJD+Gmyow3BDVLjqdwM9/XgPng/WJyMovrrDfv54Kw6CweqYsjYhqgOGmCgw3RLWXTidw+WYhus/dWeH8xJm9obZV6l0ZmYjMA8NNFRhuiCgjrwhn0/Ixd0sSjl2+VWm/P95+CP4eDqYrjIgqxevcEBFVwctZgy6NPLFubGcsH9W+0n5d5+wwYVVEZCgMN0RUq/UI9cLx6b3wTESDCucHTFyPZbsvmrgqInoQPCxFRPSn6zm38er3RxAZVAc/Hr6Kq7duS/NGdgqAn5sGz0Q0hJOap44TmRrH3FSB4YaIqqOoVIuvd12s8L5Vz3ZsAD83e/Rp4YtAT0cZqiOqfRhuqsBwQ0Q18evxaxj3vyMVzmvi44xNr3czcUVEtRMHFBMRGUj/Vn44PTMa/3oqDLb/OEX8TFoeLt0oQM7tUpmqI6KKcM8NEVEN5ReXITIuHnlFZVLb/KFhGNyGFwEkMhbuuSEiMiIntS36tfTVa3t95VGcS89Dem6RTFUR0V0MN0RE92HsQyHl2h759HdEfBiPsf89jKJSLS5nF2Ls/w4jKS1PhgqJai8eliIiuk/n0vNw+WYhrt4qwpS1J6vse3pmNO8+TvQAeFiKiMgEGnk74+Em3ngivP49+0788QQOXbppgqqIiHtuiIgMICOvCMcu56BDoAcm/ngcG0+mVdl/YGs/vBUdyntXEVUTr3NTBYYbIjKl6zm3ERm3vdL5T3fwR9xjrUxYEZFlYripAsMNEZnagZRs/HE2E5eyC/HLsWvQVfBXd1tsd4R4OZm+OCILwXBTBYYbIjIHu85l4dmv95VrPzerD+xsOByS6J84oJiIyMx1aeSJlNn98GK3IL32RpM38k7kRA+I4YaISEbv9m2KX8d3Kdc+45fTSL1RKENFRJaP4YaISGYt6rkiZXY/zHlcf2Bxt7k7cPJqjkxVEVkuhhsiIjPxZDt/pMzuh4ebeElt/T/fhT7/+gMXMvNRy4ZIEt03DigmIjIzOp3AR5vO4N+/Xyg3r0uIJ2xtFHg8vD76t/KToToiefBsqSow3BCRpTibnoden/5eZZ8lI9rhX/FncfJqLv7zXAd0a1zXRNURmRbDTRUYbojIkuQWlWLrqXRcuXkby/ZcxK3C0ir7eziqsPG1rvB20QAAikq1yCsqg6eTCgqFwhQlExkFw00VGG6IyJJdzi7E5ZuFOH4lB7M3nqm0n52NAkqFAsVlOgBAUF1HxMd2Z8Ahi8VwUwWGGyKyJltOpeH7/alo4uuCRTvP37P/vnd7Snt1iCwJw00VGG6IyJp9t/cS3lt7sso+C55pw8HIZHEYbqrAcENEtYEQAkWlOtirbAAAH29OwoIdyXp9nmxXHz8evop5T7bGoLB6cpRJVG0MN1VguCGi2mrtkat4feXRCueteikS3i5qzN2chF+PXwcAPN8lEO/1a8pxOmQWGG6qwHBDRLXZ5exCjP7PQZxJy6v2Mnsn9YSPK8fpkLwYbqrAcENEdEfqjUJ8fyC1WgORH2tbD5880Zp7cUg2FnVX8IULFyIgIAAajQYRERHYv39/pX1PnTqFIUOGICAgAAqFAvPnzzddoUREVqZBHQe807sJDkyOwqxHW+Ct6FAcei8KKbP7IWV2P3z4aEup75rDV7HkjwtIOH8Dp6/lolSrwx/nMvHb2UwZ3wFRxWzlfPGVK1ciNjYWixcvRkREBObPn4/o6GgkJSXBy8urXP/CwkIEBQXhiSeewBtvvCFDxURE1qeusxrDIhqWa38mogFul2rx/q+nAQAfbqj8ujqJM3tLg5eJ5CbrYamIiAi0b98eCxYsAADodDr4+/tj/PjxmDhxYpXLBgQE4PXXX8frr79eo9fkYSkioprJKSzF4C9242JWQZX9PBxVeKd3KIpKdZj28yl8NKQlejf3hYu9LQ9n0QOziMNSJSUlOHToEKKiov4qRqlEVFQUEhISDPY6xcXFyM3N1XsQEVH1uTrYYfuE7hjZKQBNfJyxbFR7PNzEC3OGtILj3/bWZBeU4J0fT2Daz6cAAO/8eAKtZ25B4KQNmLTmuFzlUy0k22GprKwsaLVaeHt767V7e3vjzJnKd33WVFxcHGbMmGGw9RER1UYKhQLTBzaXph8KvTN04Mn2/vjtbCZillY+XhIAvt9/Gd/vv4xfxnVBy/quRq2VSNYxN6YwadIkxMbGStO5ubnw9/eXsSIiIuvSvXFdpMzuB51OYFtiOpIz8/Fi1yB8uOEMAus6Ysrfrpg8YMEuAHcuIOiktkN6XhHeiGqMEC8nuconKyRbuPH09ISNjQ3S09P12tPT0+Hj42Ow11Gr1VCr1QZbHxERVUypVKBXcx/0+nN66oBmAIBH29RDi2mb9fquOnhFer7+z4sGvt07FC93C0ZhqRa2SgU0dhygTPdHtjE3KpUK4eHhiI+Pl9p0Oh3i4+MRGRkpV1lERGRgTmpbpMzuh5mDmlfZb86mJAS9uwEtpm1GkymbsGz3RRSVak1UJVkTWQ9LxcbGIiYmBu3atUOHDh0wf/58FBQUYNSoUQCAESNGoF69eoiLiwNwZxDy6dOnpedXr17F0aNH4eTkhJCQENneBxER3duIyAD0a+mLOk5qZOQVwUltC5WNEt3m7MC1nKJy/Wf8chozfrnzN//x8Pr4aEgr2Ch51hXdm+xXKF6wYAHmzp2LtLQ0hIWF4bPPPkNERAQAoEePHggICMDy5csBACkpKQgMDCy3ju7du2Pnzp3Vej2eCk5EZH6KSrU4dS0HX/1xEfnFZfjjXFaF/Z7t2ABOajuMfSgYzho7E1dJcuLtF6rAcENEZP5ul2jx29kMfL49Gecz81FUqivXp4mPM17sFoTo5j5wVFv9+TG1HsNNFRhuiIgsz/LdFzH9z0NUFXmpexBGdgqAr6u9CasiU2K4qQLDDRGRZSoq1aK4VIeLNwoweOHuCvvUd7fH9AHNEdXMu8L5ZLkYbqrAcENEZB3yikqxfHcKPtl6tty8kZ0CMLFPE2QXlEBjZwMPR5UMFZIhMdxUgeGGiMj66HQCu5KzsGB7MvanZFfYZ+xDwXg9qjHsbGS7Cgo9AIabKjDcEBFZr5zbpWg9Y0uVfQI9HTGmRzAiAj1Q392Bp5dbCIabKjDcEBFZt1KtDqeu5SIjtwgHUrLx87FrSM8trtayb/cOxfNdAqG25dWRzQ3DTRUYboiIah+dTuDijQLEbTiDbYnp914AQCMvJ0wb0BxdGnkauTqqDoabKjDcEBHRjfxi5BWVYez/DuNWYSmu3rp9z2WGRTRAanYh1LZK9GrugyfC60Oh4CEtU2G4qQLDDRERVaS4TIuktDyMXHYAzXxdcPp6LrILSqpcJiayIa7nFCHA0xETejVGUakOjiob2HLQssEx3FSB4YaIiKrr52PXMOnH4ygoub8beLao5wIHO1v0bemDpyMacCzPA2C4qQLDDRER3Q8hhHQYat+FG3hj5dEKb/hZHZ5OamTl3xnkHOTpiF9f7QIHFW8hURWGmyow3BARkSEIIVBYooWDygbXcoqw78INZOYVY8GOZOQVlcHXVQOVrRKXbhRWe50fP9Eaj4fXN2LVlovhpgoMN0REZEpCCCSl5+HjzUnYlpgBTycVsvIrH8vTPsAdA1v7QQDo29IXWp1AXSc1lLX8ejwMN1VguCEiInOy/Uw6nlt+8J79pg1ohqc7NIDGzgZlWh1ybpeijpPaBBWaB4abKjDcEBGROSou02LjiTTM2pCIzLzKLzqoVAC6P7+5Az0d8XATL7Ss54qE8zdQWKpFWs5ttA/wgK+bPZ4Irw+NnXUMYma4qQLDDRERmTOtTqCwpAz5xWXYejodGlsbfLwlCRlVBJ6qtG3gBmeNHXYlZ2Hek63Rv5Wf9DoqW8s5ZZ3hpgoMN0REZImu3CzEiKX7kVdUhsfa1ENQXUccvnQL+y7ewJWbt6G2VdbolHUXjS1mD2kFB5UNbJQKdA72hFYIs72xKMNNFRhuiIjI2tw9TV2rE1AAUCoVKCrV4utdF7H3wg0cvnQTtjZK5Nwurdb6Gns74dOhYfD3cIDKRmkWh7YYbqrAcENERLXVyas5GLnsAF7uHoQrN29j+Z6Uai3XwMMBXwxri0beTrBRKFBcpsP5zHx4OKrgam8HZ42dcQsHw02VGG6IiIjuuJxdiBKtDmuPXMWPh67AQW2L5Iz8Gq9HbavEyE4BUNsq4WJvh/ru9ujdwtegtTLcVIHhhoiIqGpCCBy9fAuXbhTixNUc/CchBaXa6seF9gHu+OHlTgatqSbf37zWMxEREelRKBRo08AdbRq4Y3CbepjctykuZRciPbcIyRn56BDoAWeNLbYlZuBI6k1k5Zfg97OZsLezwe1SLQLqOMpbP/fcEBERkbmryfe3eZ7vRURERHSfGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKyKrdwFmJoQAsCdW6cTERGRZbj7vX33e7wqtS7c5OXlAQD8/f1lroSIiIhqKi8vD66urlX2UYjqRCArotPpcO3aNTg7O0OhUBh03bm5ufD398fly5fh4uJi0HVbG26r6uO2qj5uq5rh9qo+bqvqM9a2EkIgLy8Pfn5+UCqrHlVT6/bcKJVK1K9f36iv4eLiwg9/NXFbVR+3VfVxW9UMt1f1cVtVnzG21b322NzFAcVERERkVRhuiIiIyKow3BiQWq3GtGnToFar5S7F7HFbVR+3VfVxW9UMt1f1cVtVnzlsq1o3oJiIiIisG/fcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKw42BLFy4EAEBAdBoNIiIiMD+/fvlLsnkpk+fDoVCofdo0qSJNL+oqAhjx45FnTp14OTkhCFDhiA9PV1vHampqejXrx8cHBzg5eWFt956C2VlZaZ+Kwb3+++/Y8CAAfDz84NCocDatWv15gshMHXqVPj6+sLe3h5RUVE4d+6cXp/s7GwMGzYMLi4ucHNzw/PPP4/8/Hy9PsePH0fXrl2h0Wjg7++POXPmGPutGdy9ttXIkSPLfc569+6t16e2bKu4uDi0b98ezs7O8PLywuDBg5GUlKTXx1C/dzt37kTbtm2hVqsREhKC5cuXG/vtGVR1tlWPHj3KfbZefvllvT61YVstWrQIrVq1ki7CFxkZiY0bN0rzLeIzJeiBrVixQqhUKrF06VJx6tQpMXr0aOHm5ibS09PlLs2kpk2bJpo3by6uX78uPTIzM6X5L7/8svD39xfx8fHi4MGDomPHjqJTp07S/LKyMtGiRQsRFRUljhw5IjZs2CA8PT3FpEmT5Hg7BrVhwwYxefJksWbNGgFA/PTTT3rzZ8+eLVxdXcXatWvFsWPHxMCBA0VgYKC4ffu21Kd3796idevWYu/eveKPP/4QISEh4umnn5bm5+TkCG9vbzFs2DBx8uRJ8f333wt7e3vx73//21Rv0yDuta1iYmJE79699T5n2dnZen1qy7aKjo4Wy5YtEydPnhRHjx4Vffv2FQ0aNBD5+flSH0P83l24cEE4ODiI2NhYcfr0afH5558LGxsbsWnTJpO+3wdRnW3VvXt3MXr0aL3PVk5OjjS/tmyrn3/+Waxfv16cPXtWJCUliXfffVfY2dmJkydPCiEs4zPFcGMAHTp0EGPHjpWmtVqt8PPzE3FxcTJWZXrTpk0TrVu3rnDerVu3hJ2dnfjhhx+ktsTERAFAJCQkCCHufKkplUqRlpYm9Vm0aJFwcXERxcXFRq3dlP75ha3T6YSPj4+YO3eu1Hbr1i2hVqvF999/L4QQ4vTp0wKAOHDggNRn48aNQqFQiKtXrwohhPjiiy+Eu7u73rZ65513RGhoqJHfkfFUFm4GDRpU6TK1dVsJIURGRoYAIH777TchhOF+795++23RvHlzvdcaOnSoiI6ONvZbMpp/bish7oSb1157rdJlauu2EkIId3d38dVXX1nMZ4qHpR5QSUkJDh06hKioKKlNqVQiKioKCQkJMlYmj3PnzsHPzw9BQUEYNmwYUlNTAQCHDh1CaWmp3nZq0qQJGjRoIG2nhIQEtGzZEt7e3lKf6Oho5Obm4tSpU6Z9IyZ08eJFpKWl6W0bV1dXRERE6G0bNzc3tGvXTuoTFRUFpVKJffv2SX26desGlUol9YmOjkZSUhJu3rxpondjGjt37oSXlxdCQ0MxZswY3LhxQ5pXm7dVTk4OAMDDwwOA4X7vEhIS9NZxt48l/43757a667///S88PT3RokULTJo0CYWFhdK82rittFotVqxYgYKCAkRGRlrMZ6rW3TjT0LKysqDVavV+iADg7e2NM2fOyFSVPCIiIrB8+XKEhobi+vXrmDFjBrp27YqTJ08iLS0NKpUKbm5uest4e3sjLS0NAJCWllbhdrw7z1rdfW8Vvfe/bxsvLy+9+ba2tvDw8NDrExgYWG4dd+e5u7sbpX5T6927Nx577DEEBgbi/PnzePfdd9GnTx8kJCTAxsam1m4rnU6H119/HZ07d0aLFi0AwGC/d5X1yc3Nxe3bt2Fvb2+Mt2Q0FW0rAHjmmWfQsGFD+Pn54fjx43jnnXeQlJSENWvWAKhd2+rEiROIjIxEUVERnJyc8NNPP6FZs2Y4evSoRXymGG7IYPr06SM9b9WqFSIiItCwYUOsWrXKYn6hyfw99dRT0vOWLVuiVatWCA4Oxs6dO9GzZ08ZK5PX2LFjcfLkSezatUvuUsxeZdvqxRdflJ63bNkSvr6+6NmzJ86fP4/g4GBTlymr0NBQHD16FDk5OVi9ejViYmLw22+/yV1WtfGw1APy9PSEjY1NuZHi6enp8PHxkakq8+Dm5obGjRsjOTkZPj4+KCkpwa1bt/T6/H07+fj4VLgd786zVnffW1WfIR8fH2RkZOjNLysrQ3Z2dq3ffkFBQfD09ERycjKA2rmtxo0bh19//RU7duxA/fr1pXZD/d5V1sfFxcXi/nGpbFtVJCIiAgD0Plu1ZVupVCqEhIQgPDwccXFxaN26Nf71r39ZzGeK4eYBqVQqhIeHIz4+XmrT6XSIj49HZGSkjJXJLz8/H+fPn4evry/Cw8NhZ2ent52SkpKQmpoqbafIyEicOHFC74tp69atcHFxQbNmzUxev6kEBgbCx8dHb9vk5uZi3759etvm1q1bOHTokNRn+/bt0Ol00h/gyMhI/P777ygtLZX6bN26FaGhoRZ5mKW6rly5ghs3bsDX1xdA7dpWQgiMGzcOP/30E7Zv317uUJuhfu8iIyP11nG3jyX9jbvXtqrI0aNHAUDvs1UbtlVFdDodiouLLeczZZBhybXcihUrhFqtFsuXLxenT58WL774onBzc9MbKV4bTJgwQezcuVNcvHhR7N69W0RFRQlPT0+RkZEhhLhz+mCDBg3E9u3bxcGDB0VkZKSIjIyUlr97+mCvXr3E0aNHxaZNm0TdunWt4lTwvLw8ceTIEXHkyBEBQMybN08cOXJEXLp0SQhx51RwNzc3sW7dOnH8+HExaNCgCk8Fb9Omjdi3b5/YtWuXaNSokd7pzbdu3RLe3t5i+PDh4uTJk2LFihXCwcHB4k5vrmpb5eXliTfffFMkJCSIixcvim3btom2bduKRo0aiaKiImkdtWVbjRkzRri6uoqdO3fqnb5cWFgo9THE793d03bfeustkZiYKBYuXGhxpzffa1slJyeLmTNnioMHD4qLFy+KdevWiaCgINGtWzdpHbVlW02cOFH89ttv4uLFi+L48eNi4sSJQqFQiC1btgghLOMzxXBjIJ9//rlo0KCBUKlUokOHDmLv3r1yl2RyQ4cOFb6+vkKlUol69eqJoUOHiuTkZGn+7du3xSuvvCLc3d2Fg4ODePTRR8X169f11pGSkiL69Okj7O3thaenp5gwYYIoLS019VsxuB07dggA5R4xMTFCiDung0+ZMkV4e3sLtVotevbsKZKSkvTWcePGDfH0008LJycn4eLiIkaNGiXy8vL0+hw7dkx06dJFqNVqUa9ePTF79mxTvUWDqWpbFRYWil69eom6desKOzs70bBhQzF69Ohy/0jUlm1V0XYCIJYtWyb1MdTv3Y4dO0RYWJhQqVQiKChI7zUswb22VWpqqujWrZvw8PAQarVahISEiLfeekvvOjdC1I5t9dxzz4mGDRsKlUol6tatK3r27CkFGyEs4zOlEEIIw+wDIiIiIpIfx9wQERGRVWG4ISIiIqvCcENERERWheGGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboio1lMoFFi7dq3cZRCRgTDcEJGsRo4cCYVCUe7Ru3dvuUsjIgtlK3cBRES9e/fGsmXL9NrUarVM1RCRpeOeGyKSnVqtho+Pj97j7t25FQoFFi1ahD59+sDe3h5BQUFYvXq13vInTpzAww8/DHt7e9SpUwcvvvgi8vPz9fosXboUzZs3h1qthq+vL8aNG6c3PysrC48++igcHBzQqFEj/Pzzz8Z900RkNAw3RGT2pkyZgiFDhuDYsWMYNmwYnnrqKSQmJgIACgoKEB0dDXd3dxw4cAA//PADtm3bphdeFi1ahLFjx+LFF1/EiRMn8PPPPyMkJETvNWbMmIEnn3wSx48fR9++fTFs2DBkZ2eb9H0SkYEY7BacRET3ISYmRtjY2AhHR0e9x6xZs4QQd+7m/PLLL+stExERIcaMGSOEEOLLL78U7u7uIj8/X5q/fv16oVQqpbuF+/n5icmTJ1daAwDx3nvvSdP5+fkCgNi4caPB3icRmQ7H3BCR7B566CEsWrRIr83Dw0N6HhkZqTcvMjISR48eBQAkJiaidevWcHR0lOZ37twZOp0OSUlJUCgUuHbtGnr27FllDa1atZKeOzo6wsXFBRkZGff7lohIRgw3RCQ7R0fHcoeJDMXe3r5a/ezs7PSmFQoFdDqdMUoiIiPjmBsiMnt79+4tN920aVMAQNOmTXHs2DEUFBRI83fv3g2lUonQ0FA4OzsjICAA8fHxJq2ZiOTDPTdEJLvi4mKkpaXptdna2sLT0xMA8MMPP6Bdu3bo0qUL/vvf/2L//v34+uuvAQDDhg3DtGnTEBMTg+nTpyMzMxPjx4/H8OHD4e3tDQCYPn06Xn75ZXh5eaFPnz7Iy8vD7t27MX78eNO+USIyCYYbIpLdpk2b4Ovrq9cWGhqKM2fOALhzJtOKFSvwyiuvwNfXF99//z2aNWsGAHBwcMDmzZvx2muvoX379nBwcMCQIUMwb948aV0xMTEoKirCp59+ijfffBOenp54/PHHTfcGicikFEIIIXcRRESVUSgU+OmnnzB48GC5SyEiC8ExN0RERGRVGG6IiIjIqnDMDRGZNR45J6Ka4p4bIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisir/B/DbD5wdlFMCAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(np.asarray(jnp.asarray(train_losses)))\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Training Loss\")\n", - "plt.title(\"Training Loss vs Epoch\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9ce3b843", - "metadata": {}, - "source": [ - "### 测试网络性能\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "f7dde304", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7830b10243a94ecd94c36bbc33ebe5d4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "准确率 0.836\n" - ] - } - ], - "source": [ - "predict_and_visualize_net_activity(net)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainpy-dev", - "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": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e8b2938eee4536ccb4f8a32065a14cf25a821015 Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Mon, 13 Oct 2025 16:54:17 +0800 Subject: [PATCH 2/8] Update version to 2.7.1 and refactor Dynamics class: enhance input handling and add new properties --- brainpy/state/_base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/brainpy/state/_base.py b/brainpy/state/_base.py index 8be13440..dc76b4f6 100644 --- a/brainpy/state/_base.py +++ b/brainpy/state/_base.py @@ -405,17 +405,17 @@ def align_pre(self, dyn: Union[ParamDescriber[T], T]) -> T: >>> n1.align_pre(brainstate.nn.Expon.desc(n1.varshape)) # n2 will run after n1 """ if isinstance(dyn, Dynamics): - self._add_after_update(id(dyn), dyn) + self.add_after_update(id(dyn), dyn) return dyn elif isinstance(dyn, ParamDescriber): if not issubclass(dyn.cls, Dynamics): raise TypeError(f'The input {dyn} should be an instance of {Dynamics}.') - if not self._has_after_update(dyn.identifier): - self._add_after_update( + if not self.has_after_update(dyn.identifier): + self.add_after_update( dyn.identifier, dyn() if ('in_size' in dyn.kwargs or len(dyn.args) > 0) else dyn(in_size=self.varshape) ) - return self._get_after_update(dyn.identifier) + return self.get_after_update(dyn.identifier) else: raise TypeError(f'The input {dyn} should be an instance of {Dynamics} or a delayed initializer.') From a45684d8fe83e2b153cfd4df7a14c79855f263e1 Mon Sep 17 00:00:00 2001 From: Minghao Wang <932179209@qq.com> Date: Mon, 13 Oct 2025 09:15:16 +0000 Subject: [PATCH 3/8] Add state version implementations for neuron and synapse models This commit implements state versions of several neuron and synapse models using the brainstate framework, following BrainPy v2.7+ architecture. Neuron Models Added: - LIF (Leaky Integrate-and-Fire) neurons with multiple variants: * LIF: Basic LIF neuron with exponential synaptic input * LifRef: LIF with refractory period * ExpIF: Exponential Integrate-and-Fire * ExpIFRef: ExpIF with refractory period * AdExIF: Adaptive Exponential Integrate-and-Fire * AdExIFRef: AdExIF with refractory period * QuaIF: Quadratic Integrate-and-Fire * QuaIFRef: QuaIF with refractory period * AdQuaIF: Adaptive Quadratic Integrate-and-Fire * AdQuaIFRef: AdQuaIF with refractory period * GifRef: Generalized Integrate-and-Fire with refractory - Izhikevich neuron model with variants: * Izhikevich: Basic Izhikevich neuron * IzhikevichRef: With refractory period - Hodgkin-Huxley (HH) neuron model: * HH: Classic Hodgkin-Huxley model with Na+ and K+ channels Synapse Models Added: - BioNMDA: Biological NMDA receptor with second-order kinetics * Implements two-state cascade dynamics (x and g variables) * Slower rise time compared to AMPA (biologically realistic) * Comprehensive documentation with mathematical formulation Testing: - Comprehensive test suites added for all models - AMPA and GABAa synapse tests added - All tests passing with proper unit handling Key Features: - Uses brainstate ecosystem (HiddenState, ShortTermState, LongTermState) - Proper unit support with brainunit - Exponential Euler integration for numerical stability - Batch processing support - Consistent API design across all models Files Modified: - brainpy/state/_lif.py: Added LIF variants - brainpy/state/_izhikevich.py: Added Izhikevich variants (new file) - brainpy/state/_hh.py: Added HH model (new file) - brainpy/state/_synapse.py: Added BioNMDA model - brainpy/state/_synapse_test.py: Added comprehensive tests - brainpy/state/_lif_test.py: Added LIF tests - brainpy/state/__init__.py: Updated exports - brainpy/dyn/neurons/lif.py: Minor documentation updates --- brainpy/dyn/neurons/lif.py | 12 + brainpy/state/__init__.py | 8 +- brainpy/state/_hh.py | 666 +++++++++++ brainpy/state/_hh_test.py | 303 +++++ brainpy/state/_izhikevich.py | 407 +++++++ brainpy/state/_izhikevich_test.py | 291 +++++ brainpy/state/_lif.py | 1862 ++++++++++++++++++++++++++--- brainpy/state/_lif_test.py | 283 ++++- brainpy/state/_synapse.py | 158 ++- brainpy/state/_synapse_test.py | 137 ++- 10 files changed, 3924 insertions(+), 203 deletions(-) create mode 100644 brainpy/state/_hh.py create mode 100644 brainpy/state/_hh_test.py create mode 100644 brainpy/state/_izhikevich.py create mode 100644 brainpy/state/_izhikevich_test.py diff --git a/brainpy/dyn/neurons/lif.py b/brainpy/dyn/neurons/lif.py index 23fa28c8..f7ddf886 100644 --- a/brainpy/dyn/neurons/lif.py +++ b/brainpy/dyn/neurons/lif.py @@ -857,6 +857,10 @@ class ExpIF(ExpIFLTC): conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire + .. seealso:: + + :class:`brainpy.state.ExpIF` provides the state-based formulation of this neuron. + **Examples** There is a simple usage example:: @@ -978,6 +982,10 @@ class ExpIFRefLTC(ExpIFLTC): conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire + .. seealso:: + + :class:`brainpy.state.ExpIFRef` provides the state-based formulation of this neuron. + **Examples** There is a simple usage example:: @@ -1319,6 +1327,10 @@ class AdExIFLTC(GradNeuDyn): inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model + .. seealso:: + + :class:`brainpy.state.AdExIF` provides the state-based formulation of this model. + **Examples** An example usage: diff --git a/brainpy/state/__init__.py b/brainpy/state/__init__.py index 05fa5fed..2186574c 100644 --- a/brainpy/state/__init__.py +++ b/brainpy/state/__init__.py @@ -22,6 +22,10 @@ from ._inputs import __all__ as inputs_all from ._lif import * from ._lif import __all__ as neuron_all +from ._izhikevich import * +from ._izhikevich import __all__ as izh_all +from ._hh import * +from ._hh import __all__ as hh_all from ._projection import * from ._projection import __all__ as proj_all from ._readout import * @@ -36,9 +40,9 @@ from ._synouts import __all__ as synout_all from .. import mixin -__main__ = ['version2', 'mixin'] + inputs_all + neuron_all + readout_all + stp_all + synapse_all +__main__ = ['version2', 'mixin'] + inputs_all + neuron_all + izh_all + hh_all + readout_all + stp_all + synapse_all __main__ = __main__ + synout_all + base_all + exp_all + proj_all + synproj_all -del inputs_all, neuron_all, readout_all, stp_all, synapse_all, synout_all, base_all +del inputs_all, neuron_all, izh_all, hh_all, readout_all, stp_all, synapse_all, synout_all, base_all del exp_all, proj_all, synproj_all if __name__ == '__main__': diff --git a/brainpy/state/_hh.py b/brainpy/state/_hh.py new file mode 100644 index 00000000..26f78210 --- /dev/null +++ b/brainpy/state/_hh.py @@ -0,0 +1,666 @@ +# Copyright 2024 BrainX Ecosystem Limited. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# -*- coding: utf-8 -*- + +from typing import Callable + +import brainstate +import braintools +import brainunit as u +import jax +from brainstate.typing import ArrayLike, Size + +from ._base import Neuron + +__all__ = [ + 'HH', 'MorrisLecar', 'WangBuzsakiHH', +] + + +class HH(Neuron): + r"""Hodgkin–Huxley neuron model. + + **Model Descriptions** + + The Hodgkin-Huxley (HH; Hodgkin & Huxley, 1952) model for the generation of + the nerve action potential is one of the most successful mathematical models of + a complex biological process that has ever been formulated. The basic concepts + expressed in the model have proved a valid approach to the study of bio-electrical + activity from the most primitive single-celled organisms such as *Paramecium*, + right through to the neurons within our own brains. + + Mathematically, the model is given by, + + $$ + C \frac {dV} {dt} = -(\bar{g}_{Na} m^3 h (V-E_{Na}) + + \bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t) + $$ + + $$ + \frac {dx} {dt} = \alpha_x (1-x) - \beta_x, \quad x\in {\rm{\{m, h, n\}}} + $$ + + where + + $$ + \alpha_m(V) = \frac {0.1(V+40)}{1-\exp(\frac{-(V + 40)} {10})} + $$ + + $$ + \beta_m(V) = 4.0 \exp(\frac{-(V + 65)} {18}) + $$ + + $$ + \alpha_h(V) = 0.07 \exp(\frac{-(V+65)}{20}) + $$ + + $$ + \beta_h(V) = \frac 1 {1 + \exp(\frac{-(V + 35)} {10})} + $$ + + $$ + \alpha_n(V) = \frac {0.01(V+55)}{1-\exp(-(V+55)/10)} + $$ + + $$ + \beta_n(V) = 0.125 \exp(\frac{-(V + 65)} {80}) + $$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + ENa : ArrayLike, default=50. * u.mV + Reversal potential of sodium. + gNa : ArrayLike, default=120. * u.msiemens + Maximum conductance of sodium channel. + EK : ArrayLike, default=-77. * u.mV + Reversal potential of potassium. + gK : ArrayLike, default=36. * u.msiemens + Maximum conductance of potassium channel. + EL : ArrayLike, default=-54.387 * u.mV + Reversal potential of leak channel. + gL : ArrayLike, default=0.03 * u.msiemens + Conductance of leak channel. + V_th : ArrayLike, default=20. * u.mV + Threshold of the membrane spike. + C : ArrayLike, default=1.0 * u.ufarad + Membrane capacitance. + V_initializer : Callable + Initializer for membrane potential. + m_initializer : Callable, optional + Initializer for m channel. If None, uses steady state. + h_initializer : Callable, optional + Initializer for h channel. If None, uses steady state. + n_initializer : Callable, optional + Initializer for n channel. If None, uses steady state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + m : HiddenState + Sodium activation variable. + h : HiddenState + Sodium inactivation variable. + n : HiddenState + Potassium activation variable. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an HH neuron layer with 10 neurons + >>> hh = brainpy.state.HH(10) + >>> + >>> # Initialize the state + >>> hh.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = hh.update(x=10.*u.uA) + + References + ---------- + .. [1] Hodgkin, Alan L., and Andrew F. Huxley. "A quantitative description + of membrane current and its application to conduction and excitation + in nerve." The Journal of physiology 117.4 (1952): 500. + .. [2] https://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model + """ + + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + ENa: ArrayLike = 50. * u.mV, + gNa: ArrayLike = 120. * u.msiemens, + EK: ArrayLike = -77. * u.mV, + gK: ArrayLike = 36. * u.msiemens, + EL: ArrayLike = -54.387 * u.mV, + gL: ArrayLike = 0.03 * u.msiemens, + V_th: ArrayLike = 20. * u.mV, + C: ArrayLike = 1.0 * u.ufarad, + V_initializer: Callable = braintools.init.Uniform(-70. * u.mV, -60. * u.mV), + m_initializer: Callable = None, + h_initializer: Callable = None, + n_initializer: Callable = None, + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.ENa = braintools.init.param(ENa, self.varshape) + self.EK = braintools.init.param(EK, self.varshape) + self.EL = braintools.init.param(EL, self.varshape) + self.gNa = braintools.init.param(gNa, self.varshape) + self.gK = braintools.init.param(gK, self.varshape) + self.gL = braintools.init.param(gL, self.varshape) + self.C = braintools.init.param(C, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + + # initializers + self.V_initializer = V_initializer + self.m_initializer = m_initializer + self.h_initializer = h_initializer + self.n_initializer = n_initializer + + def m_alpha(self, V): + return 1. / u.math.exprel(-(V + 40. * u.mV) / (10. * u.mV)) / u.ms + + def m_beta(self, V): + return 4.0 / u.ms * u.math.exp(-(V + 65. * u.mV) / (18. * u.mV)) + + def m_inf(self, V): + return self.m_alpha(V) / (self.m_alpha(V) + self.m_beta(V)) + + def h_alpha(self, V): + return 0.07 / u.ms * u.math.exp(-(V + 65. * u.mV) / (20. * u.mV)) + + def h_beta(self, V): + return 1. / u.ms / (1. + u.math.exp(-(V + 35. * u.mV) / (10. * u.mV))) + + def h_inf(self, V): + return self.h_alpha(V) / (self.h_alpha(V) + self.h_beta(V)) + + def n_alpha(self, V): + return 0.1 / u.ms / u.math.exprel(-(V + 55. * u.mV) / (10. * u.mV)) + + def n_beta(self, V): + return 0.125 / u.ms * u.math.exp(-(V + 65. * u.mV) / (80. * u.mV)) + + def n_inf(self, V): + return self.n_alpha(V) / (self.n_alpha(V) + self.n_beta(V)) + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + if self.m_initializer is None: + self.m = brainstate.HiddenState(self.m_inf(self.V.value)) + else: + self.m = brainstate.HiddenState(braintools.init.param(self.m_initializer, self.varshape, batch_size)) + if self.h_initializer is None: + self.h = brainstate.HiddenState(self.h_inf(self.V.value)) + else: + self.h = brainstate.HiddenState(braintools.init.param(self.h_initializer, self.varshape, batch_size)) + if self.n_initializer is None: + self.n = brainstate.HiddenState(self.n_inf(self.V.value)) + else: + self.n = brainstate.HiddenState(braintools.init.param(self.n_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + if self.m_initializer is None: + self.m.value = self.m_inf(self.V.value) + else: + self.m.value = braintools.init.param(self.m_initializer, self.varshape, batch_size) + if self.h_initializer is None: + self.h.value = self.h_inf(self.V.value) + else: + self.h.value = braintools.init.param(self.h_initializer, self.varshape, batch_size) + if self.n_initializer is None: + self.n.value = self.n_inf(self.V.value) + else: + self.n.value = braintools.init.param(self.n_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / self.V_th + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.uA): + last_V = self.V.value + last_m = self.m.value + last_h = self.h.value + last_n = self.n.value + + # Ionic currents + I_Na = (self.gNa * last_m ** 3 * last_h) * (last_V - self.ENa) + I_K = (self.gK * last_n ** 4) * (last_V - self.EK) + I_leak = self.gL * (last_V - self.EL) + + # Voltage dynamics + I_total = self.sum_current_inputs(x, last_V) + dV = lambda V: (-I_Na - I_K - I_leak + I_total) / self.C + + # Gating variable dynamics + dm = lambda m: self.m_alpha(last_V) * (1. - m) - self.m_beta(last_V) * m + dh = lambda h: self.h_alpha(last_V) * (1. - h) - self.h_beta(last_V) * h + dn = lambda n: self.n_alpha(last_V) * (1. - n) - self.n_beta(last_V) * n + + V = brainstate.nn.exp_euler_step(dV, last_V) + V = self.sum_delta_inputs(V) + m = brainstate.nn.exp_euler_step(dm, last_m) + h = brainstate.nn.exp_euler_step(dh, last_h) + n = brainstate.nn.exp_euler_step(dn, last_n) + + self.V.value = V + self.m.value = m + self.h.value = h + self.n.value = n + return self.get_spike(V) + + +class MorrisLecar(Neuron): + r"""The Morris-Lecar neuron model. + + **Model Descriptions** + + The Morris-Lecar model (Also known as :math:`I_{Ca}+I_K`-model) + is a two-dimensional "reduced" excitation model applicable to + systems having two non-inactivating voltage-sensitive conductances. + This model was named after Cathy Morris and Harold Lecar, who + derived it in 1981. Because it is two-dimensional, the Morris-Lecar + model is one of the favorite conductance-based models in computational neuroscience. + + The original form of the model employed an instantaneously + responding voltage-sensitive Ca2+ conductance for excitation and a delayed + voltage-dependent K+ conductance for recovery. The equations of the model are: + + $$ + \begin{aligned} + C\frac{dV}{dt} =& - g_{Ca} M_{\infty} (V - V_{Ca})- g_{K} W(V - V_{K}) - + g_{Leak} (V - V_{Leak}) + I_{ext} \\ + \frac{dW}{dt} =& \frac{W_{\infty}(V) - W}{ \tau_W(V)} + \end{aligned} + $$ + + Here, :math:`V` is the membrane potential, :math:`W` is the "recovery variable", + which is almost invariably the normalized :math:`K^+`-ion conductance, and + :math:`I_{ext}` is the applied current stimulus. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + V_Ca : ArrayLike, default=130. * u.mV + Equilibrium potential of Ca+. + g_Ca : ArrayLike, default=4.4 * u.msiemens + Maximum conductance of Ca+. + V_K : ArrayLike, default=-84. * u.mV + Equilibrium potential of K+. + g_K : ArrayLike, default=8. * u.msiemens + Maximum conductance of K+. + V_leak : ArrayLike, default=-60. * u.mV + Equilibrium potential of leak current. + g_leak : ArrayLike, default=2. * u.msiemens + Conductance of leak current. + C : ArrayLike, default=20. * u.ufarad + Membrane capacitance. + V1 : ArrayLike, default=-1.2 * u.mV + Potential at which M_inf = 0.5. + V2 : ArrayLike, default=18. * u.mV + Reciprocal of slope of voltage dependence of M_inf. + V3 : ArrayLike, default=2. * u.mV + Potential at which W_inf = 0.5. + V4 : ArrayLike, default=30. * u.mV + Reciprocal of slope of voltage dependence of W_inf. + phi : ArrayLike, default=0.04 / u.ms + Temperature factor. + V_th : ArrayLike, default=10. * u.mV + Spike threshold. + V_initializer : Callable + Initializer for membrane potential. + W_initializer : Callable + Initializer for recovery variable. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + W : HiddenState + Recovery variable. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a Morris-Lecar neuron layer with 10 neurons + >>> ml = brainpy.state.MorrisLecar(10) + >>> + >>> # Initialize the state + >>> ml.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = ml.update(x=100.*u.uA) + + References + ---------- + .. [1] Lecar, Harold. "Morris-lecar model." Scholarpedia 2.10 (2007): 1333. + .. [2] http://www.scholarpedia.org/article/Morris-Lecar_model + .. [3] https://en.wikipedia.org/wiki/Morris%E2%80%93Lecar_model + """ + + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + V_Ca: ArrayLike = 130. * u.mV, + g_Ca: ArrayLike = 4.4 * u.msiemens, + V_K: ArrayLike = -84. * u.mV, + g_K: ArrayLike = 8. * u.msiemens, + V_leak: ArrayLike = -60. * u.mV, + g_leak: ArrayLike = 2. * u.msiemens, + C: ArrayLike = 20. * u.ufarad, + V1: ArrayLike = -1.2 * u.mV, + V2: ArrayLike = 18. * u.mV, + V3: ArrayLike = 2. * u.mV, + V4: ArrayLike = 30. * u.mV, + phi: ArrayLike = 0.04 / u.ms, + V_th: ArrayLike = 10. * u.mV, + V_initializer: Callable = braintools.init.Uniform(-70. * u.mV, -60. * u.mV), + W_initializer: Callable = braintools.init.Constant(0.02), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.V_Ca = braintools.init.param(V_Ca, self.varshape) + self.g_Ca = braintools.init.param(g_Ca, self.varshape) + self.V_K = braintools.init.param(V_K, self.varshape) + self.g_K = braintools.init.param(g_K, self.varshape) + self.V_leak = braintools.init.param(V_leak, self.varshape) + self.g_leak = braintools.init.param(g_leak, self.varshape) + self.C = braintools.init.param(C, self.varshape) + self.V1 = braintools.init.param(V1, self.varshape) + self.V2 = braintools.init.param(V2, self.varshape) + self.V3 = braintools.init.param(V3, self.varshape) + self.V4 = braintools.init.param(V4, self.varshape) + self.phi = braintools.init.param(phi, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + + # initializers + self.V_initializer = V_initializer + self.W_initializer = W_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.W = brainstate.HiddenState(braintools.init.param(self.W_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.W.value = braintools.init.param(self.W_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / self.V_th + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.uA): + last_V = self.V.value + last_W = self.W.value + + # Steady states + M_inf = 0.5 * (1. + u.math.tanh((last_V - self.V1) / self.V2)) + W_inf = 0.5 * (1. + u.math.tanh((last_V - self.V3) / self.V4)) + tau_W = 1. / (self.phi * u.math.cosh((last_V - self.V3) / (2. * self.V4))) + + # Ionic currents + I_Ca = self.g_Ca * M_inf * (last_V - self.V_Ca) + I_K = self.g_K * last_W * (last_V - self.V_K) + I_leak = self.g_leak * (last_V - self.V_leak) + + # Dynamics + I_total = self.sum_current_inputs(x, last_V) + dV = lambda V: (-I_Ca - I_K - I_leak + I_total) / self.C + dW = lambda W: (W_inf - W) / tau_W + + V = brainstate.nn.exp_euler_step(dV, last_V) + V = self.sum_delta_inputs(V) + W = brainstate.nn.exp_euler_step(dW, last_W) + + self.V.value = V + self.W.value = W + return self.get_spike(V) + + +class WangBuzsakiHH(Neuron): + r"""Wang-Buzsaki model, an implementation of a modified Hodgkin-Huxley model. + + Each model is described by a single compartment and obeys the current balance equation: + + $$ + C_{m} \frac{d V}{d t}=-I_{\mathrm{Na}}-I_{\mathrm{K}}-I_{\mathrm{L}}+I_{\mathrm{app}} + $$ + + where :math:`C_{m}=1 \mu \mathrm{F} / \mathrm{cm}^{2}` and :math:`I_{\mathrm{app}}` is the + injected current (in :math:`\mu \mathrm{A} / \mathrm{cm}^{2}` ). The leak current + :math:`I_{\mathrm{L}}=g_{\mathrm{L}}\left(V-E_{\mathrm{L}}\right)` has a conductance + :math:`g_{\mathrm{L}}=0.1 \mathrm{mS} / \mathrm{cm}^{2}`. + + The spike-generating :math:`\mathrm{Na}^{+}` and :math:`\mathrm{K}^{+}` voltage-dependent ion + currents are of the Hodgkin-Huxley type. The transient sodium current + :math:`I_{\mathrm{Na}}=g_{\mathrm{Na}} m_{\infty}^{3} h\left(V-E_{\mathrm{Na}}\right)`, + where the activation variable :math:`m` is assumed fast and substituted by its steady-state + function :math:`m_{\infty}=\alpha_{m} /\left(\alpha_{m}+\beta_{m}\right)`; + :math:`\alpha_{m}(V)=-0.1(V+35) /(\exp (-0.1(V+35))-1)`, :math:`\beta_{m}(V)=4 \exp (-(V+60) / 18)`. + + The inactivation variable :math:`h` obeys: + + $$ + \frac{d h}{d t}=\phi\left(\alpha_{h}(1-h)-\beta_{h} h\right) + $$ + + where :math:`\alpha_{h}(V)=0.07 \exp (-(V+58) / 20)` and + :math:`\beta_{h}(V)=1 /(\exp (-0.1(V+28)) +1)`. + + The delayed rectifier :math:`I_{\mathrm{K}}=g_{\mathrm{K}} n^{4}\left(V-E_{\mathrm{K}}\right)`, + where the activation variable :math:`n` obeys: + + $$ + \frac{d n}{d t}=\phi\left(\alpha_{n}(1-n)-\beta_{n} n\right) + $$ + + with :math:`\alpha_{n}(V)=-0.01(V+34) /(\exp (-0.1(V+34))-1)` and + :math:`\beta_{n}(V)=0.125\exp (-(V+44) / 80)`. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + ENa : ArrayLike, default=55. * u.mV + Reversal potential of sodium. + gNa : ArrayLike, default=35. * u.msiemens + Maximum conductance of sodium channel. + EK : ArrayLike, default=-90. * u.mV + Reversal potential of potassium. + gK : ArrayLike, default=9. * u.msiemens + Maximum conductance of potassium channel. + EL : ArrayLike, default=-65. * u.mV + Reversal potential of leak channel. + gL : ArrayLike, default=0.1 * u.msiemens + Conductance of leak channel. + V_th : ArrayLike, default=20. * u.mV + Threshold of the membrane spike. + phi : ArrayLike, default=5.0 + Temperature regulator constant. + C : ArrayLike, default=1.0 * u.ufarad + Membrane capacitance. + V_initializer : Callable + Initializer for membrane potential. + h_initializer : Callable + Initializer for h channel. + n_initializer : Callable + Initializer for n channel. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + h : HiddenState + Sodium inactivation variable. + n : HiddenState + Potassium activation variable. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a WangBuzsakiHH neuron layer with 10 neurons + >>> wb = brainpy.state.WangBuzsakiHH(10) + >>> + >>> # Initialize the state + >>> wb.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = wb.update(x=1.*u.uA) + + References + ---------- + .. [1] Wang, X.J. and Buzsaki, G., (1996) Gamma oscillation by synaptic + inhibition in a hippocampal interneuronal network model. Journal of + neuroscience, 16(20), pp.6402-6413. + """ + + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + ENa: ArrayLike = 55. * u.mV, + gNa: ArrayLike = 35. * u.msiemens, + EK: ArrayLike = -90. * u.mV, + gK: ArrayLike = 9. * u.msiemens, + EL: ArrayLike = -65. * u.mV, + gL: ArrayLike = 0.1 * u.msiemens, + V_th: ArrayLike = 20. * u.mV, + phi: ArrayLike = 5.0, + C: ArrayLike = 1.0 * u.ufarad, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + h_initializer: Callable = braintools.init.Constant(0.6), + n_initializer: Callable = braintools.init.Constant(0.32), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.ENa = braintools.init.param(ENa, self.varshape) + self.EK = braintools.init.param(EK, self.varshape) + self.EL = braintools.init.param(EL, self.varshape) + self.gNa = braintools.init.param(gNa, self.varshape) + self.gK = braintools.init.param(gK, self.varshape) + self.gL = braintools.init.param(gL, self.varshape) + self.phi = braintools.init.param(phi, self.varshape) + self.C = braintools.init.param(C, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + + # initializers + self.V_initializer = V_initializer + self.h_initializer = h_initializer + self.n_initializer = n_initializer + + def m_inf(self, V): + alpha = 1. / u.math.exprel(-0.1 * (V + 35. * u.mV) / u.mV) / u.ms + beta = 4. / u.ms * u.math.exp(-(V + 60. * u.mV) / (18. * u.mV)) + return alpha / (alpha + beta) + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.h = brainstate.HiddenState(braintools.init.param(self.h_initializer, self.varshape, batch_size)) + self.n = brainstate.HiddenState(braintools.init.param(self.n_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.h.value = braintools.init.param(self.h_initializer, self.varshape, batch_size) + self.n.value = braintools.init.param(self.n_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / self.V_th + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.uA): + last_V = self.V.value + last_h = self.h.value + last_n = self.n.value + + # Ionic currents + m_inf_val = self.m_inf(last_V) + I_Na = self.gNa * m_inf_val ** 3 * last_h * (last_V - self.ENa) + I_K = self.gK * last_n ** 4 * (last_V - self.EK) + I_L = self.gL * (last_V - self.EL) + + # Voltage dynamics + I_total = self.sum_current_inputs(x, last_V) + dV = lambda V: (-I_Na - I_K - I_L + I_total) / self.C + + # Gating variable dynamics + h_alpha = 0.07 / u.ms * u.math.exp(-(last_V + 58. * u.mV) / (20. * u.mV)) + h_beta = 1. / u.ms / (u.math.exp(-0.1 * (last_V + 28. * u.mV) / u.mV) + 1.) + dh = lambda h: self.phi * (h_alpha * (1. - h) - h_beta * h) + + n_alpha = 1. / u.ms / u.math.exprel(-0.1 * (last_V + 34. * u.mV) / u.mV) + n_beta = 0.125 / u.ms * u.math.exp(-(last_V + 44. * u.mV) / (80. * u.mV)) + dn = lambda n: self.phi * (n_alpha * (1. - n) - n_beta * n) + + V = brainstate.nn.exp_euler_step(dV, last_V) + V = self.sum_delta_inputs(V) + h = brainstate.nn.exp_euler_step(dh, last_h) + n = brainstate.nn.exp_euler_step(dn, last_n) + + self.V.value = V + self.h.value = h + self.n.value = n + return self.get_spike(V) diff --git a/brainpy/state/_hh_test.py b/brainpy/state/_hh_test.py new file mode 100644 index 00000000..94930174 --- /dev/null +++ b/brainpy/state/_hh_test.py @@ -0,0 +1,303 @@ +# Copyright 2024 BrainX Ecosystem Limited. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# -*- coding: utf-8 -*- + + +import unittest + +import brainstate +import braintools +import brainunit as u +import jax +import jax.numpy as jnp + +from brainpy.state import HH, MorrisLecar, WangBuzsakiHH + + +class TestHHNeuron(unittest.TestCase): + def setUp(self): + self.in_size = 10 + self.batch_size = 5 + self.time_steps = 100 + self.dt = 0.01 * u.ms + + def generate_input(self): + return brainstate.random.randn(self.time_steps, self.batch_size, self.in_size) * u.uA + + def test_hh_neuron(self): + with brainstate.environ.context(dt=self.dt): + neuron = HH(self.in_size) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.m.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.h.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.n.value.shape, (self.batch_size, self.in_size)) + + def test_morris_lecar_neuron(self): + with brainstate.environ.context(dt=self.dt): + neuron = MorrisLecar(self.in_size) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.W.value.shape, (self.batch_size, self.in_size)) + + def test_wang_buzsaki_hh_neuron(self): + with brainstate.environ.context(dt=self.dt): + neuron = WangBuzsakiHH(self.in_size) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.h.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.n.value.shape, (self.batch_size, self.in_size)) + + def test_spike_function(self): + for NeuronClass in [HH, MorrisLecar, WangBuzsakiHH]: + neuron = NeuronClass(self.in_size) + neuron.init_state() + v = jnp.linspace(-80, 40, self.in_size) * u.mV + spikes = neuron.get_spike(v) + self.assertTrue(jnp.all((spikes >= 0) & (spikes <= 1))) + + def test_soft_reset(self): + for NeuronClass in [HH, MorrisLecar, WangBuzsakiHH]: + neuron = NeuronClass(self.in_size, spk_reset='soft') + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + # Check that voltage doesn't exceed threshold too much + self.assertTrue(jnp.all(neuron.V.value <= neuron.V_th + 20 * u.mV)) + + def test_hard_reset(self): + for NeuronClass in [HH, MorrisLecar, WangBuzsakiHH]: + neuron = NeuronClass(self.in_size, spk_reset='hard') + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + # Just check that it runs without error + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + def test_detach_spike(self): + for NeuronClass in [HH, MorrisLecar, WangBuzsakiHH]: + neuron = NeuronClass(self.in_size) + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertFalse(jax.tree_util.tree_leaves(out)[0].aval.weak_type) + + def test_keep_size(self): + in_size = (2, 3) + for NeuronClass in [HH, MorrisLecar, WangBuzsakiHH]: + neuron = NeuronClass(in_size) + self.assertEqual(neuron.in_size, in_size) + self.assertEqual(neuron.out_size, in_size) + + inputs = brainstate.random.randn(self.time_steps, self.batch_size, *in_size) * u.uA + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, *in_size)) + + def test_hh_gating_variables(self): + # Test that gating variables are properly initialized and updated + neuron = HH(self.in_size) + neuron.init_state(self.batch_size) + + # Check initial values are in valid range [0, 1] + self.assertTrue(jnp.all((neuron.m.value >= 0) & (neuron.m.value <= 1))) + self.assertTrue(jnp.all((neuron.h.value >= 0) & (neuron.h.value <= 1))) + self.assertTrue(jnp.all((neuron.n.value >= 0) & (neuron.n.value <= 1))) + + # Run for some time steps + inputs = self.generate_input() + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(20): + out = call(inputs[t]) + + # Gating variables should still be in valid range + self.assertTrue(jnp.all((neuron.m.value >= 0) & (neuron.m.value <= 1))) + self.assertTrue(jnp.all((neuron.h.value >= 0) & (neuron.h.value <= 1))) + self.assertTrue(jnp.all((neuron.n.value >= 0) & (neuron.n.value <= 1))) + + def test_hh_alpha_beta_functions(self): + # Test that alpha and beta functions return positive values + neuron = HH(self.in_size) + neuron.init_state() + + V_test = jnp.linspace(-80, 40, self.in_size) * u.mV + + m_alpha = neuron.m_alpha(V_test) + m_beta = neuron.m_beta(V_test) + h_alpha = neuron.h_alpha(V_test) + h_beta = neuron.h_beta(V_test) + n_alpha = neuron.n_alpha(V_test) + n_beta = neuron.n_beta(V_test) + + # All rate constants should be positive + if hasattr(m_alpha, 'mantissa'): + self.assertTrue(jnp.all(m_alpha.mantissa > 0)) + self.assertTrue(jnp.all(m_beta.mantissa > 0)) + self.assertTrue(jnp.all(h_alpha.mantissa > 0)) + self.assertTrue(jnp.all(h_beta.mantissa > 0)) + self.assertTrue(jnp.all(n_alpha.mantissa > 0)) + self.assertTrue(jnp.all(n_beta.mantissa > 0)) + else: + self.assertTrue(jnp.all(m_alpha > 0)) + self.assertTrue(jnp.all(m_beta > 0)) + self.assertTrue(jnp.all(h_alpha > 0)) + self.assertTrue(jnp.all(h_beta > 0)) + self.assertTrue(jnp.all(n_alpha > 0)) + self.assertTrue(jnp.all(n_beta > 0)) + + def test_morris_lecar_steady_states(self): + # Test that steady-state functions return values in valid range + neuron = MorrisLecar(self.in_size) + neuron.init_state() + + V_test = jnp.linspace(-100, 50, self.in_size) * u.mV + + # Manually compute steady states + M_inf = 0.5 * (1. + u.math.tanh((V_test - neuron.V1) / neuron.V2)) + W_inf = 0.5 * (1. + u.math.tanh((V_test - neuron.V3) / neuron.V4)) + + # Steady states should be in [0, 1] + if hasattr(M_inf, 'mantissa'): + self.assertTrue(jnp.all((M_inf.mantissa >= 0) & (M_inf.mantissa <= 1))) + self.assertTrue(jnp.all((W_inf.mantissa >= 0) & (W_inf.mantissa <= 1))) + else: + self.assertTrue(jnp.all((M_inf >= 0) & (M_inf <= 1))) + self.assertTrue(jnp.all((W_inf >= 0) & (W_inf <= 1))) + + def test_wang_buzsaki_m_inf(self): + # Test that m_inf is properly computed and in valid range + neuron = WangBuzsakiHH(self.in_size) + neuron.init_state() + + V_test = jnp.linspace(-80, 40, self.in_size) * u.mV + m_inf = neuron.m_inf(V_test) + + # m_inf should be in [0, 1] + if hasattr(m_inf, 'mantissa'): + self.assertTrue(jnp.all((m_inf.mantissa >= 0) & (m_inf.mantissa <= 1))) + else: + self.assertTrue(jnp.all((m_inf >= 0) & (m_inf <= 1))) + + def test_different_parameters(self): + # Test HH with different conductance values + hh_custom = HH( + self.in_size, + ENa=50. * u.mV, + gNa=100. * u.msiemens, + EK=-80. * u.mV, + gK=30. * u.msiemens + ) + hh_custom.init_state(self.batch_size) + self.assertEqual(hh_custom.ENa, 50. * u.mV) + self.assertEqual(hh_custom.gNa, 100. * u.msiemens) + + # Test MorrisLecar with different parameters + ml_custom = MorrisLecar( + self.in_size, + V_Ca=120. * u.mV, + g_Ca=4.0 * u.msiemens, + phi=0.05 / u.ms + ) + ml_custom.init_state(self.batch_size) + self.assertEqual(ml_custom.V_Ca, 120. * u.mV) + self.assertEqual(ml_custom.phi, 0.05 / u.ms) + + # Test WangBuzsakiHH with different phi + wb_custom = WangBuzsakiHH( + self.in_size, + phi=10.0 + ) + wb_custom.init_state(self.batch_size) + if hasattr(wb_custom.phi, 'mantissa'): + self.assertEqual(float(wb_custom.phi.mantissa), 10.0) + else: + self.assertEqual(float(wb_custom.phi), 10.0) + + def test_ionic_currents(self): + # Test that ionic currents are computed + neuron = HH(self.in_size) + neuron.init_state(self.batch_size) + + # Run one update + inputs = jnp.ones((self.batch_size, self.in_size)) * 10. * u.uA + with brainstate.environ.context(dt=self.dt): + out = neuron.update(inputs) + + # Check that state variables have changed (indicating currents were applied) + initial_V = braintools.init.param(neuron.V_initializer, neuron.varshape, self.batch_size) + if hasattr(initial_V, 'mantissa'): + self.assertFalse(jnp.allclose(neuron.V.value.mantissa, initial_V.mantissa)) + else: + self.assertFalse(jnp.allclose(neuron.V.value, initial_V)) + + +if __name__ == '__main__': + unittest.main() diff --git a/brainpy/state/_izhikevich.py b/brainpy/state/_izhikevich.py new file mode 100644 index 00000000..7ac8a4f4 --- /dev/null +++ b/brainpy/state/_izhikevich.py @@ -0,0 +1,407 @@ +# Copyright 2024 BrainX Ecosystem Limited. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# -*- coding: utf-8 -*- + +from typing import Callable + +import brainstate +import braintools +import brainunit as u +import jax +from brainstate.typing import ArrayLike, Size + +from ._base import Neuron + +__all__ = [ + 'Izhikevich', 'IzhikevichRef', +] + + +class Izhikevich(Neuron): + r"""Izhikevich neuron model. + + This class implements the Izhikevich neuron model, a two-dimensional spiking neuron + model that can reproduce a wide variety of neuronal firing patterns observed in + biological neurons. The model combines computational efficiency with biological + plausibility through a quadratic voltage dynamics and a linear recovery variable. + + The model is characterized by the following differential equations: + + $$ + \frac{dV}{dt} = 0.04 V^2 + 5V + 140 - u + I(t) + $$ + + $$ + \frac{du}{dt} = a(bV - u) + $$ + + Spike condition: + If $V \geq V_{th}$: emit spike, set $V = c$ and $u = u + d$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + a : ArrayLike, default=0.02 / u.ms + Time scale of the recovery variable u. Smaller values result in slower recovery. + b : ArrayLike, default=0.2 / u.ms + Sensitivity of the recovery variable u to the membrane potential V. + c : ArrayLike, default=-65. * u.mV + After-spike reset value of the membrane potential. + d : ArrayLike, default=8. * u.mV / u.ms + After-spike increment of the recovery variable u. + V_th : ArrayLike, default=30. * u.mV + Spike threshold voltage. + V_initializer : Callable + Initializer for the membrane potential state. + u_initializer : Callable + Initializer for the recovery variable state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the non-differentiable spike generation. + spk_reset : str, default='hard' + Reset mechanism after spike generation: + - 'soft': subtract threshold V = V - V_th + - 'hard': strict reset using stop_gradient + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + u : HiddenState + Recovery variable. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an Izhikevich neuron layer with 10 neurons + >>> izh = brainpy.state.Izhikevich(10) + >>> + >>> # Initialize the state + >>> izh.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = izh.update(x=10.*u.mV/u.ms) + + Notes + ----- + - The quadratic term in the voltage equation (0.04*V^2) provides a sharp spike + upstroke similar to biological neurons. + - Different combinations of parameters (a, b, c, d) can reproduce various neuronal + behaviors including regular spiking, intrinsically bursting, chattering, and + fast spiking. + - The model uses a hard reset mechanism where V is set to c and u is incremented + by d when a spike occurs. + - Parameter ranges: a ∈ [0.01, 0.1], b ∈ [0.2, 0.3], c ∈ [-65, -50], d ∈ [0.1, 10] + + References + ---------- + .. [1] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions + on neural networks, 14(6), 1569-1572. + .. [2] Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons?. + IEEE transactions on neural networks, 15(5), 1063-1070. + """ + + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + a: ArrayLike = 0.02 / u.ms, + b: ArrayLike = 0.2 / u.ms, + c: ArrayLike = -65. * u.mV, + d: ArrayLike = 8. * u.mV / u.ms, + V_th: ArrayLike = 30. * u.mV, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + u_initializer: Callable = braintools.init.Constant(0. * u.mV / u.ms), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'hard', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.c = braintools.init.param(c, self.varshape) + self.d = braintools.init.param(d, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + + # pre-computed coefficients for quadratic equation + self.p1 = 0.04 / (u.ms * u.mV) + self.p2 = 5. / u.ms + self.p3 = 140. * u.mV / u.ms + + # initializers + self.V_initializer = V_initializer + self.u_initializer = u_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.u = brainstate.HiddenState(braintools.init.param(self.u_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.u.value = braintools.init.param(self.u_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / self.V_th + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mV / u.ms): + last_v = self.V.value + last_u = self.u.value + last_spk = self.get_spike(last_v) + + # Izhikevich uses hard reset: V → c, u → u + d + V = u.math.where(last_spk > 0., self.c, last_v) + u_val = last_u + self.d * last_spk + + # voltage dynamics: dV/dt = 0.04*V^2 + 5*V + 140 - u + I + def dv(v): + I_total = self.sum_current_inputs(x, v) + return self.p1 * v * v + self.p2 * v + self.p3 - u_val + I_total + + # recovery dynamics: du/dt = a(bV - u) + def du(u_): + return self.a * (self.b * V - u_) + + V = brainstate.nn.exp_euler_step(dv, V) + V = self.sum_delta_inputs(V) + u_val = brainstate.nn.exp_euler_step(du, u_val) + + self.V.value = V + self.u.value = u_val + return self.get_spike(V) + + +class IzhikevichRef(Neuron): + r"""Izhikevich neuron model with refractory period. + + This class implements the Izhikevich neuron model with an absolute refractory period. + During the refractory period after a spike, the neuron cannot fire regardless of input, + which better captures the behavior of biological neurons that exhibit a recovery period + after action potential generation. + + The model is characterized by the following equations: + + When not in refractory period: + + $$ + \frac{dV}{dt} = 0.04 V^2 + 5V + 140 - u + I(t) + $$ + + $$ + \frac{du}{dt} = a(bV - u) + $$ + + During refractory period: + + $$ + V = c, \quad u = u + $$ + + Spike condition: + If $V \geq V_{th}$ and not in refractory period: emit spike, set $V = c$, $u = u + d$, + and enter refractory period for $\tau_{ref}$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + a : ArrayLike, default=0.02 / u.ms + Time scale of the recovery variable u. + b : ArrayLike, default=0.2 / u.ms + Sensitivity of the recovery variable u to the membrane potential V. + c : ArrayLike, default=-65. * u.mV + After-spike reset value of the membrane potential. + d : ArrayLike, default=8. * u.mV / u.ms + After-spike increment of the recovery variable u. + V_th : ArrayLike, default=30. * u.mV + Spike threshold voltage. + tau_ref : ArrayLike, default=0. * u.ms + Refractory period duration. + V_initializer : Callable + Initializer for the membrane potential state. + u_initializer : Callable + Initializer for the recovery variable state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the non-differentiable spike generation. + spk_reset : str, default='hard' + Reset mechanism after spike generation. + ref_var : bool, default=False + Whether to expose a boolean refractory state variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + u : HiddenState + Recovery variable. + last_spike_time : ShortTermState + Time of the last spike, used to implement refractory period. + refractory : HiddenState + Neuron refractory state (if ref_var=True). + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an IzhikevichRef neuron layer with 10 neurons + >>> izh_ref = brainpy.state.IzhikevichRef(10, tau_ref=2.*u.ms) + >>> + >>> # Initialize the state + >>> izh_ref.init_state(batch_size=1) + >>> + >>> # Generate inputs and run simulation + >>> time_steps = 100 + >>> inputs = brainstate.random.randn(time_steps, 1, 10) * u.mV / u.ms + >>> + >>> with brainstate.environ.context(dt=0.1 * u.ms): + >>> for t in range(time_steps): + >>> with brainstate.environ.context(t=t*0.1*u.ms): + >>> spikes = izh_ref.update(x=inputs[t]) + + Notes + ----- + - The refractory period is implemented by tracking the time of the last spike + and preventing membrane potential updates if the elapsed time is less than tau_ref. + - During the refractory period, the membrane potential remains at the reset value c + regardless of input current strength. + - Refractory periods prevent high-frequency repetitive firing and are critical + for realistic neural dynamics. + - The simulation environment time variable 't' is used to track the refractory state. + + References + ---------- + .. [1] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions + on neural networks, 14(6), 1569-1572. + .. [2] Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons?. + IEEE transactions on neural networks, 15(5), 1063-1070. + """ + + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + a: ArrayLike = 0.02 / u.ms, + b: ArrayLike = 0.2 / u.ms, + c: ArrayLike = -65. * u.mV, + d: ArrayLike = 8. * u.mV / u.ms, + V_th: ArrayLike = 30. * u.mV, + tau_ref: ArrayLike = 0. * u.ms, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + u_initializer: Callable = braintools.init.Constant(0. * u.mV / u.ms), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'hard', + ref_var: bool = False, + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.c = braintools.init.param(c, self.varshape) + self.d = braintools.init.param(d, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + + # pre-computed coefficients for quadratic equation + self.p1 = 0.04 / (u.ms * u.mV) + self.p2 = 5. / u.ms + self.p3 = 140. * u.mV / u.ms + + # initializers + self.V_initializer = V_initializer + self.u_initializer = u_initializer + self.ref_var = ref_var + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.u = brainstate.HiddenState(braintools.init.param(self.u_initializer, self.varshape, batch_size)) + self.last_spike_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + if self.ref_var: + self.refractory = brainstate.HiddenState( + braintools.init.param(braintools.init.Constant(False), self.varshape, batch_size) + ) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.u.value = braintools.init.param(self.u_initializer, self.varshape, batch_size) + self.last_spike_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size + ) + if self.ref_var: + self.refractory.value = braintools.init.param( + braintools.init.Constant(False), self.varshape, batch_size + ) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / self.V_th + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mV / u.ms): + t = brainstate.environ.get('t') + last_v = self.V.value + last_u = self.u.value + last_spk = self.get_spike(last_v) + + # Izhikevich uses hard reset: V → c, u → u + d + v_reset = u.math.where(last_spk > 0., self.c, last_v) + u_reset = last_u + self.d * last_spk + + # voltage dynamics: dV/dt = 0.04*V^2 + 5*V + 140 - u + I + def dv(v): + I_total = self.sum_current_inputs(x, v) + return self.p1 * v * v + self.p2 * v + self.p3 - u_reset + I_total + + # recovery dynamics: du/dt = a(bV - u) + def du(u_): + return self.a * (self.b * V_candidate - u_) + + V_candidate = brainstate.nn.exp_euler_step(dv, v_reset) + V_candidate = self.sum_delta_inputs(V_candidate) + u_candidate = brainstate.nn.exp_euler_step(du, u_reset) + + # apply refractory period + refractory = (t - self.last_spike_time.value) < self.tau_ref + self.V.value = u.math.where(refractory, v_reset, V_candidate) + self.u.value = u.math.where(refractory, u_reset, u_candidate) + + # spike time evaluation + spike_cond = self.V.value >= self.V_th + self.last_spike_time.value = jax.lax.stop_gradient( + u.math.where(spike_cond, t, self.last_spike_time.value) + ) + if self.ref_var: + self.refractory.value = jax.lax.stop_gradient( + u.math.logical_or(refractory, spike_cond) + ) + return self.get_spike() diff --git a/brainpy/state/_izhikevich_test.py b/brainpy/state/_izhikevich_test.py new file mode 100644 index 00000000..c6cd8a23 --- /dev/null +++ b/brainpy/state/_izhikevich_test.py @@ -0,0 +1,291 @@ +# Copyright 2024 BrainX Ecosystem Limited. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# -*- coding: utf-8 -*- + + +import unittest + +import brainstate +import brainunit as u +import jax +import jax.numpy as jnp + +from brainpy.state import Izhikevich, IzhikevichRef + + +class TestIzhikevichNeuron(unittest.TestCase): + def setUp(self): + self.in_size = 10 + self.batch_size = 5 + self.time_steps = 100 + self.dt = 0.1 * u.ms + + def generate_input(self): + return brainstate.random.randn(self.time_steps, self.batch_size, self.in_size) * u.mV / u.ms + + def test_izhikevich_neuron(self): + with brainstate.environ.context(dt=self.dt): + neuron = Izhikevich(self.in_size) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.u.value.shape, (self.batch_size, self.in_size)) + + def test_izhikevich_ref_neuron(self): + tau_ref = 2.0 * u.ms + neuron = IzhikevichRef(self.in_size, tau_ref=tau_ref) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau_ref, tau_ref) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.u.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + + def test_izhikevich_ref_with_ref_var(self): + tau_ref = 2.0 * u.ms + ref_var = True + neuron = IzhikevichRef(self.in_size, tau_ref=tau_ref, ref_var=ref_var) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.ref_var, ref_var) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Check refractory variable + if neuron.ref_var: + self.assertEqual(neuron.refractory.value.shape, (self.batch_size, self.in_size)) + + def test_spike_function(self): + for NeuronClass in [Izhikevich, IzhikevichRef]: + neuron = NeuronClass(self.in_size) + neuron.init_state() + v = jnp.linspace(-80, 40, self.in_size) * u.mV + spikes = neuron.get_spike(v) + self.assertTrue(jnp.all((spikes >= 0) & (spikes <= 1))) + + def test_soft_reset(self): + for NeuronClass in [Izhikevich, IzhikevichRef]: + neuron = NeuronClass(self.in_size, spk_reset='soft') + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + # For Izhikevich model, soft reset still applies hard reset logic + # So we just check that V doesn't exceed V_th significantly + self.assertTrue(jnp.all(neuron.V.value <= neuron.V_th + 10 * u.mV)) + + def test_hard_reset(self): + for NeuronClass in [Izhikevich, IzhikevichRef]: + neuron = NeuronClass(self.in_size, spk_reset='hard') + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + # For Izhikevich, after spike V should be reset to c + # Check that V is either below threshold or near reset value + above_c = neuron.V.value >= (neuron.c - 5 * u.mV) + below_th = neuron.V.value < neuron.V_th + self.assertTrue(jnp.all(above_c | below_th)) + + def test_detach_spike(self): + for NeuronClass in [Izhikevich, IzhikevichRef]: + neuron = NeuronClass(self.in_size) + inputs = self.generate_input() + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + self.assertFalse(jax.tree_util.tree_leaves(out)[0].aval.weak_type) + + def test_keep_size(self): + in_size = (2, 3) + for NeuronClass in [Izhikevich, IzhikevichRef]: + neuron = NeuronClass(in_size) + self.assertEqual(neuron.in_size, in_size) + self.assertEqual(neuron.out_size, in_size) + + inputs = brainstate.random.randn(self.time_steps, self.batch_size, *in_size) * u.mV / u.ms + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, *in_size)) + + def test_different_parameters(self): + # Test regular spiking (RS) parameters + rs_neuron = Izhikevich( + self.in_size, + a=0.02 / u.ms, + b=0.2 / u.ms, + c=-65. * u.mV, + d=8. * u.mV / u.ms + ) + rs_neuron.init_state(self.batch_size) + self.assertEqual(rs_neuron.a, 0.02 / u.ms) + self.assertEqual(rs_neuron.b, 0.2 / u.ms) + + # Test intrinsically bursting (IB) parameters + ib_neuron = Izhikevich( + self.in_size, + a=0.02 / u.ms, + b=0.2 / u.ms, + c=-55. * u.mV, + d=4. * u.mV / u.ms + ) + ib_neuron.init_state(self.batch_size) + self.assertEqual(ib_neuron.c, -55. * u.mV) + self.assertEqual(ib_neuron.d, 4. * u.mV / u.ms) + + # Test chattering (CH) parameters + ch_neuron = Izhikevich( + self.in_size, + a=0.02 / u.ms, + b=0.2 / u.ms, + c=-50. * u.mV, + d=2. * u.mV / u.ms + ) + ch_neuron.init_state(self.batch_size) + self.assertEqual(ch_neuron.c, -50. * u.mV) + + # Test fast spiking (FS) parameters + fs_neuron = Izhikevich( + self.in_size, + a=0.1 / u.ms, + b=0.2 / u.ms, + c=-65. * u.mV, + d=2. * u.mV / u.ms + ) + fs_neuron.init_state(self.batch_size) + self.assertEqual(fs_neuron.a, 0.1 / u.ms) + + def test_refractory_period_effectiveness(self): + # Test that refractory period actually prevents firing + tau_ref = 5.0 * u.ms + neuron = IzhikevichRef(self.in_size, tau_ref=tau_ref) + neuron.init_state(self.batch_size) + + # Strong constant input to encourage firing + strong_input = jnp.ones((self.batch_size, self.in_size)) * 20. * u.mV / u.ms + + spike_times = [] + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t * self.dt): + out = call(strong_input) + if jnp.any(out > 0): + spike_times.append(t * self.dt) + + # Check that consecutive spikes are separated by at least tau_ref + if len(spike_times) > 1: + for i in range(len(spike_times) - 1): + time_diff = spike_times[i + 1] - spike_times[i] + # Allow small numerical errors + self.assertGreaterEqual(time_diff.to_value(u.ms), (tau_ref - 0.5 * self.dt).to_value(u.ms)) + + def test_quadratic_dynamics(self): + # Test that the quadratic term in voltage dynamics is working + neuron = Izhikevich(self.in_size) + neuron.init_state(1) + + # Set initial conditions + V_low = -70. * u.mV + V_high = -50. * u.mV + + # Check that dV/dt has quadratic relationship with V + # At low V, dV/dt should be more negative + # At high V, dV/dt should be less negative or positive + + # This is a qualitative test to ensure the quadratic term is present + # coefficient p1 should be positive for upward parabola + # p1 = 0.04 / (ms * mV), just check it's set and positive + self.assertIsNotNone(neuron.p1) + # Extract the mantissa value for comparison + if hasattr(neuron.p1, 'mantissa'): + self.assertGreater(float(neuron.p1.mantissa), 0) + else: + self.assertGreater(float(neuron.p1), 0) + + def test_recovery_variable_dynamics(self): + # Test that recovery variable u properly tracks and affects V + neuron = Izhikevich(self.in_size) + neuron.init_state(self.batch_size) + + initial_u = neuron.u.value.mantissa.copy() + + # Run for some time steps with moderate input + moderate_input = jnp.ones((self.batch_size, self.in_size)) * 5. * u.mV / u.ms + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(20): + call(moderate_input) + + # u should change from initial value + self.assertFalse(jnp.allclose(neuron.u.value.mantissa, initial_u, rtol=0.01)) + + # After a spike, u should increase by d + # (This is implicitly tested in the spike generation tests) + + +if __name__ == '__main__': + unittest.main() diff --git a/brainpy/state/_lif.py b/brainpy/state/_lif.py index 0a4eb164..c6983700 100644 --- a/brainpy/state/_lif.py +++ b/brainpy/state/_lif.py @@ -26,7 +26,8 @@ from ._base import Neuron __all__ = [ - 'IF', 'LIF', 'LIFRef', 'ALIF', + 'IF', 'LIF', 'ExpIF', 'ExpIFRef', 'AdExIF', 'AdExIFRef', 'LIFRef', 'ALIF', + 'QuaIF', 'AdQuaIF', 'AdQuaIFRef', 'Gif', 'GifRef', ] @@ -207,12 +208,12 @@ class LIF(Neuron): Examples -------- - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> >>> # Create a LIF neuron layer with 10 neurons - >>> lif = brainpy.LIF(10, tau=10*u.ms, V_th=0.8*u.mV) + >>> lif = brainpy.state.LIF(10, tau=10*u.ms, V_th=0.8*u.mV) >>> >>> # Initialize the state >>> lif.init_state(batch_size=1) @@ -274,9 +275,9 @@ def get_spike(self, V: ArrayLike = None): def update(self, x=0. * u.mA): last_v = self.V.value - lst_spk = self.get_spike(last_v) + last_spk = self.get_spike(last_v) V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) - V = last_v - (V_th - self.V_reset) * lst_spk + V = last_v - (V_th - self.V_reset) * last_spk # membrane potential dv = lambda v: (-v + self.V_rest + self.R * self.sum_current_inputs(x, v)) / self.tau V = brainstate.nn.exp_euler_step(dv, V) @@ -285,28 +286,22 @@ def update(self, x=0. * u.mA): return self.get_spike(V) -class LIFRef(Neuron): - r"""Leaky Integrate-and-Fire neuron model with refractory period. - - This class implements a Leaky Integrate-and-Fire neuron model that includes a - refractory period after spiking, during which the neuron cannot fire regardless - of input. This better captures the behavior of biological neurons that exhibit - a recovery period after action potential generation. +class ExpIF(Neuron): + r"""Exponential Integrate-and-Fire (ExpIF) neuron model. - The model is characterized by the following equations: + This model augments the LIF neuron by adding an exponential spike-initiation + term, which provides a smooth approximation of the action potential onset + and improves biological plausibility for cortical pyramidal cells. - When not in refractory period: - $$ - \tau \frac{dV}{dt} = -(V - V_{rest}) + R \cdot I(t) - $$ + The membrane potential dynamics follow: - During refractory period: $$ - V = V_{reset} + \tau \frac{dV}{dt} = -(V - V_{rest}) + \Delta_T \exp\left(\frac{V - V_T}{\Delta_T}\right) + R \cdot I(t) $$ Spike condition: - If $V \geq V_{th}$: emit spike, set $V = V_{reset}$, and enter refractory period for $\tau_{ref}$ + If $V \geq V_{th}$: emit spike and reset $V = V_{reset}$ (hard reset) or + $V = V - (V_{th} - V_{reset})$ (soft reset). Parameters ---------- @@ -314,24 +309,24 @@ class LIFRef(Neuron): Size of the input to the neuron. R : ArrayLike, default=1. * u.ohm Membrane resistance. - tau : ArrayLike, default=5. * u.ms + tau : ArrayLike, default=10. * u.ms Membrane time constant. - tau_ref : ArrayLike, default=5. * u.ms - Refractory period duration. - V_th : ArrayLike, default=1. * u.mV - Firing threshold voltage. - V_reset : ArrayLike, default=0. * u.mV + V_th : ArrayLike, default=-30. * u.mV + Numerical firing threshold voltage. + V_reset : ArrayLike, default=-68. * u.mV Reset voltage after spike. - V_rest : ArrayLike, default=0. * u.mV + V_rest : ArrayLike, default=-65. * u.mV Resting membrane potential. + V_T : ArrayLike, default=-59.9 * u.mV + Threshold potential of the exponential term. + delta_T : ArrayLike, default=3.48 * u.mV + Spike slope factor controlling the sharpness of spike initiation. V_initializer : Callable Initializer for the membrane potential state. spk_fun : Callable, default=surrogate.ReluGrad() - Surrogate gradient function for the non-differentiable spike generation. + Surrogate gradient function for the spike generation. spk_reset : str, default='soft' - Reset mechanism after spike generation: - - 'soft': subtract threshold V = V - V_th - - 'hard': strict reset using stop_gradient + Reset mechanism after spike generation. name : str, optional Name of the neuron layer. @@ -339,53 +334,51 @@ class LIFRef(Neuron): ---------- V : HiddenState Membrane potential. - last_spike_time : ShortTermState - Time of the last spike, used to implement refractory period. Examples -------- - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> - >>> # Create a LIFRef neuron layer with 10 neurons - >>> lifref = brainpy.LIFRef(10, - ... tau=10*u.ms, - ... tau_ref=5*u.ms, - ... V_th=0.8*u.mV) + >>> # Create a ExpIF neuron layer with 10 neurons + >>> expif = brainpy.state.ExpIF(10, tau=10*u.ms, V_th=-30*u.mV) >>> >>> # Initialize the state - >>> lifref.init_state(batch_size=1) + >>> expif.init_state(batch_size=1) >>> >>> # Apply an input current and update the neuron state - >>> spikes = lifref.update(x=1.5*u.mA) - >>> - >>> # Create a network with refractory neurons - >>> network = brainstate.nn.Sequential([ - ... brainpy.LIFRef(100, tau_ref=4*u.ms), - ... brainstate.nn.Linear(100, 10) - ... ]) + >>> spikes = expif.update(x=1.5*u.mA) Notes ----- - - The refractory period is implemented by tracking the time of the last spike - and preventing membrane potential updates if the elapsed time is less than tau_ref. - - During the refractory period, the membrane potential remains at the reset value - regardless of input current strength. - - Refractory periods prevent high-frequency repetitive firing and are critical - for realistic neural dynamics. - - The time-dependent dynamics are integrated using an exponential Euler method. - - The simulation environment time variable 't' is used to track the refractory state. + - The model was first introduced by Nicolas Fourcaud-Trocmé, David Hansel, Carl van Vreeswijk + and Nicolas Brunel [1]_. The exponential nonlinearity was later confirmed by Badel et al. [3]_. + It is one of the prominent examples of a precise theoretical prediction in computational + neuroscience that was later confirmed by experimental neuroscience. + - The right-hand side of the above equation contains a nonlinearity + that can be directly extracted from experimental data [3]_. In this sense the exponential + nonlinearity is not an arbitrary choice but directly supported by experimental evidence. + - Even though it is a nonlinear model, it is simple enough to calculate the firing + rate for constant input, and the linear response to fluctuations, even in the presence + of input noise [4]_. References ---------- - .. [1] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). - Neuronal dynamics: From single neurons to networks and models of cognition. - Cambridge University Press. - .. [2] Burkitt, A. N. (2006). A review of the integrate-and-fire neuron model: - I. Homogeneous synaptic input. Biological cybernetics, 95(1), 1-19. - .. [3] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on - neural networks, 14(6), 1569-1572. + .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation + mechanisms determine the neuronal response to fluctuating + inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. + .. [2] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). + Neuronal dynamics: From single neurons to networks and models + of cognition. Cambridge University Press. + .. [3] Badel, Laurent, Sandrine Lefort, Romain Brette, Carl CH Petersen, + Wulfram Gerstner, and Magnus JE Richardson. "Dynamic IV curves + are reliable predictors of naturalistic pyramidal-neuron voltage + traces." Journal of Neurophysiology 99, no. 2 (2008): 656-666. + .. [4] Richardson, Magnus JE. "Firing-rate response of linear and nonlinear + integrate-and-fire neurons to modulated current-based and + conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. + .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire """ __module__ = 'brainpy' @@ -393,12 +386,13 @@ def __init__( self, in_size: Size, R: ArrayLike = 1. * u.ohm, - tau: ArrayLike = 5. * u.ms, - tau_ref: ArrayLike = 5. * u.ms, - V_th: ArrayLike = 1. * u.mV, - V_reset: ArrayLike = 0. * u.mV, - V_rest: ArrayLike = 0. * u.mV, - V_initializer: Callable = braintools.init.Constant(0. * u.mV), + tau: ArrayLike = 10. * u.ms, + V_th: ArrayLike = -30. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_T: ArrayLike = -59.9 * u.mV, + delta_T: ArrayLike = 3.48 * u.mV, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), spk_fun: Callable = braintools.surrogate.ReluGrad(), spk_reset: str = 'soft', name: str = None, @@ -408,23 +402,162 @@ def __init__( # parameters self.R = braintools.init.param(R, self.varshape) self.tau = braintools.init.param(tau, self.varshape) - self.tau_ref = braintools.init.param(tau_ref, self.varshape) self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_T = braintools.init.param(V_T, self.varshape) + self.delta_T = braintools.init.param(delta_T, self.varshape) + self.V_initializer = V_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + last_v = self.V.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + V = last_v - (V_th - self.V_reset) * last_spk + + def dv(v): + exp_term = self.delta_T * u.math.exp((v - self.V_T) / self.delta_T) + return (-(v - self.V_rest) + exp_term + self.R * self.sum_current_inputs(x, v)) / self.tau + + V = brainstate.nn.exp_euler_step(dv, V) + V = self.sum_delta_inputs(V) + self.V.value = V + return self.get_spike(V) + + +class ExpIFRef(Neuron): + r"""Exponential Integrate-and-Fire neuron model with refractory mechanism. + + This neuron adds an absolute refractory period to :class:`ExpIF`. While the exponential + spike-initiation term keeps the membrane potential dynamics smooth, the refractory + mechanism prevents the neuron from firing within ``tau_ref`` after a spike. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=10. * u.ms + Membrane time constant. + tau_ref : ArrayLike, default=1.7 * u.ms + Absolute refractory period duration. + V_th : ArrayLike, default=-30. * u.mV + Numerical firing threshold voltage. + V_reset : ArrayLike, default=-68. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=-65. * u.mV + Resting membrane potential. + V_T : ArrayLike, default=-59.9 * u.mV + Threshold potential of the exponential term. + delta_T : ArrayLike, default=3.48 * u.mV + Spike slope factor controlling spike initiation sharpness. + V_initializer : Callable + Initializer for the membrane potential state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + ref_var : bool, default=False + Whether to expose a boolean refractory state variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + last_spike_time : ShortTermState + Last spike time recorder. + refractory : HiddenState + Neuron refractory state. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a ExpIF neuron layer with 10 neurons + >>> expif = brainpy.state.ExpIF(10, tau=10*u.ms, V_th=-30*u.mV) + >>> + >>> # Initialize the state + >>> expif.init_state(batch_size=1) + >>> + >>> # Generate inputs + >>> time_steps = 100 + >>> inputs = brainstate.random.randn(time_steps, 1, 10) * u.mA + >>> + >>> # Apply an input current and update the neuron state + >>> + >>> with brainstate.environ.context(dt=0.1 * u.ms): + >>> for t in range(time_steps): + >>> with brainstate.environ.context(t=t*0.1*u.ms): + >>> spikes = expif.update(x=inputs[t]) + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 10. * u.ms, + tau_ref: ArrayLike = 1.7 * u.ms, + V_th: ArrayLike = -30. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_T: ArrayLike = -59.9 * u.mV, + delta_T: ArrayLike = 3.48 * u.mV, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + ref_var: bool = False, + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_T = braintools.init.param(V_T, self.varshape) + self.delta_T = braintools.init.param(delta_T, self.varshape) self.V_initializer = V_initializer + self.ref_var = ref_var def init_state(self, batch_size: int = None, **kwargs): self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) self.last_spike_time = brainstate.ShortTermState( braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) ) + if self.ref_var: + self.refractory = brainstate.HiddenState( + braintools.init.param(braintools.init.Constant(False), self.varshape, batch_size) + ) def reset_state(self, batch_size: int = None, **kwargs): self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) self.last_spike_time.value = braintools.init.param( braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size ) + if self.ref_var: + self.refractory.value = braintools.init.param( + braintools.init.Constant(False), self.varshape, batch_size + ) def get_spike(self, V: ArrayLike = None): V = self.V.value if V is None else V @@ -434,42 +567,50 @@ def get_spike(self, V: ArrayLike = None): def update(self, x=0. * u.mA): t = brainstate.environ.get('t') last_v = self.V.value - lst_spk = self.get_spike(last_v) + last_spk = self.get_spike(last_v) V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) - last_v = last_v - (V_th - self.V_reset) * lst_spk - # membrane potential - dv = lambda v: (-v + self.V_rest + self.R * self.sum_current_inputs(x, v)) / self.tau - V = brainstate.nn.exp_euler_step(dv, last_v) - V = self.sum_delta_inputs(V) - self.V.value = u.math.where(t - self.last_spike_time.value < self.tau_ref, last_v, V) - # spike time evaluation - lst_spk_time = u.math.where( - self.V.value >= self.V_th, brainstate.environ.get('t'), self.last_spike_time.value) - self.last_spike_time.value = jax.lax.stop_gradient(lst_spk_time) - return self.get_spike() + v_reset = last_v - (V_th - self.V_reset) * last_spk + def dv(v): + exp_term = self.delta_T * u.math.exp((v - self.V_T) / self.delta_T) + return (-(v - self.V_rest) + exp_term + self.R * self.sum_current_inputs(x, v)) / self.tau -class ALIF(Neuron): - r"""Adaptive Leaky Integrate-and-Fire (ALIF) neuron model. + V_candidate = brainstate.nn.exp_euler_step(dv, v_reset) + V_candidate = self.sum_delta_inputs(V_candidate) - This class implements the Adaptive Leaky Integrate-and-Fire neuron model, which extends - the basic LIF model by adding an adaptation variable. This adaptation mechanism increases - the effective firing threshold after each spike, allowing the neuron to exhibit - spike-frequency adaptation - a common feature in biological neurons that reduces - firing rate during sustained stimulation. + refractory = (t - self.last_spike_time.value) < self.tau_ref + self.V.value = u.math.where(refractory, v_reset, V_candidate) - The model is characterized by the following differential equations: + spike_cond = self.V.value >= self.V_th + self.last_spike_time.value = jax.lax.stop_gradient( + u.math.where(spike_cond, t, self.last_spike_time.value) + ) + if self.ref_var: + self.refractory.value = jax.lax.stop_gradient( + u.math.logical_or(refractory, spike_cond) + ) + return self.get_spike() + + +class AdExIF(Neuron): + r"""Adaptive exponential Integrate-and-Fire (AdExIF) neuron model. + + This model extends :class:`ExpIF` by adding an adaptation current ``w`` that is + incremented after each spike and relaxes with time constant ``tau_w``. The membrane + dynamics are governed by two coupled differential equations [1]_: $$ - \tau \frac{dV}{dt} = -(V - V_{rest}) + R \cdot I(t) + \tau \frac{dV}{dt} = -(V - V_{rest}) + \Delta_T + \exp\left(\frac{V - V_T}{\Delta_T}\right) - R w + R \cdot I(t) $$ $$ - \tau_a \frac{da}{dt} = -a + \tau_w \frac{dw}{dt} = a (V - V_{rest}) - w $$ - Spike condition: - If $V \geq V_{th} + \beta \cdot a$: emit spike, set $V = V_{reset}$, and increment $a = a + 1$ + After each spike the membrane potential is reset and the adaptation current + increases by ``b``. This simple mechanism generates rich firing patterns such + as spike-frequency adaptation and bursting. Parameters ---------- @@ -477,29 +618,32 @@ class ALIF(Neuron): Size of the input to the neuron. R : ArrayLike, default=1. * u.ohm Membrane resistance. - tau : ArrayLike, default=5. * u.ms + tau : ArrayLike, default=10. * u.ms Membrane time constant. - tau_a : ArrayLike, default=100. * u.ms - Adaptation time constant (typically much longer than tau). - V_th : ArrayLike, default=1. * u.mV - Base firing threshold voltage. - V_reset : ArrayLike, default=0. * u.mV - Reset voltage after spike. - V_rest : ArrayLike, default=0. * u.mV + tau_w : ArrayLike, default=30. * u.ms + Adaptation current time constant. + V_th : ArrayLike, default=-55. * u.mV + Spike threshold used for reset. + V_reset : ArrayLike, default=-68. * u.mV + Reset potential after spike. + V_rest : ArrayLike, default=-65. * u.mV Resting membrane potential. - beta : ArrayLike, default=0.1 * u.mV - Adaptation coupling parameter that scales the effect of the adaptation variable. - spk_fun : Callable - Surrogate gradient function for the non-differentiable spike generation. - spk_reset : str, default='soft' - Reset mechanism after spike generation: - - - 'soft': subtract threshold V = V - V_th - - 'hard': strict reset using stop_gradient + V_T : ArrayLike, default=-59.9 * u.mV + Threshold of the exponential term. + delta_T : ArrayLike, default=3.48 * u.mV + Spike slope factor controlling the sharpness of spike initiation. + a : ArrayLike, default=1. * u.siemens + Coupling strength from voltage to adaptation current. + b : ArrayLike, default=1. * u.mA + Increment of the adaptation current after a spike. V_initializer : Callable Initializer for the membrane potential state. - a_initializer : Callable - Initializer for the adaptation variable. + w_initializer : Callable + Initializer for the adaptation current. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation. name : str, optional Name of the neuron layer. @@ -507,57 +651,34 @@ class ALIF(Neuron): ---------- V : HiddenState Membrane potential. - a : HiddenState - Adaptation variable that increases after each spike and decays exponentially. + w : HiddenState + Adaptation current. Examples -------- - >>> import brainpy.state as brainpy + >>> import brainpy >>> import brainstate >>> import brainunit as u >>> - >>> # Create an ALIF neuron layer with 10 neurons - >>> alif = brainpy.ALIF(10, - ... tau=10*u.ms, - ... tau_a=200*u.ms, - ... beta=0.2*u.mV) + >>> # Create a AdExIF neuron layer with 10 neurons + >>> adexif = brainpy.state.AdExIF(10, tau=10*u.ms) >>> >>> # Initialize the state - >>> alif.init_state(batch_size=1) + >>> adexif.init_state(batch_size=1) >>> >>> # Apply an input current and update the neuron state - >>> spikes = alif.update(x=1.5*u.mA) - >>> - >>> # Create a network with adaptation for burst detection - >>> network = brainstate.nn.Sequential([ - ... brainpy.ALIF(100, tau_a=150*u.ms, beta=0.3*u.mV), - ... brainstate.nn.Linear(100, 10) - ... ]) - - Notes - ----- - - The adaptation variable 'a' increases by 1 with each spike and decays exponentially - with time constant tau_a between spikes. - - The effective threshold increases by beta*a, making it progressively harder for the - neuron to fire when it has recently been active. - - This adaptation mechanism creates spike-frequency adaptation, allowing the neuron - to respond strongly to input onset but then reduce its firing rate even if the - input remains constant. - - The adaptation time constant tau_a is typically much larger than the membrane time - constant tau, creating a longer-lasting adaptation effect. - - The time-dependent dynamics are integrated using an exponential Euler method. + >>> spikes = adexif.update(x=1.5*u.mA) References ---------- - .. [1] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). - Neuronal dynamics: From single neurons to networks and models of cognition. - Cambridge University Press. - .. [2] Brette, R., & Gerstner, W. (2005). Adaptive exponential integrate-and-fire model - as an effective description of neuronal activity. Journal of neurophysiology, - 94(5), 3637-3642. - .. [3] Naud, R., Marcille, N., Clopath, C., & Gerstner, W. (2008). Firing patterns in - the adaptive exponential integrate-and-fire model. Biological cybernetics, - 99(4), 335-347. + .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation + mechanisms determine the neuronal response to fluctuating + inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. + .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model + + .. seealso:: + + :class:`brainpy.dyn.AdExIF` for the dynamical-system counterpart. """ __module__ = 'brainpy' @@ -565,16 +686,19 @@ def __init__( self, in_size: Size, R: ArrayLike = 1. * u.ohm, - tau: ArrayLike = 5. * u.ms, - tau_a: ArrayLike = 100. * u.ms, - V_th: ArrayLike = 1. * u.mV, - V_reset: ArrayLike = 0. * u.mV, - V_rest: ArrayLike = 0. * u.mV, - beta: ArrayLike = 0.1 * u.mV, + tau: ArrayLike = 10. * u.ms, + tau_w: ArrayLike = 30. * u.ms, + V_th: ArrayLike = -55. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_T: ArrayLike = -59.9 * u.mV, + delta_T: ArrayLike = 3.48 * u.mV, + a: ArrayLike = 1. * u.siemens, + b: ArrayLike = 1. * u.mA, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + w_initializer: Callable = braintools.init.Constant(0. * u.mA), spk_fun: Callable = braintools.surrogate.ReluGrad(), spk_reset: str = 'soft', - V_initializer: Callable = braintools.init.Constant(0. * u.mV), - a_initializer: Callable = braintools.init.Constant(0.), name: str = None, ): super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) @@ -582,42 +706,1430 @@ def __init__( # parameters self.R = braintools.init.param(R, self.varshape) self.tau = braintools.init.param(tau, self.varshape) - self.tau_a = braintools.init.param(tau_a, self.varshape) + self.tau_w = braintools.init.param(tau_w, self.varshape) self.V_th = braintools.init.param(V_th, self.varshape) self.V_reset = braintools.init.param(V_reset, self.varshape) self.V_rest = braintools.init.param(V_rest, self.varshape) - self.beta = braintools.init.param(beta, self.varshape) + self.V_T = braintools.init.param(V_T, self.varshape) + self.delta_T = braintools.init.param(delta_T, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) - # functions + # initializers self.V_initializer = V_initializer - self.a_initializer = a_initializer + self.w_initializer = w_initializer def init_state(self, batch_size: int = None, **kwargs): self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) - self.a = brainstate.HiddenState(braintools.init.param(self.a_initializer, self.varshape, batch_size)) + self.w = brainstate.HiddenState(braintools.init.param(self.w_initializer, self.varshape, batch_size)) def reset_state(self, batch_size: int = None, **kwargs): self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) - self.a.value = braintools.init.param(self.a_initializer, self.varshape, batch_size) + self.w.value = braintools.init.param(self.w_initializer, self.varshape, batch_size) - def get_spike(self, V=None, a=None): + def get_spike(self, V: ArrayLike = None): V = self.V.value if V is None else V - a = self.a.value if a is None else a - v_scaled = (V - self.V_th - self.beta * a) / (self.V_th - self.V_reset) + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) return self.spk_fun(v_scaled) def update(self, x=0. * u.mA): last_v = self.V.value - last_a = self.a.value - lst_spk = self.get_spike(last_v, last_a) + last_w = self.w.value + last_spk = self.get_spike(last_v) V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) - V = last_v - (V_th - self.V_reset) * lst_spk - a = last_a + lst_spk - # membrane potential - dv = lambda v: (-v + self.V_rest + self.R * self.sum_current_inputs(x, v)) / self.tau - da = lambda a: -a / self.tau_a + V = last_v - (V_th - self.V_reset) * last_spk + w = last_w + self.b * last_spk + + def dv(v): + exp_term = self.delta_T * u.math.exp((v - self.V_T) / self.delta_T) + I_total = self.sum_current_inputs(x, v) + return (-(v - self.V_rest) + exp_term - self.R * w + self.R * I_total) / self.tau + V = brainstate.nn.exp_euler_step(dv, V) - a = brainstate.nn.exp_euler_step(da, a) + V = self.sum_delta_inputs(V) + + def dw_func(w_val): + return (self.a * (V - self.V_rest) - w_val) / self.tau_w + + w = brainstate.nn.exp_euler_step(dw_func, w) + self.V.value = V + self.w.value = w + return self.get_spike(self.V.value) + + +class AdExIFRef(Neuron): + r"""Adaptive exponential Integrate-and-Fire neuron model with refractory mechanism. + + This model extends :class:`AdExIF` by adding an absolute refractory period. While the + exponential spike-initiation term and adaptation current keep the membrane potential + dynamics biologically realistic, the refractory mechanism prevents the neuron from + firing within ``tau_ref`` after a spike. + + The membrane dynamics are governed by two coupled differential equations: + + $$ + \tau \frac{dV}{dt} = -(V - V_{rest}) + \Delta_T + \exp\left(\frac{V - V_T}{\Delta_T}\right) - R w + R \cdot I(t) + $$ + + $$ + \tau_w \frac{dw}{dt} = a (V - V_{rest}) - w + $$ + + After each spike the membrane potential is reset and the adaptation current + increases by ``b``. During the refractory period, the membrane potential + remains at the reset value. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=10. * u.ms + Membrane time constant. + tau_w : ArrayLike, default=30. * u.ms + Adaptation current time constant. + tau_ref : ArrayLike, default=1.7 * u.ms + Absolute refractory period duration. + V_th : ArrayLike, default=-55. * u.mV + Spike threshold used for reset. + V_reset : ArrayLike, default=-68. * u.mV + Reset potential after spike. + V_rest : ArrayLike, default=-65. * u.mV + Resting membrane potential. + V_T : ArrayLike, default=-59.9 * u.mV + Threshold of the exponential term. + delta_T : ArrayLike, default=3.48 * u.mV + Spike slope factor controlling the sharpness of spike initiation. + a : ArrayLike, default=1. * u.siemens + Coupling strength from voltage to adaptation current. + b : ArrayLike, default=1. * u.mA + Increment of the adaptation current after a spike. + V_initializer : Callable + Initializer for the membrane potential state. + w_initializer : Callable + Initializer for the adaptation current. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + ref_var : bool, default=False + Whether to expose a boolean refractory state variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + w : HiddenState + Adaptation current. + last_spike_time : ShortTermState + Last spike time recorder. + refractory : HiddenState + Neuron refractory state (if ref_var=True). + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an AdExIFRef neuron layer with 10 neurons + >>> adexif_ref = brainpy.state.AdExIFRef(10, tau=10*u.ms, tau_ref=2*u.ms) + >>> + >>> # Initialize the state + >>> adexif_ref.init_state(batch_size=1) + >>> + >>> # Generate inputs + >>> time_steps = 100 + >>> inputs = brainstate.random.randn(time_steps, 1, 10) * u.mA + >>> + >>> # Apply input currents and update the neuron state + >>> with brainstate.environ.context(dt=0.1 * u.ms): + >>> for t in range(time_steps): + >>> with brainstate.environ.context(t=t*0.1*u.ms): + >>> spikes = adexif_ref.update(x=inputs[t]) + + References + ---------- + .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation + mechanisms determine the neuronal response to fluctuating + inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. + .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model + + .. seealso:: + + :class:`brainpy.dyn.AdExIFRef` for the dynamical-system counterpart. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 10. * u.ms, + tau_w: ArrayLike = 30. * u.ms, + tau_ref: ArrayLike = 1.7 * u.ms, + V_th: ArrayLike = -55. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_T: ArrayLike = -59.9 * u.mV, + delta_T: ArrayLike = 3.48 * u.mV, + a: ArrayLike = 1. * u.siemens, + b: ArrayLike = 1. * u.mA, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + w_initializer: Callable = braintools.init.Constant(0. * u.mA), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + ref_var: bool = False, + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_w = braintools.init.param(tau_w, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_T = braintools.init.param(V_T, self.varshape) + self.delta_T = braintools.init.param(delta_T, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + + # initializers + self.V_initializer = V_initializer + self.w_initializer = w_initializer + self.ref_var = ref_var + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.w = brainstate.HiddenState(braintools.init.param(self.w_initializer, self.varshape, batch_size)) + self.last_spike_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + if self.ref_var: + self.refractory = brainstate.HiddenState( + braintools.init.param(braintools.init.Constant(False), self.varshape, batch_size) + ) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.w.value = braintools.init.param(self.w_initializer, self.varshape, batch_size) + self.last_spike_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size + ) + if self.ref_var: + self.refractory.value = braintools.init.param( + braintools.init.Constant(False), self.varshape, batch_size + ) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + t = brainstate.environ.get('t') + last_v = self.V.value + last_w = self.w.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + v_reset = last_v - (V_th - self.V_reset) * last_spk + w_reset = last_w + self.b * last_spk + + def dv(v): + exp_term = self.delta_T * u.math.exp((v - self.V_T) / self.delta_T) + I_total = self.sum_current_inputs(x, v) + return (-(v - self.V_rest) + exp_term - self.R * w_reset + self.R * I_total) / self.tau + + V_candidate = brainstate.nn.exp_euler_step(dv, v_reset) + V_candidate = self.sum_delta_inputs(V_candidate) + + def dw_func(w_val): + return (self.a * (V_candidate - self.V_rest) - w_val) / self.tau_w + + w_candidate = brainstate.nn.exp_euler_step(dw_func, w_reset) + + refractory = (t - self.last_spike_time.value) < self.tau_ref + self.V.value = u.math.where(refractory, v_reset, V_candidate) + self.w.value = u.math.where(refractory, w_reset, w_candidate) + + spike_cond = self.V.value >= self.V_th + self.last_spike_time.value = jax.lax.stop_gradient( + u.math.where(spike_cond, t, self.last_spike_time.value) + ) + if self.ref_var: + self.refractory.value = jax.lax.stop_gradient( + u.math.logical_or(refractory, spike_cond) + ) + return self.get_spike() + + +class LIFRef(Neuron): + r"""Leaky Integrate-and-Fire neuron model with refractory period. + + This class implements a Leaky Integrate-and-Fire neuron model that includes a + refractory period after spiking, during which the neuron cannot fire regardless + of input. This better captures the behavior of biological neurons that exhibit + a recovery period after action potential generation. + + The model is characterized by the following equations: + + When not in refractory period: + $$ + \tau \frac{dV}{dt} = -(V - V_{rest}) + R \cdot I(t) + $$ + + During refractory period: + $$ + V = V_{reset} + $$ + + Spike condition: + If $V \geq V_{th}$: emit spike, set $V = V_{reset}$, and enter refractory period for $\tau_{ref}$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=5. * u.ms + Membrane time constant. + tau_ref : ArrayLike, default=5. * u.ms + Refractory period duration. + V_th : ArrayLike, default=1. * u.mV + Firing threshold voltage. + V_reset : ArrayLike, default=0. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=0. * u.mV + Resting membrane potential. + V_initializer : Callable + Initializer for the membrane potential state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the non-differentiable spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation: + - 'soft': subtract threshold V = V - V_th + - 'hard': strict reset using stop_gradient + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + last_spike_time : ShortTermState + Time of the last spike, used to implement refractory period. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a LIFRef neuron layer with 10 neurons + >>> lifref = brainpy.state.LIFRef(10, + ... tau=10*u.ms, + ... tau_ref=5*u.ms, + ... V_th=0.8*u.mV) + >>> + >>> # Initialize the state + >>> lifref.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = lifref.update(x=1.5*u.mA) + >>> + >>> # Create a network with refractory neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.LIFRef(100, tau_ref=4*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - The refractory period is implemented by tracking the time of the last spike + and preventing membrane potential updates if the elapsed time is less than tau_ref. + - During the refractory period, the membrane potential remains at the reset value + regardless of input current strength. + - Refractory periods prevent high-frequency repetitive firing and are critical + for realistic neural dynamics. + - The time-dependent dynamics are integrated using an exponential Euler method. + - The simulation environment time variable 't' is used to track the refractory state. + + References + ---------- + .. [1] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). + Neuronal dynamics: From single neurons to networks and models of cognition. + Cambridge University Press. + .. [2] Burkitt, A. N. (2006). A review of the integrate-and-fire neuron model: + I. Homogeneous synaptic input. Biological cybernetics, 95(1), 1-19. + .. [3] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on + neural networks, 14(6), 1569-1572. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 5. * u.ms, + tau_ref: ArrayLike = 5. * u.ms, + V_th: ArrayLike = 1. * u.mV, + V_reset: ArrayLike = 0. * u.mV, + V_rest: ArrayLike = 0. * u.mV, + V_initializer: Callable = braintools.init.Constant(0. * u.mV), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_initializer = V_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.last_spike_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.last_spike_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size + ) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + t = brainstate.environ.get('t') + last_v = self.V.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + last_v = last_v - (V_th - self.V_reset) * last_spk + # membrane potential + dv = lambda v: (-v + self.V_rest + self.R * self.sum_current_inputs(x, v)) / self.tau + V = brainstate.nn.exp_euler_step(dv, last_v) + V = self.sum_delta_inputs(V) + self.V.value = u.math.where(t - self.last_spike_time.value < self.tau_ref, last_v, V) + # spike time evaluation + last_spk_time = u.math.where( + self.V.value >= self.V_th, brainstate.environ.get('t'), self.last_spike_time.value) + self.last_spike_time.value = jax.lax.stop_gradient(last_spk_time) + return self.get_spike() + + +class ALIF(Neuron): + r"""Adaptive Leaky Integrate-and-Fire (ALIF) neuron model. + + This class implements the Adaptive Leaky Integrate-and-Fire neuron model, which extends + the basic LIF model by adding an adaptation variable. This adaptation mechanism increases + the effective firing threshold after each spike, allowing the neuron to exhibit + spike-frequency adaptation - a common feature in biological neurons that reduces + firing rate during sustained stimulation. + + The model is characterized by the following differential equations: + + $$ + \tau \frac{dV}{dt} = -(V - V_{rest}) + R \cdot I(t) + $$ + + $$ + \tau_a \frac{da}{dt} = -a + $$ + + Spike condition: + If $V \geq V_{th} + \beta \cdot a$: emit spike, set $V = V_{reset}$, and increment $a = a + 1$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=5. * u.ms + Membrane time constant. + tau_a : ArrayLike, default=100. * u.ms + Adaptation time constant (typically much longer than tau). + V_th : ArrayLike, default=1. * u.mV + Base firing threshold voltage. + V_reset : ArrayLike, default=0. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=0. * u.mV + Resting membrane potential. + beta : ArrayLike, default=0.1 * u.mV + Adaptation coupling parameter that scales the effect of the adaptation variable. + spk_fun : Callable + Surrogate gradient function for the non-differentiable spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation: + + - 'soft': subtract threshold V = V - V_th + - 'hard': strict reset using stop_gradient + V_initializer : Callable + Initializer for the membrane potential state. + a_initializer : Callable + Initializer for the adaptation variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + a : HiddenState + Adaptation variable that increases after each spike and decays exponentially. + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an ALIF neuron layer with 10 neurons + >>> alif = brainpy.state.ALIF(10, + ... tau=10*u.ms, + ... tau_a=200*u.ms, + ... beta=0.2*u.mV) + >>> + >>> # Initialize the state + >>> alif.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = alif.update(x=1.5*u.mA) + >>> + >>> # Create a network with adaptation for burst detection + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.ALIF(100, tau_a=150*u.ms, beta=0.3*u.mV), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - The adaptation variable 'a' increases by 1 with each spike and decays exponentially + with time constant tau_a between spikes. + - The effective threshold increases by beta*a, making it progressively harder for the + neuron to fire when it has recently been active. + - This adaptation mechanism creates spike-frequency adaptation, allowing the neuron + to respond strongly to input onset but then reduce its firing rate even if the + input remains constant. + - The adaptation time constant tau_a is typically much larger than the membrane time + constant tau, creating a longer-lasting adaptation effect. + - The time-dependent dynamics are integrated using an exponential Euler method. + + References + ---------- + .. [1] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). + Neuronal dynamics: From single neurons to networks and models of cognition. + Cambridge University Press. + .. [2] Brette, R., & Gerstner, W. (2005). Adaptive exponential integrate-and-fire model + as an effective description of neuronal activity. Journal of neurophysiology, + 94(5), 3637-3642. + .. [3] Naud, R., Marcille, N., Clopath, C., & Gerstner, W. (2008). Firing patterns in + the adaptive exponential integrate-and-fire model. Biological cybernetics, + 99(4), 335-347. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 5. * u.ms, + tau_a: ArrayLike = 100. * u.ms, + V_th: ArrayLike = 1. * u.mV, + V_reset: ArrayLike = 0. * u.mV, + V_rest: ArrayLike = 0. * u.mV, + beta: ArrayLike = 0.1 * u.mV, + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + V_initializer: Callable = braintools.init.Constant(0. * u.mV), + a_initializer: Callable = braintools.init.Constant(0.), + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_a = braintools.init.param(tau_a, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.beta = braintools.init.param(beta, self.varshape) + + # functions + self.V_initializer = V_initializer + self.a_initializer = a_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.a = brainstate.HiddenState(braintools.init.param(self.a_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.a.value = braintools.init.param(self.a_initializer, self.varshape, batch_size) + + def get_spike(self, V=None, a=None): + V = self.V.value if V is None else V + a = self.a.value if a is None else a + v_scaled = (V - self.V_th - self.beta * a) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + last_v = self.V.value + last_a = self.a.value + lst_spk = self.get_spike(last_v, last_a) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + V = last_v - (V_th - self.V_reset) * lst_spk + a = last_a + lst_spk + # membrane potential + dv = lambda v: (-v + self.V_rest + self.R * self.sum_current_inputs(x, v)) / self.tau + da = lambda a: -a / self.tau_a + V = brainstate.nn.exp_euler_step(dv, V) + a = brainstate.nn.exp_euler_step(da, a) self.V.value = self.sum_delta_inputs(V) self.a.value = a return self.get_spike(self.V.value, self.a.value) + + +class QuaIF(Neuron): + r"""Quadratic Integrate-and-Fire (QuaIF) neuron model. + + This model extends the basic integrate-and-fire neuron by adding a quadratic + nonlinearity in the voltage dynamics. The quadratic term creates a soft spike + initiation, making the model more biologically realistic than the linear IF model. + + The model is characterized by the following differential equation: + + $$ + \tau \frac{dV}{dt} = c(V - V_{rest})(V - V_c) + R \cdot I(t) + $$ + + Spike condition: + If $V \geq V_{th}$: emit spike and reset $V = V_{reset}$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=10. * u.ms + Membrane time constant. + V_th : ArrayLike, default=-30. * u.mV + Firing threshold voltage. + V_reset : ArrayLike, default=-68. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=-65. * u.mV + Resting membrane potential. + V_c : ArrayLike, default=-50. * u.mV + Critical voltage for spike initiation. Must be larger than V_rest. + c : ArrayLike, default=0.07 / u.mV + Coefficient describing membrane potential update. Larger than 0. + V_initializer : Callable + Initializer for the membrane potential state. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function for the spike generation. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + + References + ---------- + .. [1] P. E. Latham, B.J. Richmond, P. Nelson and S. Nirenberg + (2000) Intrinsic dynamics in neuronal networks. I. Theory. + J. Neurophysiology 83, pp. 808–827. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 10. * u.ms, + V_th: ArrayLike = -30. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_c: ArrayLike = -50. * u.mV, + c: ArrayLike = 0.07 / u.mV, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_c = braintools.init.param(V_c, self.varshape) + self.c = braintools.init.param(c, self.varshape) + self.V_initializer = V_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + last_v = self.V.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + V = last_v - (V_th - self.V_reset) * last_spk + + def dv(v): + return (self.c * (v - self.V_rest) * (v - self.V_c) + self.R * self.sum_current_inputs(x, v)) / self.tau + + V = brainstate.nn.exp_euler_step(dv, V) + V = self.sum_delta_inputs(V) + self.V.value = V + return self.get_spike(V) + + +class AdQuaIF(Neuron): + r"""Adaptive Quadratic Integrate-and-Fire (AdQuaIF) neuron model. + + This model extends the QuaIF model by adding an adaptation current that increases + after each spike and decays exponentially between spikes. The adaptation mechanism + produces spike-frequency adaptation and enables the neuron to exhibit various + firing patterns. + + The model is characterized by the following differential equations: + + $$ + \tau \frac{dV}{dt} = c(V - V_{rest})(V - V_c) - w + R \cdot I(t) + $$ + + $$ + \tau_w \frac{dw}{dt} = a(V - V_{rest}) - w + $$ + + After a spike: $V \rightarrow V_{reset}$, $w \rightarrow w + b$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=10. * u.ms + Membrane time constant. + tau_w : ArrayLike, default=10. * u.ms + Adaptation current time constant. + V_th : ArrayLike, default=-30. * u.mV + Firing threshold voltage. + V_reset : ArrayLike, default=-68. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=-65. * u.mV + Resting membrane potential. + V_c : ArrayLike, default=-50. * u.mV + Critical voltage for spike initiation. + c : ArrayLike, default=0.07 / u.mV + Coefficient describing membrane potential update. + a : ArrayLike, default=1. * u.siemens + Coupling strength from voltage to adaptation current. + b : ArrayLike, default=0.1 * u.mA + Increment of adaptation current after a spike. + V_initializer : Callable + Initializer for the membrane potential state. + w_initializer : Callable + Initializer for the adaptation current. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + w : HiddenState + Adaptation current. + + References + ---------- + .. [1] Izhikevich, E. M. (2004). Which model to use for cortical spiking + neurons?. IEEE transactions on neural networks, 15(5), 1063-1070. + .. [2] Touboul, Jonathan. "Bifurcation analysis of a general class of + nonlinear integrate-and-fire neurons." SIAM Journal on Applied + Mathematics 68, no. 4 (2008): 1045-1079. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 10. * u.ms, + tau_w: ArrayLike = 10. * u.ms, + V_th: ArrayLike = -30. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_c: ArrayLike = -50. * u.mV, + c: ArrayLike = 0.07 / u.mV, + a: ArrayLike = 1. * u.siemens, + b: ArrayLike = 0.1 * u.mA, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + w_initializer: Callable = braintools.init.Constant(0. * u.mA), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_w = braintools.init.param(tau_w, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_c = braintools.init.param(V_c, self.varshape) + self.c = braintools.init.param(c, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.V_initializer = V_initializer + self.w_initializer = w_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.w = brainstate.HiddenState(braintools.init.param(self.w_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.w.value = braintools.init.param(self.w_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + last_v = self.V.value + last_w = self.w.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + V = last_v - (V_th - self.V_reset) * last_spk + w = last_w + self.b * last_spk + + def dv(v): + return (self.c * (v - self.V_rest) * (v - self.V_c) - self.R * w + self.R * self.sum_current_inputs(x, v)) / self.tau + + def dw_func(w_val): + return (self.a * (V - self.V_rest) - w_val) / self.tau_w + + V = brainstate.nn.exp_euler_step(dv, V) + V = self.sum_delta_inputs(V) + w = brainstate.nn.exp_euler_step(dw_func, w) + + self.V.value = V + self.w.value = w + return self.get_spike(V) + + +class AdQuaIFRef(Neuron): + r"""Adaptive Quadratic Integrate-and-Fire neuron model with refractory mechanism. + + This model extends AdQuaIF by adding an absolute refractory period during which + the neuron cannot fire regardless of input. The combination of adaptation and + refractory period creates realistic firing patterns. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=1. * u.ohm + Membrane resistance. + tau : ArrayLike, default=10. * u.ms + Membrane time constant. + tau_w : ArrayLike, default=10. * u.ms + Adaptation current time constant. + tau_ref : ArrayLike, default=1.7 * u.ms + Absolute refractory period duration. + V_th : ArrayLike, default=-30. * u.mV + Firing threshold voltage. + V_reset : ArrayLike, default=-68. * u.mV + Reset voltage after spike. + V_rest : ArrayLike, default=-65. * u.mV + Resting membrane potential. + V_c : ArrayLike, default=-50. * u.mV + Critical voltage for spike initiation. + c : ArrayLike, default=0.07 / u.mV + Coefficient describing membrane potential update. + a : ArrayLike, default=1. * u.siemens + Coupling strength from voltage to adaptation current. + b : ArrayLike, default=0.1 * u.mA + Increment of adaptation current after a spike. + V_initializer : Callable + Initializer for the membrane potential state. + w_initializer : Callable + Initializer for the adaptation current. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + ref_var : bool, default=False + Whether to expose a boolean refractory state variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + w : HiddenState + Adaptation current. + last_spike_time : ShortTermState + Last spike time recorder. + refractory : HiddenState + Neuron refractory state (if ref_var=True). + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 1. * u.ohm, + tau: ArrayLike = 10. * u.ms, + tau_w: ArrayLike = 10. * u.ms, + tau_ref: ArrayLike = 1.7 * u.ms, + V_th: ArrayLike = -30. * u.mV, + V_reset: ArrayLike = -68. * u.mV, + V_rest: ArrayLike = -65. * u.mV, + V_c: ArrayLike = -50. * u.mV, + c: ArrayLike = 0.07 / u.mV, + a: ArrayLike = 1. * u.siemens, + b: ArrayLike = 0.1 * u.mA, + V_initializer: Callable = braintools.init.Constant(-65. * u.mV), + w_initializer: Callable = braintools.init.Constant(0. * u.mA), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + ref_var: bool = False, + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_w = braintools.init.param(tau_w, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + self.V_th = braintools.init.param(V_th, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_c = braintools.init.param(V_c, self.varshape) + self.c = braintools.init.param(c, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.V_initializer = V_initializer + self.w_initializer = w_initializer + self.ref_var = ref_var + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.w = brainstate.HiddenState(braintools.init.param(self.w_initializer, self.varshape, batch_size)) + self.last_spike_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + if self.ref_var: + self.refractory = brainstate.HiddenState( + braintools.init.param(braintools.init.Constant(False), self.varshape, batch_size) + ) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.w.value = braintools.init.param(self.w_initializer, self.varshape, batch_size) + self.last_spike_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size + ) + if self.ref_var: + self.refractory.value = braintools.init.param( + braintools.init.Constant(False), self.varshape, batch_size + ) + + def get_spike(self, V: ArrayLike = None): + V = self.V.value if V is None else V + v_scaled = (V - self.V_th) / (self.V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + t = brainstate.environ.get('t') + last_v = self.V.value + last_w = self.w.value + last_spk = self.get_spike(last_v) + V_th = self.V_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + v_reset = last_v - (V_th - self.V_reset) * last_spk + w_reset = last_w + self.b * last_spk + + def dv(v): + return (self.c * (v - self.V_rest) * (v - self.V_c) - self.R * w_reset + self.R * self.sum_current_inputs(x, v)) / self.tau + + V_candidate = brainstate.nn.exp_euler_step(dv, v_reset) + V_candidate = self.sum_delta_inputs(V_candidate) + + def dw_func(w_val): + return (self.a * (V_candidate - self.V_rest) - w_val) / self.tau_w + + w_candidate = brainstate.nn.exp_euler_step(dw_func, w_reset) + + refractory = (t - self.last_spike_time.value) < self.tau_ref + self.V.value = u.math.where(refractory, v_reset, V_candidate) + self.w.value = u.math.where(refractory, w_reset, w_candidate) + + spike_cond = self.V.value >= self.V_th + self.last_spike_time.value = jax.lax.stop_gradient( + u.math.where(spike_cond, t, self.last_spike_time.value) + ) + if self.ref_var: + self.refractory.value = jax.lax.stop_gradient( + u.math.logical_or(refractory, spike_cond) + ) + return self.get_spike() + + +class Gif(Neuron): + r"""Generalized Integrate-and-Fire (Gif) neuron model. + + This model extends the basic integrate-and-fire neuron by adding internal + currents and a dynamic threshold. The model can reproduce diverse firing + patterns observed in biological neurons. + + The model is characterized by the following equations: + + $$ + \frac{dI_1}{dt} = -k_1 I_1 + $$ + + $$ + \frac{dI_2}{dt} = -k_2 I_2 + $$ + + $$ + \tau \frac{dV}{dt} = -(V - V_{rest}) + R(I_1 + I_2 + I(t)) + $$ + + $$ + \frac{dV_{th}}{dt} = a(V - V_{rest}) - b(V_{th} - V_{th\infty}) + $$ + + When $V \geq V_{th}$: + - $I_1 \leftarrow R_1 I_1 + A_1$ + - $I_2 \leftarrow R_2 I_2 + A_2$ + - $V \leftarrow V_{reset}$ + - $V_{th} \leftarrow \max(V_{th_{reset}}, V_{th})$ + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=20. * u.ohm + Membrane resistance. + tau : ArrayLike, default=20. * u.ms + Membrane time constant. + V_rest : ArrayLike, default=-70. * u.mV + Resting potential. + V_reset : ArrayLike, default=-70. * u.mV + Reset potential after spike. + V_th_inf : ArrayLike, default=-50. * u.mV + Target value of threshold potential updating. + V_th_reset : ArrayLike, default=-60. * u.mV + Free parameter, should be larger than V_reset. + V_th_initializer : Callable + Initializer for the threshold potential. + a : ArrayLike, default=0. / u.ms + Coefficient describes dependence of V_th on membrane potential. + b : ArrayLike, default=0.01 / u.ms + Coefficient describes V_th update. + k1 : ArrayLike, default=0.2 / u.ms + Constant of I1. + k2 : ArrayLike, default=0.02 / u.ms + Constant of I2. + R1 : ArrayLike, default=0. + Free parameter describing dependence of I1 reset value on I1 before spiking. + R2 : ArrayLike, default=1. + Free parameter describing dependence of I2 reset value on I2 before spiking. + A1 : ArrayLike, default=0. * u.mA + Free parameter. + A2 : ArrayLike, default=0. * u.mA + Free parameter. + V_initializer : Callable + Initializer for the membrane potential state. + I1_initializer : Callable + Initializer for internal current I1. + I2_initializer : Callable + Initializer for internal current I2. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + I1 : HiddenState + Internal current 1. + I2 : HiddenState + Internal current 2. + V_th : HiddenState + Spiking threshold potential. + + References + ---------- + .. [1] Mihalaş, Ştefan, and Ernst Niebur. "A generalized linear + integrate-and-fire neural model produces diverse spiking + behaviors." Neural computation 21.3 (2009): 704-718. + .. [2] Teeter, Corinne, Ramakrishnan Iyer, Vilas Menon, Nathan + Gouwens, David Feng, Jim Berg, Aaron Szafer et al. "Generalized + leaky integrate-and-fire models classify multiple neuron types." + Nature communications 9, no. 1 (2018): 1-15. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 20. * u.ohm, + tau: ArrayLike = 20. * u.ms, + V_rest: ArrayLike = -70. * u.mV, + V_reset: ArrayLike = -70. * u.mV, + V_th_inf: ArrayLike = -50. * u.mV, + V_th_reset: ArrayLike = -60. * u.mV, + V_th_initializer: Callable = braintools.init.Constant(-50. * u.mV), + a: ArrayLike = 0. / u.ms, + b: ArrayLike = 0.01 / u.ms, + k1: ArrayLike = 0.2 / u.ms, + k2: ArrayLike = 0.02 / u.ms, + R1: ArrayLike = 0., + R2: ArrayLike = 1., + A1: ArrayLike = 0. * u.mA, + A2: ArrayLike = 0. * u.mA, + V_initializer: Callable = braintools.init.Constant(-70. * u.mV), + I1_initializer: Callable = braintools.init.Constant(0. * u.mA), + I2_initializer: Callable = braintools.init.Constant(0. * u.mA), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_th_inf = braintools.init.param(V_th_inf, self.varshape) + self.V_th_reset = braintools.init.param(V_th_reset, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.k1 = braintools.init.param(k1, self.varshape) + self.k2 = braintools.init.param(k2, self.varshape) + self.R1 = braintools.init.param(R1, self.varshape) + self.R2 = braintools.init.param(R2, self.varshape) + self.A1 = braintools.init.param(A1, self.varshape) + self.A2 = braintools.init.param(A2, self.varshape) + self.V_initializer = V_initializer + self.I1_initializer = I1_initializer + self.I2_initializer = I2_initializer + self.V_th_initializer = V_th_initializer + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.I1 = brainstate.HiddenState(braintools.init.param(self.I1_initializer, self.varshape, batch_size)) + self.I2 = brainstate.HiddenState(braintools.init.param(self.I2_initializer, self.varshape, batch_size)) + self.V_th = brainstate.HiddenState(braintools.init.param(self.V_th_initializer, self.varshape, batch_size)) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.I1.value = braintools.init.param(self.I1_initializer, self.varshape, batch_size) + self.I2.value = braintools.init.param(self.I2_initializer, self.varshape, batch_size) + self.V_th.value = braintools.init.param(self.V_th_initializer, self.varshape, batch_size) + + def get_spike(self, V: ArrayLike = None, V_th: ArrayLike = None): + V = self.V.value if V is None else V + V_th = self.V_th.value if V_th is None else V_th + v_scaled = (V - V_th) / (V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + last_v = self.V.value + last_i1 = self.I1.value + last_i2 = self.I2.value + last_v_th = self.V_th.value + last_spk = self.get_spike(last_v, last_v_th) + + # Apply spike effects + V_th_val = last_v_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + V = last_v - (V_th_val - self.V_reset) * last_spk + I1 = last_i1 + last_spk * (self.R1 * last_i1 + self.A1 - last_i1) + I2 = last_i2 + last_spk * (self.R2 * last_i2 + self.A2 - last_i2) + V_th = last_v_th + last_spk * (u.math.maximum(self.V_th_reset, last_v_th) - last_v_th) + + # Update dynamics + def dI1(i1): + return -self.k1 * i1 + + def dI2(i2): + return -self.k2 * i2 + + def dV_th_func(v_th): + return self.a * (V - self.V_rest) - self.b * (v_th - self.V_th_inf) + + def dv(v): + return (-(v - self.V_rest) + self.R * (I1 + I2 + self.sum_current_inputs(x, v))) / self.tau + + I1 = brainstate.nn.exp_euler_step(dI1, I1) + I2 = brainstate.nn.exp_euler_step(dI2, I2) + V_th = brainstate.nn.exp_euler_step(dV_th_func, V_th) + V = brainstate.nn.exp_euler_step(dv, V) + V = self.sum_delta_inputs(V) + + self.V.value = V + self.I1.value = I1 + self.I2.value = I2 + self.V_th.value = V_th + return self.get_spike(V, V_th) + + +class GifRef(Neuron): + r"""Generalized Integrate-and-Fire neuron model with refractory mechanism. + + This model extends Gif by adding an absolute refractory period during which + the neuron cannot fire. This creates more realistic firing patterns and + prevents unrealistic high-frequency firing. + + Parameters + ---------- + in_size : Size + Size of the input to the neuron. + R : ArrayLike, default=20. * u.ohm + Membrane resistance. + tau : ArrayLike, default=20. * u.ms + Membrane time constant. + tau_ref : ArrayLike, default=1.7 * u.ms + Absolute refractory period duration. + V_rest : ArrayLike, default=-70. * u.mV + Resting potential. + V_reset : ArrayLike, default=-70. * u.mV + Reset potential after spike. + V_th_inf : ArrayLike, default=-50. * u.mV + Target value of threshold potential updating. + V_th_reset : ArrayLike, default=-60. * u.mV + Free parameter, should be larger than V_reset. + V_th_initializer : Callable + Initializer for the threshold potential. + a : ArrayLike, default=0. / u.ms + Coefficient describes dependence of V_th on membrane potential. + b : ArrayLike, default=0.01 / u.ms + Coefficient describes V_th update. + k1 : ArrayLike, default=0.2 / u.ms + Constant of I1. + k2 : ArrayLike, default=0.02 / u.ms + Constant of I2. + R1 : ArrayLike, default=0. + Free parameter. + R2 : ArrayLike, default=1. + Free parameter. + A1 : ArrayLike, default=0. * u.mA + Free parameter. + A2 : ArrayLike, default=0. * u.mA + Free parameter. + V_initializer : Callable + Initializer for the membrane potential state. + I1_initializer : Callable + Initializer for internal current I1. + I2_initializer : Callable + Initializer for internal current I2. + spk_fun : Callable, default=surrogate.ReluGrad() + Surrogate gradient function. + spk_reset : str, default='soft' + Reset mechanism after spike generation. + ref_var : bool, default=False + Whether to expose a boolean refractory state variable. + name : str, optional + Name of the neuron layer. + + Attributes + ---------- + V : HiddenState + Membrane potential. + I1 : HiddenState + Internal current 1. + I2 : HiddenState + Internal current 2. + V_th : HiddenState + Spiking threshold potential. + last_spike_time : ShortTermState + Last spike time recorder. + refractory : HiddenState + Neuron refractory state (if ref_var=True). + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + R: ArrayLike = 20. * u.ohm, + tau: ArrayLike = 20. * u.ms, + tau_ref: ArrayLike = 1.7 * u.ms, + V_rest: ArrayLike = -70. * u.mV, + V_reset: ArrayLike = -70. * u.mV, + V_th_inf: ArrayLike = -50. * u.mV, + V_th_reset: ArrayLike = -60. * u.mV, + V_th_initializer: Callable = braintools.init.Constant(-50. * u.mV), + a: ArrayLike = 0. / u.ms, + b: ArrayLike = 0.01 / u.ms, + k1: ArrayLike = 0.2 / u.ms, + k2: ArrayLike = 0.02 / u.ms, + R1: ArrayLike = 0., + R2: ArrayLike = 1., + A1: ArrayLike = 0. * u.mA, + A2: ArrayLike = 0. * u.mA, + V_initializer: Callable = braintools.init.Constant(-70. * u.mV), + I1_initializer: Callable = braintools.init.Constant(0. * u.mA), + I2_initializer: Callable = braintools.init.Constant(0. * u.mA), + spk_fun: Callable = braintools.surrogate.ReluGrad(), + spk_reset: str = 'soft', + ref_var: bool = False, + name: str = None, + ): + super().__init__(in_size, name=name, spk_fun=spk_fun, spk_reset=spk_reset) + + # parameters + self.R = braintools.init.param(R, self.varshape) + self.tau = braintools.init.param(tau, self.varshape) + self.tau_ref = braintools.init.param(tau_ref, self.varshape) + self.V_rest = braintools.init.param(V_rest, self.varshape) + self.V_reset = braintools.init.param(V_reset, self.varshape) + self.V_th_inf = braintools.init.param(V_th_inf, self.varshape) + self.V_th_reset = braintools.init.param(V_th_reset, self.varshape) + self.a = braintools.init.param(a, self.varshape) + self.b = braintools.init.param(b, self.varshape) + self.k1 = braintools.init.param(k1, self.varshape) + self.k2 = braintools.init.param(k2, self.varshape) + self.R1 = braintools.init.param(R1, self.varshape) + self.R2 = braintools.init.param(R2, self.varshape) + self.A1 = braintools.init.param(A1, self.varshape) + self.A2 = braintools.init.param(A2, self.varshape) + self.V_initializer = V_initializer + self.I1_initializer = I1_initializer + self.I2_initializer = I2_initializer + self.V_th_initializer = V_th_initializer + self.ref_var = ref_var + + def init_state(self, batch_size: int = None, **kwargs): + self.V = brainstate.HiddenState(braintools.init.param(self.V_initializer, self.varshape, batch_size)) + self.I1 = brainstate.HiddenState(braintools.init.param(self.I1_initializer, self.varshape, batch_size)) + self.I2 = brainstate.HiddenState(braintools.init.param(self.I2_initializer, self.varshape, batch_size)) + self.V_th = brainstate.HiddenState(braintools.init.param(self.V_th_initializer, self.varshape, batch_size)) + self.last_spike_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + if self.ref_var: + self.refractory = brainstate.HiddenState( + braintools.init.param(braintools.init.Constant(False), self.varshape, batch_size) + ) + + def reset_state(self, batch_size: int = None, **kwargs): + self.V.value = braintools.init.param(self.V_initializer, self.varshape, batch_size) + self.I1.value = braintools.init.param(self.I1_initializer, self.varshape, batch_size) + self.I2.value = braintools.init.param(self.I2_initializer, self.varshape, batch_size) + self.V_th.value = braintools.init.param(self.V_th_initializer, self.varshape, batch_size) + self.last_spike_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size + ) + if self.ref_var: + self.refractory.value = braintools.init.param( + braintools.init.Constant(False), self.varshape, batch_size + ) + + def get_spike(self, V: ArrayLike = None, V_th: ArrayLike = None): + V = self.V.value if V is None else V + V_th = self.V_th.value if V_th is None else V_th + v_scaled = (V - V_th) / (V_th - self.V_reset) + return self.spk_fun(v_scaled) + + def update(self, x=0. * u.mA): + t = brainstate.environ.get('t') + last_v = self.V.value + last_i1 = self.I1.value + last_i2 = self.I2.value + last_v_th = self.V_th.value + last_spk = self.get_spike(last_v, last_v_th) + + # Apply spike effects + V_th_val = last_v_th if self.spk_reset == 'soft' else jax.lax.stop_gradient(last_v) + v_reset = last_v - (V_th_val - self.V_reset) * last_spk + i1_reset = last_i1 + last_spk * (self.R1 * last_i1 + self.A1 - last_i1) + i2_reset = last_i2 + last_spk * (self.R2 * last_i2 + self.A2 - last_i2) + v_th_reset = last_v_th + last_spk * (u.math.maximum(self.V_th_reset, last_v_th) - last_v_th) + + # Update dynamics + def dI1(i1): + return -self.k1 * i1 + + def dI2(i2): + return -self.k2 * i2 + + def dV_th_func(v_th): + return self.a * (v_reset - self.V_rest) - self.b * (v_th - self.V_th_inf) + + def dv(v): + return (-(v - self.V_rest) + self.R * (i1_reset + i2_reset + self.sum_current_inputs(x, v))) / self.tau + + I1_candidate = brainstate.nn.exp_euler_step(dI1, i1_reset) + I2_candidate = brainstate.nn.exp_euler_step(dI2, i2_reset) + V_th_candidate = brainstate.nn.exp_euler_step(dV_th_func, v_th_reset) + V_candidate = brainstate.nn.exp_euler_step(dv, v_reset) + V_candidate = self.sum_delta_inputs(V_candidate) + + refractory = (t - self.last_spike_time.value) < self.tau_ref + self.V.value = u.math.where(refractory, v_reset, V_candidate) + self.I1.value = u.math.where(refractory, i1_reset, I1_candidate) + self.I2.value = u.math.where(refractory, i2_reset, I2_candidate) + self.V_th.value = u.math.where(refractory, v_th_reset, V_th_candidate) + + spike_cond = self.V.value >= self.V_th.value + self.last_spike_time.value = jax.lax.stop_gradient( + u.math.where(spike_cond, t, self.last_spike_time.value) + ) + if self.ref_var: + self.refractory.value = jax.lax.stop_gradient( + u.math.logical_or(refractory, spike_cond) + ) + return self.get_spike() diff --git a/brainpy/state/_lif_test.py b/brainpy/state/_lif_test.py index 1f320f2f..f0587134 100644 --- a/brainpy/state/_lif_test.py +++ b/brainpy/state/_lif_test.py @@ -23,7 +23,7 @@ import jax import jax.numpy as jnp -from brainpy.state import IF, LIF, ALIF +from brainpy.state import IF, LIF, LIFRef, ALIF, ExpIF, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef class TestNeuron(unittest.TestCase): @@ -31,12 +31,13 @@ def setUp(self): self.in_size = 10 self.batch_size = 5 self.time_steps = 100 + self.dt = 0.1 * u.ms def generate_input(self): return brainstate.random.randn(self.time_steps, self.batch_size, self.in_size) * u.mA def test_if_neuron(self): - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): neuron = IF(self.in_size) inputs = self.generate_input() @@ -57,7 +58,7 @@ def test_if_neuron(self): self.assertTrue(jnp.all((spikes >= 0) & (spikes <= 1))) def test_lif_neuron(self): - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): tau = 20.0 * u.ms neuron = LIF(self.in_size, tau=tau) inputs = self.generate_input() @@ -90,13 +91,240 @@ def test_alif_neuron(self): # Test forward pass neuron.init_state(self.batch_size) call = brainstate.compile.jit(neuron) - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): for t in range(self.time_steps): out = call(inputs[t]) self.assertEqual(out.shape, (self.batch_size, self.in_size)) + def test_expif_neuron(self): + tau = 10.0 * u.ms + neuron = ExpIF(self.in_size, tau=tau) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + + # Test forward pass + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + def test_LIFRef_neuron(self): + tau = 10.0 * u.ms + tau_ref = 2.0 * u.ms + neuron = LIFRef(self.in_size, tau=tau, tau_ref=tau_ref) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_ref, tau_ref) + + neuron.init_state(self.batch_size) + + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + + def test_ExpIFRef_neuron(self): + tau = 10.0 * u.ms + tau_ref = 2.0 * u.ms + ref_var = True + neuron = ExpIFRef(self.in_size, tau=tau, tau_ref=tau_ref, ref_var=ref_var) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_ref, tau_ref) + self.assertEqual(neuron.ref_var, ref_var) + + neuron.init_state(self.batch_size) + + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + if neuron.ref_var: + self.assertEqual(neuron.refractory.value.shape, (self.batch_size, self.in_size)) + + def test_adexif_neuron(self): + tau = 10.0 * u.ms + tau_w = 30.0 * u.ms + neuron = AdExIF(self.in_size, tau=tau, tau_w=tau_w) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_w, tau_w) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.w.value.shape, (self.batch_size, self.in_size)) + + def test_adexifref_neuron(self): + tau = 10.0 * u.ms + tau_w = 30.0 * u.ms + tau_ref = 2.0 * u.ms + ref_var = True + neuron = AdExIFRef(self.in_size, tau=tau, tau_w=tau_w, tau_ref=tau_ref, ref_var=ref_var) + inputs = self.generate_input() + + # Test initialization + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_w, tau_w) + self.assertEqual(neuron.tau_ref, tau_ref) + self.assertEqual(neuron.ref_var, ref_var) + + neuron.init_state(self.batch_size) + + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + # Test state variables + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.w.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + if neuron.ref_var: + self.assertEqual(neuron.refractory.value.shape, (self.batch_size, self.in_size)) + + def test_quaif_neuron(self): + tau = 10.0 * u.ms + neuron = QuaIF(self.in_size, tau=tau) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + def test_adquaif_neuron(self): + tau = 10.0 * u.ms + tau_w = 30.0 * u.ms + neuron = AdQuaIF(self.in_size, tau=tau, tau_w=tau_w) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_w, tau_w) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.w.value.shape, (self.batch_size, self.in_size)) + + def test_adquaifref_neuron(self): + tau = 10.0 * u.ms + tau_w = 30.0 * u.ms + tau_ref = 2.0 * u.ms + ref_var = True + neuron = AdQuaIFRef(self.in_size, tau=tau, tau_w=tau_w, tau_ref=tau_ref, ref_var=ref_var) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_w, tau_w) + self.assertEqual(neuron.tau_ref, tau_ref) + self.assertEqual(neuron.ref_var, ref_var) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.w.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + if neuron.ref_var: + self.assertEqual(neuron.refractory.value.shape, (self.batch_size, self.in_size)) + + def test_gif_neuron(self): + tau = 20.0 * u.ms + neuron = Gif(self.in_size, tau=tau) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.I1.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.I2.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.V_th.value.shape, (self.batch_size, self.in_size)) + + def test_gifref_neuron(self): + tau = 20.0 * u.ms + tau_ref = 2.0 * u.ms + ref_var = True + neuron = GifRef(self.in_size, tau=tau, tau_ref=tau_ref, ref_var=ref_var) + inputs = self.generate_input() + + self.assertEqual(neuron.in_size, (self.in_size,)) + self.assertEqual(neuron.out_size, (self.in_size,)) + self.assertEqual(neuron.tau, tau) + self.assertEqual(neuron.tau_ref, tau_ref) + self.assertEqual(neuron.ref_var, ref_var) + + neuron.init_state(self.batch_size) + call = brainstate.compile.jit(neuron) + with brainstate.environ.context(dt=self.dt): + for t in range(self.time_steps): + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, self.in_size)) + + self.assertEqual(neuron.V.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.I1.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.I2.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.V_th.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(neuron.last_spike_time.value.shape, (self.batch_size, self.in_size)) + if neuron.ref_var: + self.assertEqual(neuron.refractory.value.shape, (self.batch_size, self.in_size)) + def test_spike_function(self): - for NeuronClass in [IF, LIF, ALIF]: + for NeuronClass in [IF, LIF, ALIF, ExpIF, LIFRef, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef]: neuron = NeuronClass(self.in_size) neuron.init_state() v = jnp.linspace(-1, 1, self.in_size) * u.mV @@ -104,41 +332,48 @@ def test_spike_function(self): self.assertTrue(jnp.all((spikes >= 0) & (spikes <= 1))) def test_soft_reset(self): - for NeuronClass in [IF, LIF, ALIF]: + for NeuronClass in [IF, LIF, ALIF, ExpIF, LIFRef, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef]: neuron = NeuronClass(self.in_size, spk_reset='soft') inputs = self.generate_input() state = neuron.init_state(self.batch_size) call = brainstate.compile.jit(neuron) - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): for t in range(self.time_steps): - out = call(inputs[t]) - self.assertTrue(jnp.all(neuron.V.value <= neuron.V_th)) + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + # For Gif models, V_th is a state variable + V_th = neuron.V_th.value if hasattr(neuron.V_th, 'value') else neuron.V_th + self.assertTrue(jnp.all(neuron.V.value <= V_th)) def test_hard_reset(self): - for NeuronClass in [IF, LIF, ALIF]: + for NeuronClass in [IF, LIF, ALIF, ExpIF, LIFRef, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef]: neuron = NeuronClass(self.in_size, spk_reset='hard') inputs = self.generate_input() state = neuron.init_state(self.batch_size) call = brainstate.compile.jit(neuron) - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): for t in range(self.time_steps): - out = call(inputs[t]) - self.assertTrue(jnp.all((neuron.V.value < neuron.V_th) | (neuron.V.value == 0. * u.mV))) + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + # For Gif models, V_th is a state variable + V_th = neuron.V_th.value if hasattr(neuron.V_th, 'value') else neuron.V_th + self.assertTrue(jnp.all((neuron.V.value < V_th) | (neuron.V.value == 0. * u.mV))) def test_detach_spike(self): - for NeuronClass in [IF, LIF, ALIF]: + for NeuronClass in [IF, LIF, ALIF, ExpIF, LIFRef, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef]: neuron = NeuronClass(self.in_size) inputs = self.generate_input() state = neuron.init_state(self.batch_size) call = brainstate.compile.jit(neuron) - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): for t in range(self.time_steps): - out = call(inputs[t]) - self.assertFalse(jax.tree_util.tree_leaves(out)[0].aval.weak_type) + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertFalse(jax.tree_util.tree_leaves(out)[0].aval.weak_type) def test_keep_size(self): in_size = (2, 3) - for NeuronClass in [IF, LIF, ALIF]: + for NeuronClass in [IF, LIF, ALIF, ExpIF, LIFRef, ExpIFRef, AdExIF, AdExIFRef, QuaIF, AdQuaIF, AdQuaIFRef, Gif, GifRef]: neuron = NeuronClass(in_size) self.assertEqual(neuron.in_size, in_size) self.assertEqual(neuron.out_size, in_size) @@ -146,12 +381,14 @@ def test_keep_size(self): inputs = brainstate.random.randn(self.time_steps, self.batch_size, *in_size) * u.mA state = neuron.init_state(self.batch_size) call = brainstate.compile.jit(neuron) - with brainstate.environ.context(dt=0.1 * u.ms): + with brainstate.environ.context(dt=self.dt): for t in range(self.time_steps): - out = call(inputs[t]) - self.assertEqual(out.shape, (self.batch_size, *in_size)) + with brainstate.environ.context(t=t*self.dt): + out = call(inputs[t]) + self.assertEqual(out.shape, (self.batch_size, *in_size)) if __name__ == '__main__': - with brainstate.environ.context(dt=0.1): - unittest.main() + # with brainstate.environ.context(dt=0.1): + # unittest.main() + unittest.main() diff --git a/brainpy/state/_synapse.py b/brainpy/state/_synapse.py index 625d479a..beb04800 100644 --- a/brainpy/state/_synapse.py +++ b/brainpy/state/_synapse.py @@ -26,7 +26,7 @@ from ._base import Synapse __all__ = [ - 'Alpha', 'AMPA', 'GABAa', + 'Alpha', 'AMPA', 'GABAa', 'BioNMDA', ] @@ -310,3 +310,159 @@ def __init__( in_size=in_size, g_initializer=g_initializer ) + + +class BioNMDA(Synapse): + r"""Biological NMDA receptor synapse model. + + This class implements a detailed kinetic model of NMDA (N-methyl-D-aspartate) + receptor-mediated synaptic transmission. NMDA receptors are ionotropic glutamate + receptors that play a crucial role in synaptic plasticity, learning, and memory. + + Unlike AMPA receptors, NMDA receptors exhibit both ligand-gating and voltage-dependent + properties. The voltage dependence arises from the blocking of the receptor pore by + extracellular magnesium ions (Mg²⁺) at resting potential. The model uses a second-order + kinetic scheme to capture the dynamics of NMDA receptors: + + $$ + \frac{dg}{dt} = \alpha_1 x (1-g) - \beta_1 g + $$ + + $$ + \frac{dx}{dt} = \alpha_2 [T] (1-x) - \beta_2 x + $$ + + $$ + I_{syn} = g_{max} \cdot g \cdot g_{\infty}(V,[Mg^{2+}]_o) \cdot (V - E) + $$ + + where: + + - $g$ represents the fraction of receptors in the open state + - $x$ is an auxiliary variable representing an intermediate state + - $\alpha_1, \beta_1$ are the conversion rates for the $g$ variable [ms^-1] + - $\alpha_2, \beta_2$ are the conversion rates for the $x$ variable [ms^-1] or [ms^-1 mM^-1] + - $[T]$ is the neurotransmitter (glutamate) concentration [mM] + - $g_{\infty}(V,[Mg^{2+}]_o)$ is the voltage-dependent magnesium block function + - $I_{syn}$ is the resulting synaptic current + - $g_{max}$ is the maximum conductance + - $V$ is the membrane potential + - $E$ is the reversal potential + + The magnesium block is typically modeled as: + + $$ + g_{\infty}(V,[Mg^{2+}]_o) = \frac{1}{1 + [Mg^{2+}]_o \cdot \exp(-a \cdot V) / b} + $$ + + The neurotransmitter concentration $[T]$ follows a square pulse of amplitude T and + duration T_dur after each presynaptic spike. + + Parameters + ---------- + in_size : Size + Size of the input. + name : str, optional + Name of the synapse instance. + alpha1 : ArrayLike, default=2.0/u.ms + Conversion rate of g from inactive to active [ms^-1]. + beta1 : ArrayLike, default=0.01/u.ms + Conversion rate of g from active to inactive [ms^-1]. + alpha2 : ArrayLike, default=1.0/(u.ms*u.mM) + Conversion rate of x from inactive to active [ms^-1 mM^-1]. + beta2 : ArrayLike, default=0.5/u.ms + Conversion rate of x from active to inactive [ms^-1]. + T : ArrayLike, default=1.0*u.mM + Peak neurotransmitter concentration when released [mM]. + T_dur : ArrayLike, default=0.5*u.ms + Duration of neurotransmitter presence in the synaptic cleft [ms]. + g_initializer : ArrayLike or Callable, default=init.Constant(0. * u.mS) + Initial value or initializer for the synaptic conductance. + x_initializer : ArrayLike or Callable, default=init.Constant(0.) + Initial value or initializer for the auxiliary state variable. + + Attributes + ---------- + g : HiddenState + Fraction of receptors in the open state. + x : HiddenState + Auxiliary state variable representing intermediate receptor state. + spike_arrival_time : ShortTermState + Time of the most recent presynaptic spike. + + Notes + ----- + - NMDA receptors have slower kinetics compared to AMPA receptors, with rise times + of several milliseconds and decay time constants of tens to hundreds of milliseconds. + - The voltage-dependent magnesium block is typically implemented in the output layer + or postsynaptic neuron model, not in this synapse model itself. + - NMDA receptors are permeable to calcium ions, which can trigger various intracellular + signaling cascades important for synaptic plasticity. + - This implementation uses an exponential Euler integration method for both state variables. + + References + ---------- + .. [1] Devaney, A. J. (2010). Mathematical Foundations of Neuroscience. Springer New York, 162. + .. [2] Furukawa, H., Singh, S. K., Mancusso, R., & Gouaux, E. (2005). Subunit arrangement + and function in NMDA receptors. Nature, 438(7065), 185-192. + .. [3] Li, F., & Tsien, J. Z. (2009). Memory and the NMDA receptors. The New England + Journal of Medicine, 361(3), 302. + .. [4] Jahr, C. E., & Stevens, C. F. (1990). Voltage dependence of NMDA-activated + macroscopic conductances predicted by single-channel kinetics. Journal of Neuroscience, + 10(9), 3178-3182. + """ + __module__ = 'brainpy' + + def __init__( + self, + in_size: Size, + name: Optional[str] = None, + alpha1: ArrayLike = 2.0 / u.ms, + beta1: ArrayLike = 0.01 / u.ms, + alpha2: ArrayLike = 1.0 / (u.ms * u.mM), + beta2: ArrayLike = 0.5 / u.ms, + T: ArrayLike = 1.0 * u.mM, + T_dur: ArrayLike = 0.5 * u.ms, + g_initializer: ArrayLike | Callable = braintools.init.Constant(0. * u.mS), + x_initializer: ArrayLike | Callable = braintools.init.Constant(0.), + ): + super().__init__(name=name, in_size=in_size) + + # parameters + self.alpha1 = braintools.init.param(alpha1, self.varshape) + self.beta1 = braintools.init.param(beta1, self.varshape) + self.alpha2 = braintools.init.param(alpha2, self.varshape) + self.beta2 = braintools.init.param(beta2, self.varshape) + self.T = braintools.init.param(T, self.varshape) + self.T_duration = braintools.init.param(T_dur, self.varshape) + self.g_initializer = g_initializer + self.x_initializer = x_initializer + + def init_state(self, batch_size=None): + self.g = brainstate.HiddenState(braintools.init.param(self.g_initializer, self.varshape, batch_size)) + self.x = brainstate.HiddenState(braintools.init.param(self.x_initializer, self.varshape, batch_size)) + self.spike_arrival_time = brainstate.ShortTermState( + braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size) + ) + + def reset_state(self, batch_or_mode=None, **kwargs): + self.g.value = braintools.init.param(self.g_initializer, self.varshape, batch_or_mode) + self.x.value = braintools.init.param(self.x_initializer, self.varshape, batch_or_mode) + self.spike_arrival_time.value = braintools.init.param( + braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_or_mode + ) + + def update(self, pre_spike): + t = brainstate.environ.get('t') + self.spike_arrival_time.value = u.math.where(pre_spike, t, self.spike_arrival_time.value) + TT = ((t - self.spike_arrival_time.value) < self.T_duration) * self.T + + # Update x first (intermediate state) + dx = lambda x: self.alpha2 * TT * (1 * u.get_unit(x) - x) - self.beta2 * x + self.x.value = brainstate.nn.exp_euler_step(dx, self.x.value) + + # Update g (open state) based on current x value + dg = lambda g: self.alpha1 * self.x.value * (1 * u.get_unit(g) - g) - self.beta1 * g + self.g.value = brainstate.nn.exp_euler_step(dg, self.g.value) + + return self.g.value diff --git a/brainpy/state/_synapse_test.py b/brainpy/state/_synapse_test.py index 4154bd9d..0260fd9e 100644 --- a/brainpy/state/_synapse_test.py +++ b/brainpy/state/_synapse_test.py @@ -21,7 +21,7 @@ import jax.numpy as jnp import pytest -from brainpy.state import Expon, STP, STD +from brainpy.state import Expon, STP, STD, AMPA, GABAa, BioNMDA class TestSynapse(unittest.TestCase): @@ -125,7 +125,140 @@ def test_keep_size(self): out = call(inputs[t]) self.assertEqual(out.shape, (self.batch_size, *in_size)) + def test_ampa_synapse(self): + alpha = 0.98 / (u.ms * u.mM) + beta = 0.18 / u.ms + T = 0.5 * u.mM + T_dur = 0.5 * u.ms + synapse = AMPA(self.in_size, alpha=alpha, beta=beta, T=T, T_dur=T_dur) + + # Test initialization + self.assertEqual(synapse.in_size, (self.in_size,)) + self.assertEqual(synapse.out_size, (self.in_size,)) + self.assertEqual(synapse.alpha, alpha) + self.assertEqual(synapse.beta, beta) + self.assertEqual(synapse.T, T) + self.assertEqual(synapse.T_duration, T_dur) + + # Test forward pass + synapse.init_state(self.batch_size) + call = brainstate.compile.jit(synapse) + with brainstate.environ.context(dt=0.1 * u.ms, t=0. * u.ms): + # Test with spike input (True/False array) + spike_input = jnp.zeros((self.batch_size, self.in_size), dtype=bool) + spike_input = spike_input.at[0, 0].set(True) # Single spike + + out1 = call(spike_input) + self.assertEqual(out1.shape, (self.batch_size, self.in_size)) + + # Conductance should increase after spike + out2 = call(jnp.zeros((self.batch_size, self.in_size), dtype=bool)) + self.assertTrue(jnp.any(out2[0, 0] > 0 * u.mS)) # Should have some conductance + + def test_gabaa_synapse(self): + alpha = 0.53 / (u.ms * u.mM) + beta = 0.18 / u.ms + T = 1.0 * u.mM + T_dur = 1.0 * u.ms + synapse = GABAa(self.in_size, alpha=alpha, beta=beta, T=T, T_dur=T_dur) + + # Test initialization + self.assertEqual(synapse.in_size, (self.in_size,)) + self.assertEqual(synapse.out_size, (self.in_size,)) + self.assertEqual(synapse.alpha, alpha) + self.assertEqual(synapse.beta, beta) + self.assertEqual(synapse.T, T) + self.assertEqual(synapse.T_duration, T_dur) + + # Test forward pass + synapse.init_state(self.batch_size) + call = brainstate.compile.jit(synapse) + with brainstate.environ.context(dt=0.1 * u.ms, t=0. * u.ms): + spike_input = jnp.zeros((self.batch_size, self.in_size), dtype=bool) + spike_input = spike_input.at[0, 0].set(True) + + out1 = call(spike_input) + self.assertEqual(out1.shape, (self.batch_size, self.in_size)) + + # Conductance should increase after spike + out2 = call(jnp.zeros((self.batch_size, self.in_size), dtype=bool)) + self.assertTrue(jnp.any(out2[0, 0] > 0 * u.mS)) + + def test_bionmda_synapse(self): + alpha1 = 2.0 / u.ms + beta1 = 0.01 / u.ms + alpha2 = 1.0 / (u.ms * u.mM) + beta2 = 0.5 / u.ms + T = 1.0 * u.mM + T_dur = 0.5 * u.ms + synapse = BioNMDA(self.in_size, alpha1=alpha1, beta1=beta1, + alpha2=alpha2, beta2=beta2, T=T, T_dur=T_dur) + + # Test initialization + self.assertEqual(synapse.in_size, (self.in_size,)) + self.assertEqual(synapse.out_size, (self.in_size,)) + self.assertEqual(synapse.alpha1, alpha1) + self.assertEqual(synapse.beta1, beta1) + self.assertEqual(synapse.alpha2, alpha2) + self.assertEqual(synapse.beta2, beta2) + self.assertEqual(synapse.T, T) + self.assertEqual(synapse.T_duration, T_dur) + + # Test forward pass with spike inputs + synapse.init_state(self.batch_size) + call = brainstate.compile.jit(synapse) + with brainstate.environ.context(dt=0.1 * u.ms, t=0. * u.ms): + # Create spike input at first time step + spike_input = jnp.zeros((self.batch_size, self.in_size), dtype=bool) + spike_input = spike_input.at[0, 0].set(True) # Single spike at position (0, 0) + + # First call with spike + out1 = call(spike_input) + self.assertEqual(out1.shape, (self.batch_size, self.in_size)) + + # Verify state variables exist and have correct shape + self.assertEqual(synapse.g.value.shape, (self.batch_size, self.in_size)) + self.assertEqual(synapse.x.value.shape, (self.batch_size, self.in_size)) + + # Continue simulation without spikes + no_spike = jnp.zeros((self.batch_size, self.in_size), dtype=bool) + + # NMDA should have slower dynamics - collect several time points + outputs = [out1] + for _ in range(10): + out = call(no_spike) + outputs.append(out) + + # Check that conductance increases over time initially (slower rise time for NMDA) + # Due to the two-state kinetics, there should be some non-zero conductance + self.assertTrue(jnp.any(outputs[-1][0, 0] >= 0 * u.mS)) # Should have developed some conductance + + def test_bionmda_two_state_dynamics(self): + """Test that BioNMDA properly implements second-order kinetics with two state variables""" + synapse = BioNMDA(self.in_size) + synapse.init_state(self.batch_size) + call = brainstate.compile.jit(synapse) + + with brainstate.environ.context(dt=0.1 * u.ms, t=0. * u.ms): + # Initial state should be zero (g has units, x is dimensionless) + self.assertTrue(jnp.allclose(synapse.g.value.to_decimal(u.mS), 0.)) + self.assertTrue(jnp.allclose(synapse.x.value, 0.)) + + # Apply a spike + spike_input = jnp.zeros((self.batch_size, self.in_size), dtype=bool) + spike_input = spike_input.at[0, 0].set(True) + + call(spike_input) + + # After spike, both x and g should be non-negative + x_val = synapse.x.value[0, 0] + g_val = synapse.g.value[0, 0] + + # x is dimensionless, g has units + self.assertTrue(x_val >= 0) + self.assertTrue(g_val >= 0 * u.mS) + if __name__ == '__main__': - with brainstate.environ.context(dt=0.1): + with brainstate.environ.context(dt=0.1 * u.ms): unittest.main() From 23f0e9fcab1e98f09e6262254dbe4aebf86be3e3 Mon Sep 17 00:00:00 2001 From: Minghao Wang <932179209@qq.com> Date: Mon, 13 Oct 2025 09:36:14 +0000 Subject: [PATCH 4/8] Add Examples sections to neuron classes in _lif.py Added comprehensive Examples sections to 5 neuron classes that were missing them: - QuaIF: Quadratic Integrate-and-Fire neuron - AdQuaIF: Adaptive Quadratic Integrate-and-Fire neuron - AdQuaIFRef: AdQuaIF with refractory period - Gif: Generalized Integrate-and-Fire neuron - GifRef: Gif with refractory period Each Examples section includes: - Import statements for required modules - Basic usage with parameter specifications - State initialization examples - Update and spike generation examples - Network integration with brainstate.nn.Sequential - Additional Notes highlighting key features All 13 neuron classes in _lif.py now have complete documentation with Examples sections following consistent format and style. --- brainpy/state/_lif.py | 152 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) diff --git a/brainpy/state/_lif.py b/brainpy/state/_lif.py index c6983700..5388580e 100644 --- a/brainpy/state/_lif.py +++ b/brainpy/state/_lif.py @@ -1356,6 +1356,34 @@ class QuaIF(Neuron): V : HiddenState Membrane potential. + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a QuaIF neuron layer with 10 neurons + >>> quaif = brainpy.state.QuaIF(10, tau=10*u.ms, V_th=-30*u.mV, V_c=-50*u.mV) + >>> + >>> # Initialize the state + >>> quaif.init_state(batch_size=1) + >>> + >>> # Apply an input current and update the neuron state + >>> spikes = quaif.update(x=2.5*u.mA) + >>> + >>> # Create a network with QuaIF neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.QuaIF(100, tau=10.0*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - The quadratic nonlinearity provides a more realistic spike initiation compared to LIF. + - The critical voltage V_c determines the onset of spike generation. + - When V approaches V_c, the quadratic term causes rapid acceleration toward threshold. + - This model can exhibit Type I excitability (continuous f-I curve). + References ---------- .. [1] P. E. Latham, B.J. Richmond, P. Nelson and S. Nirenberg @@ -1479,6 +1507,36 @@ class AdQuaIF(Neuron): w : HiddenState Adaptation current. + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an AdQuaIF neuron layer with 10 neurons + >>> adquaif = brainpy.state.AdQuaIF(10, tau=10*u.ms, tau_w=100*u.ms, + ... a=1.0*u.siemens, b=0.1*u.mA) + >>> + >>> # Initialize the state + >>> adquaif.init_state(batch_size=1) + >>> + >>> # Apply an input current and observe spike-frequency adaptation + >>> spikes = adquaif.update(x=3.0*u.mA) + >>> + >>> # Create a network with adaptive neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.AdQuaIF(100, tau=10.0*u.ms, tau_w=100.0*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - The adaptation current w provides negative feedback, reducing firing rate. + - Parameter 'a' controls subthreshold adaptation (coupling from V to w). + - Parameter 'b' controls spike-triggered adaptation (increment after spike). + - With appropriate parameters, can exhibit regular spiking, bursting, etc. + - The adaptation time constant tau_w determines adaptation speed. + References ---------- .. [1] Izhikevich, E. M. (2004). Which model to use for cortical spiking @@ -1616,6 +1674,37 @@ class AdQuaIFRef(Neuron): Last spike time recorder. refractory : HiddenState Neuron refractory state (if ref_var=True). + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create an AdQuaIFRef neuron layer with refractory period + >>> adquaif_ref = brainpy.state.AdQuaIFRef(10, tau=10*u.ms, tau_w=100*u.ms, + ... tau_ref=2.0*u.ms, ref_var=True) + >>> + >>> # Initialize the state + >>> adquaif_ref.init_state(batch_size=1) + >>> + >>> # Apply input and observe refractory behavior + >>> with brainstate.environ.context(dt=0.1*u.ms, t=0.0*u.ms): + ... spikes = adquaif_ref.update(x=3.0*u.mA) + >>> + >>> # Create a network with refractory adaptive neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.AdQuaIFRef(100, tau=10.0*u.ms, tau_ref=2.0*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - Combines spike-frequency adaptation with absolute refractory period. + - During refractory period, neuron state is held at reset values. + - Set ref_var=True to track refractory state as a boolean variable. + - Refractory period prevents unrealistically high firing rates. + - More biologically realistic than AdQuaIF without refractory period. """ __module__ = 'brainpy' @@ -1809,6 +1898,37 @@ class Gif(Neuron): V_th : HiddenState Spiking threshold potential. + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a Gif neuron layer with dynamic threshold + >>> gif = brainpy.state.Gif(10, tau=20*u.ms, k1=0.2/u.ms, k2=0.02/u.ms, + ... a=0.005/u.ms, b=0.01/u.ms) + >>> + >>> # Initialize the state + >>> gif.init_state(batch_size=1) + >>> + >>> # Apply input and observe diverse firing patterns + >>> spikes = gif.update(x=1.5*u.mA) + >>> + >>> # Create a network with Gif neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.Gif(100, tau=20.0*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - The Gif model uses internal currents (I1, I2) for complex dynamics. + - Dynamic threshold V_th adapts based on membrane potential and its own dynamics. + - Can reproduce diverse firing patterns: regular spiking, bursting, adaptation. + - Parameters a and b control threshold adaptation. + - Parameters k1, k2, R1, R2, A1, A2 control internal current dynamics. + - More flexible than simpler IF models for matching biological data. + References ---------- .. [1] Mihalaş, Ştefan, and Ernst Niebur. "A generalized linear @@ -1998,6 +2118,38 @@ class GifRef(Neuron): Last spike time recorder. refractory : HiddenState Neuron refractory state (if ref_var=True). + + Examples + -------- + >>> import brainpy + >>> import brainstate + >>> import brainunit as u + >>> + >>> # Create a GifRef neuron layer with refractory period + >>> gif_ref = brainpy.state.GifRef(10, tau=20*u.ms, tau_ref=2.0*u.ms, + ... k1=0.2/u.ms, k2=0.02/u.ms, ref_var=True) + >>> + >>> # Initialize the state + >>> gif_ref.init_state(batch_size=1) + >>> + >>> # Apply input and observe refractory behavior + >>> with brainstate.environ.context(dt=0.1*u.ms, t=0.0*u.ms): + ... spikes = gif_ref.update(x=1.5*u.mA) + >>> + >>> # Create a network with refractory Gif neurons + >>> network = brainstate.nn.Sequential([ + ... brainpy.state.GifRef(100, tau=20.0*u.ms, tau_ref=2.0*u.ms), + ... brainstate.nn.Linear(100, 10) + ... ]) + + Notes + ----- + - Combines Gif model's rich dynamics with absolute refractory period. + - During refractory period, all state variables are held at reset values. + - Set ref_var=True to track refractory state as a boolean variable. + - More biologically realistic than Gif without refractory mechanism. + - Can still exhibit diverse firing patterns: regular, bursting, adaptation. + - Refractory period prevents unrealistically high firing rates. """ __module__ = 'brainpy' From 06b2bf4d6acfeb8d6e9944a86756727da6c515a9 Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Mon, 13 Oct 2025 17:56:14 +0800 Subject: [PATCH 5/8] Refactor module assignments to 'brainpy.state' for consistency across files --- brainpy/state/__init__.py | 15 +++++---------- brainpy/state/_base.py | 6 ++++-- brainpy/state/_exponential.py | 4 ++-- brainpy/state/_hh.py | 6 +++--- brainpy/state/_inputs.py | 10 +++++----- brainpy/state/_izhikevich.py | 4 ++-- brainpy/state/_lif.py | 26 +++++++++++++------------- brainpy/state/_projection.py | 10 ++++++---- brainpy/state/_readout.py | 4 ++-- brainpy/state/_stp.py | 4 ++-- brainpy/state/_synapse.py | 8 ++++---- brainpy/state/_synaptic_projection.py | 10 ++++++++-- brainpy/state/_synouts.py | 8 ++++---- 13 files changed, 60 insertions(+), 55 deletions(-) diff --git a/brainpy/state/__init__.py b/brainpy/state/__init__.py index 2186574c..8fbeed14 100644 --- a/brainpy/state/__init__.py +++ b/brainpy/state/__init__.py @@ -18,14 +18,14 @@ from ._base import __all__ as base_all from ._exponential import * from ._exponential import __all__ as exp_all +from ._hh import * +from ._hh import __all__ as hh_all from ._inputs import * from ._inputs import __all__ as inputs_all -from ._lif import * -from ._lif import __all__ as neuron_all from ._izhikevich import * from ._izhikevich import __all__ as izh_all -from ._hh import * -from ._hh import __all__ as hh_all +from ._lif import * +from ._lif import __all__ as neuron_all from ._projection import * from ._projection import __all__ as proj_all from ._readout import * @@ -38,13 +38,8 @@ from ._synaptic_projection import __all__ as synproj_all from ._synouts import * from ._synouts import __all__ as synout_all -from .. import mixin -__main__ = ['version2', 'mixin'] + inputs_all + neuron_all + izh_all + hh_all + readout_all + stp_all + synapse_all +__main__ = inputs_all + neuron_all + izh_all + hh_all + readout_all + stp_all + synapse_all __main__ = __main__ + synout_all + base_all + exp_all + proj_all + synproj_all del inputs_all, neuron_all, izh_all, hh_all, readout_all, stp_all, synapse_all, synout_all, base_all del exp_all, proj_all, synproj_all - -if __name__ == '__main__': - mixin - diff --git a/brainpy/state/_base.py b/brainpy/state/_base.py index 8be13440..e1892bb8 100644 --- a/brainpy/state/_base.py +++ b/brainpy/state/_base.py @@ -40,6 +40,8 @@ def _input_label_repr(name: str, label: Optional[str] = None): class Dynamics(brainstate.nn.Dynamics): + __module__ = 'brainpy.state' + def __init__(self, in_size: Size, name: Optional[str] = None): # initialize super().__init__(name=name, in_size=in_size) @@ -595,7 +597,7 @@ class Neuron(Dynamics): .. [3] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -849,4 +851,4 @@ class Synapse(Dynamics): .. [3] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' diff --git a/brainpy/state/_exponential.py b/brainpy/state/_exponential.py index 1f0ff1b0..30b39189 100644 --- a/brainpy/state/_exponential.py +++ b/brainpy/state/_exponential.py @@ -71,7 +71,7 @@ class Expon(Synapse, AlignPost): where synaptic variables are aligned with post-synaptic neurons, enabling event-driven computation and more efficient handling of sparse connectivity patterns. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -156,7 +156,7 @@ class DualExpon(Synapse, AlignPost): where synaptic variables are aligned with post-synaptic neurons, enabling event-driven computation and more efficient handling of sparse connectivity patterns. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_hh.py b/brainpy/state/_hh.py index 26f78210..86d4f1f2 100644 --- a/brainpy/state/_hh.py +++ b/brainpy/state/_hh.py @@ -148,7 +148,7 @@ class HH(Neuron): .. [2] https://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -380,7 +380,7 @@ class MorrisLecar(Neuron): .. [3] https://en.wikipedia.org/wiki/Morris%E2%80%93Lecar_model """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -572,7 +572,7 @@ class WangBuzsakiHH(Neuron): neuroscience, 16(20), pp.6402-6413. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_inputs.py b/brainpy/state/_inputs.py index 6ab87a85..8e67d4ad 100644 --- a/brainpy/state/_inputs.py +++ b/brainpy/state/_inputs.py @@ -60,7 +60,7 @@ class SpikeTime(brainstate.nn.Dynamics): name : str, optional The name of the dynamic system. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -116,7 +116,7 @@ class PoissonSpike(brainstate.nn.Dynamics): """ Poisson Neuron Group. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -203,7 +203,7 @@ class PoissonEncoder(brainstate.nn.Dynamics): - The independence of spike generation between time steps results in renewal process statistics without memory of previous spiking history. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -316,7 +316,7 @@ class PoissonInput(brainstate.nn.Module): - The update method internally calls the poisson_input function which handles the spike generation and target state updates. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -348,7 +348,7 @@ def update(self): ) -@set_module_as('brainpy') +@set_module_as('brainpy.state') def poisson_input( freq: u.Quantity[u.Hz], num_input: int, diff --git a/brainpy/state/_izhikevich.py b/brainpy/state/_izhikevich.py index 7ac8a4f4..586e23c7 100644 --- a/brainpy/state/_izhikevich.py +++ b/brainpy/state/_izhikevich.py @@ -119,7 +119,7 @@ class Izhikevich(Neuron): IEEE transactions on neural networks, 15(5), 1063-1070. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -302,7 +302,7 @@ class IzhikevichRef(Neuron): IEEE transactions on neural networks, 15(5), 1063-1070. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_lif.py b/brainpy/state/_lif.py index 5388580e..713f4fe2 100644 --- a/brainpy/state/_lif.py +++ b/brainpy/state/_lif.py @@ -113,7 +113,7 @@ class IF(Neuron): I. Homogeneous synaptic input. Biological cybernetics, 95(1), 1-19. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -237,7 +237,7 @@ class LIF(Neuron): .. [2] Burkitt, A. N. (2006). A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological cybernetics, 95(1), 1-19. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -380,7 +380,7 @@ class ExpIF(Neuron): conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -506,7 +506,7 @@ class ExpIFRef(Neuron): >>> with brainstate.environ.context(t=t*0.1*u.ms): >>> spikes = expif.update(x=inputs[t]) """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -680,7 +680,7 @@ class AdExIF(Neuron): :class:`brainpy.dyn.AdExIF` for the dynamical-system counterpart. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -863,7 +863,7 @@ class AdExIFRef(Neuron): :class:`brainpy.dyn.AdExIFRef` for the dynamical-system counterpart. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1072,7 +1072,7 @@ class LIFRef(Neuron): .. [3] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on neural networks, 14(6), 1569-1572. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1244,7 +1244,7 @@ class ALIF(Neuron): the adaptive exponential integrate-and-fire model. Biological cybernetics, 99(4), 335-347. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1390,7 +1390,7 @@ class QuaIF(Neuron): (2000) Intrinsic dynamics in neuronal networks. I. Theory. J. Neurophysiology 83, pp. 808–827. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1545,7 +1545,7 @@ class AdQuaIF(Neuron): nonlinear integrate-and-fire neurons." SIAM Journal on Applied Mathematics 68, no. 4 (2008): 1045-1079. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1706,7 +1706,7 @@ class AdQuaIFRef(Neuron): - Refractory period prevents unrealistically high firing rates. - More biologically realistic than AdQuaIF without refractory period. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -1939,7 +1939,7 @@ class Gif(Neuron): leaky integrate-and-fire models classify multiple neuron types." Nature communications 9, no. 1 (2018): 1-15. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -2151,7 +2151,7 @@ class GifRef(Neuron): - Can still exhibit diverse firing patterns: regular, bursting, adaptation. - Refractory period prevents unrealistically high firing rates. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_projection.py b/brainpy/state/_projection.py index 5a47d510..49da161f 100644 --- a/brainpy/state/_projection.py +++ b/brainpy/state/_projection.py @@ -71,7 +71,7 @@ class Projection(brainstate.nn.Module): Derived classes should implement specific projection behaviors, such as dense connectivity, sparse connectivity, or specific weight update rules. """ - __module__ = 'brainstate.nn' + __module__ = 'brainpy.state' def update(self, *args, **kwargs): sub_nodes = tuple(self.nodes(allowed_hierarchy=(1, 1)).values()) @@ -181,7 +181,7 @@ class AlignPostProj(Projection): >>> exe_current = E(pop.get_spike()) """ - __module__ = 'brainstate.nn' + __module__ = 'brainpy.state' def __init__( self, @@ -303,7 +303,7 @@ class DeltaProj(Projection): ... ) >>> delta_input(1.0) # Apply voltage increment directly """ - __module__ = 'brainstate.nn' + __module__ = 'brainpy.state' def __init__( self, @@ -380,7 +380,7 @@ class CurrentProj(Projection): ... ) >>> current_input(0.2) # Apply external current """ - __module__ = 'brainstate.nn' + __module__ = 'brainpy.state' def __init__( self, @@ -439,6 +439,7 @@ class align_pre_projection(Projection): short-term plasticity. """ + __module__ = 'brainpy.state' def __init__( self, @@ -486,6 +487,7 @@ class align_post_projection(Projection): properties, synaptic outputs, post-synaptic dynamics, and short-term plasticity. """ + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_readout.py b/brainpy/state/_readout.py index 3775220a..2125f83f 100644 --- a/brainpy/state/_readout.py +++ b/brainpy/state/_readout.py @@ -74,7 +74,7 @@ class LeakyRateReadout(brainstate.nn.Module): r : HiddenState Hidden state representing the output values """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -160,7 +160,7 @@ class LeakySpikeReadout(Neuron): Synaptic weight matrix """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_stp.py b/brainpy/state/_stp.py index 8442bdd5..1dd53cb7 100644 --- a/brainpy/state/_stp.py +++ b/brainpy/state/_stp.py @@ -97,7 +97,7 @@ class STP(Synapse): .. [2] Tsodyks, M., Pawelzik, K., & Markram, H. (1998). Neural networks with dynamic synapses. Neural computation, 10(4), 821-835. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -203,7 +203,7 @@ class STD(Synapse): pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences, 94(2), 719-723. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_synapse.py b/brainpy/state/_synapse.py index beb04800..17811014 100644 --- a/brainpy/state/_synapse.py +++ b/brainpy/state/_synapse.py @@ -70,7 +70,7 @@ class Alpha(Synapse): This implementation uses an exponential Euler integration method. The output of this synapse is the conductance value. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -176,7 +176,7 @@ class AMPA(Synapse): and implications for stimulus processing. Proceedings of the National Academy of Sciences, 109(45), 18553-18558. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -289,7 +289,7 @@ class GABAa(AMPA): properties of neocortical pyramidal neurons in vivo. Journal of neurophysiology, 81(4), 1531-1547. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, @@ -411,7 +411,7 @@ class BioNMDA(Synapse): macroscopic conductances predicted by single-channel kinetics. Journal of Neuroscience, 10(9), 3178-3182. """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, diff --git a/brainpy/state/_synaptic_projection.py b/brainpy/state/_synaptic_projection.py index e291100c..b534d5ce 100644 --- a/brainpy/state/_synaptic_projection.py +++ b/brainpy/state/_synaptic_projection.py @@ -22,6 +22,7 @@ import brainunit as u from brainstate.typing import ArrayLike +from ._misc import set_module_as from ._projection import Projection __all__ = [ @@ -31,11 +32,11 @@ class align_pre_ltp(Projection): - pass + __module__ = 'brainpy.state' class align_post_ltp(Projection): - pass + __module__ = 'brainpy.state' def get_gap_junction_post_key(i: int): @@ -82,6 +83,8 @@ class SymmetryGapJunction(Projection): AsymmetryGapJunction : For gap junctions with different conductances in each direction. """ + __module__ = 'brainpy.state' + def __init__( self, couples: Union[Tuple[brainstate.nn.Dynamics, brainstate.nn.Dynamics], brainstate.nn.Dynamics], @@ -132,6 +135,7 @@ def update(self, *args, **kwargs): ) +@set_module_as('brainpy.state') def symmetry_gap_junction_projection( pre: brainstate.nn.Dynamics, pre_value: ArrayLike, @@ -281,6 +285,7 @@ class AsymmetryGapJunction(Projection): -------- SymmetryGapJunction : For gap junctions with identical conductance in both directions. """ + __module__ = 'brainpy.state' def __init__( self, @@ -322,6 +327,7 @@ def update(self, *args, **kwargs): ) +@set_module_as('brainpy.state') def asymmetry_gap_junction_projection( pre: brainstate.nn.Dynamics, pre_value: ArrayLike, diff --git a/brainpy/state/_synouts.py b/brainpy/state/_synouts.py index 54605e52..8592ded8 100644 --- a/brainpy/state/_synouts.py +++ b/brainpy/state/_synouts.py @@ -33,7 +33,7 @@ class SynOut(brainstate.nn.Module, BindCondData): :py:class:`~.SynOut` is also subclass of :py:class:`~.ParamDesc` and :py:class:`~.BindCondData`. """ - __module__ = 'brainstate.nn' + __module__ = 'brainpy.state' def __init__(self, ): super().__init__() @@ -71,7 +71,7 @@ class COBA(SynOut): -------- CUBA """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__(self, E: brainstate.typing.ArrayLike): super().__init__() @@ -100,7 +100,7 @@ class CUBA(SynOut): -------- COBA """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__(self, scale: brainstate.typing.ArrayLike = u.volt): super().__init__() @@ -140,7 +140,7 @@ class MgBlock(SynOut): V_offset: ArrayLike The offset potential. Default 0. [mV] """ - __module__ = 'brainpy' + __module__ = 'brainpy.state' def __init__( self, From 057b872d43fdff1814ee81f21ea93922298ea415 Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Mon, 13 Oct 2025 18:01:32 +0800 Subject: [PATCH 6/8] Update import paths in _base.py: change references from brainstate to brainpy for consistency --- brainpy/state/_base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/brainpy/state/_base.py b/brainpy/state/_base.py index ed9c573e..7b17dd78 100644 --- a/brainpy/state/_base.py +++ b/brainpy/state/_base.py @@ -403,8 +403,8 @@ def align_pre(self, dyn: Union[ParamDescriber[T], T]) -> T: Examples -------- >>> import brainstate - >>> n1 = brainstate.nn.LIF(10) - >>> n1.align_pre(brainstate.nn.Expon.desc(n1.varshape)) # n2 will run after n1 + >>> n1 = brainpy.state.LIF(10) + >>> n1.align_pre(brainpy.state.Expon.desc(n1.varshape)) # n2 will run after n1 """ if isinstance(dyn, Dynamics): self.add_after_update(id(dyn), dyn) @@ -427,7 +427,7 @@ class Neuron(Dynamics): Base class for all spiking neuron models. This abstract class serves as the foundation for implementing various spiking neuron - models in the BrainPy framework. It extends the ``brainstate.nn.Dynamics`` class and + models in the BrainPy framework. It extends the ``brainpy.state.Dynamics`` class and provides common functionality for spike generation, membrane potential dynamics, and surrogate gradient handling required for training spiking neural networks. From 95ec2037b4495ceea308c2df4be3bde68b20730d Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Mon, 13 Oct 2025 18:22:52 +0800 Subject: [PATCH 7/8] fix tests --- brainpy/state/_base_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/brainpy/state/_base_test.py b/brainpy/state/_base_test.py index b378ed55..f3dcdad5 100644 --- a/brainpy/state/_base_test.py +++ b/brainpy/state/_base_test.py @@ -148,7 +148,7 @@ def test_neuron_soft_vs_hard_reset(self): def test_neuron_module_attribute(self): """Test __module__ attribute is correctly set.""" neuron = Neuron(in_size=self.in_size) - self.assertEqual(neuron.__module__, 'brainpy') + self.assertEqual(neuron.__module__, 'brainpy.state') class TestSynapseBaseClass(unittest.TestCase): @@ -247,7 +247,7 @@ def update(self, x=None): def test_synapse_module_attribute(self): """Test __module__ attribute is correctly set.""" synapse = Synapse(in_size=self.in_size) - self.assertEqual(synapse.__module__, 'brainpy') + self.assertEqual(synapse.__module__, 'brainpy.state') def test_synapse_varshape_attribute(self): """Test varshape attribute is correctly set.""" From a08bcd7ae5f80426c34e33afab818a9899f7898d Mon Sep 17 00:00:00 2001 From: Chaoming Wang Date: Mon, 13 Oct 2025 18:32:47 +0800 Subject: [PATCH 8/8] Update import paths in _inputs.py: change references from brainpy to brainpy.state for consistency --- brainpy/state/_inputs.py | 16 +++--- changelog.md | 105 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+), 8 deletions(-) diff --git a/brainpy/state/_inputs.py b/brainpy/state/_inputs.py index 8e67d4ad..8b88f9c8 100644 --- a/brainpy/state/_inputs.py +++ b/brainpy/state/_inputs.py @@ -173,7 +173,7 @@ class PoissonEncoder(brainstate.nn.Dynamics): >>> import numpy as np >>> >>> # Create a Poisson encoder for 10 neurons - >>> encoder = brainpy.PoissonEncoder(10) + >>> encoder = brainpy.state.PoissonEncoder(10) >>> >>> # Generate spikes with varying firing rates >>> rates = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) * u.Hz @@ -186,7 +186,7 @@ class PoissonEncoder(brainstate.nn.Dynamics): >>> spike_train = encoder.update(firing_rates) >>> >>> # Feed the spikes into a spiking neural network - >>> neuron_layer = brainpy.LIF(10) + >>> neuron_layer = brainpy.state.LIF(10) >>> neuron_layer.init_state(batch_size=1) >>> output_spikes = neuron_layer.update(spike_train) @@ -278,11 +278,11 @@ class PoissonInput(brainstate.nn.Module): >>> import numpy as np >>> >>> # Create a neuron group with membrane potential - >>> neuron = brainpy.LIF(100) + >>> neuron = brainpy.state.LIF(100) >>> neuron.init_state(batch_size=1) >>> >>> # Add Poisson input to all neurons - >>> poisson_in = brainpy.PoissonInput( + >>> poisson_in = brainpy.state.PoissonInput( ... target=neuron.V, ... indices=None, ... num_input=200, @@ -292,7 +292,7 @@ class PoissonInput(brainstate.nn.Module): >>> >>> # Add Poisson input only to specific neurons >>> indices = np.array([0, 10, 20, 30]) - >>> specific_input = brainpy.PoissonInput( + >>> specific_input = brainpy.state.PoissonInput( ... target=neuron.V, ... indices=indices, ... num_input=50, @@ -421,7 +421,7 @@ def poisson_input( >>> V = brainstate.HiddenState(np.zeros(100) * u.mV) >>> >>> # Add Poisson input to all neurons at 50 Hz - >>> brainpy.poisson_input( + >>> brainpy.state.poisson_input( ... freq=50 * u.Hz, ... num_input=200, ... weight=0.1 * u.mV, @@ -430,7 +430,7 @@ def poisson_input( >>> >>> # Apply Poisson input only to a subset of neurons >>> indices = np.array([0, 10, 20, 30]) - >>> brainpy.poisson_input( + >>> brainpy.state.poisson_input( ... freq=100 * u.Hz, ... num_input=50, ... weight=0.2 * u.mV, @@ -441,7 +441,7 @@ def poisson_input( >>> # Apply input with refractory mask >>> refractory = np.zeros(100, dtype=bool) >>> refractory[40:60] = True # neurons 40-59 are in refractory period - >>> brainpy.poisson_input( + >>> brainpy.state.poisson_input( ... freq=75 * u.Hz, ... num_input=100, ... weight=0.15 * u.mV, diff --git a/changelog.md b/changelog.md index 097aa7c6..31105d61 100644 --- a/changelog.md +++ b/changelog.md @@ -1,5 +1,110 @@ # Changelog +## Version 2.7.1 + +**Release Date:** October 2025 + +This is a feature release that introduces new neuron and synapse models in the state-based API (`brainpy.state`) and enhances the Dynamics base class with improved input handling. + +### Major Changes + +#### New Neuron Models (brainpy.state) +- **LIF (Leaky Integrate-and-Fire) Variants**: Added comprehensive set of LIF neuron models + - `LIF`: Basic LIF neuron with exponential synaptic input + - `LifRef`: LIF with refractory period + - `ExpIF`: Exponential Integrate-and-Fire neuron + - `ExpIFRef`: ExpIF with refractory period + - `AdExIF`: Adaptive Exponential Integrate-and-Fire neuron + - `AdExIFRef`: AdExIF with refractory period + - `QuaIF`: Quadratic Integrate-and-Fire neuron + - `QuaIFRef`: QuaIF with refractory period + - `AdQuaIF`: Adaptive Quadratic Integrate-and-Fire neuron + - `AdQuaIFRef`: AdQuaIF with refractory period + - `GifRef`: Generalized Integrate-and-Fire with refractory period + +- **Izhikevich Neuron Models**: Added new Izhikevich neuron implementations + - `Izhikevich`: Basic Izhikevich neuron model + - `IzhikevichRef`: Izhikevich with refractory period + +- **Hodgkin-Huxley Model**: Added classic biophysical neuron model + - `HH`: Classic Hodgkin-Huxley model with Na+ and K+ channels + +#### New Synapse Models (brainpy.state) +- **BioNMDA**: Biological NMDA receptor with second-order kinetics + - Implements two-state cascade dynamics (x and g variables) + - Slower rise time compared to AMPA (biologically realistic) + - Comprehensive documentation with mathematical formulation + +### Features + +#### Model Implementation +- All new models use the brainstate ecosystem (HiddenState, ShortTermState, LongTermState) +- Proper unit support with brainunit integration +- Exponential Euler integration for numerical stability +- Batch processing support across all models +- Consistent API design following BrainPy v2.7+ architecture + +#### Dynamics Class Enhancements +- Enhanced input handling capabilities in the Dynamics base class +- Added new properties for better state management +- Improved integration with brainstate framework +- Refactored to use public methods instead of private counterparts for clarity + +#### Documentation +- Added comprehensive Examples sections to all neuron classes in `_lif.py` +- Each example includes: + - Import statements for required modules + - Basic usage with parameter specifications + - State initialization examples + - Update and spike generation examples + - Network integration with `brainstate.nn.Sequential` + - Notes highlighting key features +- All 13 neuron classes in `_lif.py` now have complete documentation +- Simplified documentation paths by removing 'core-concepts' and 'quickstart' prefixes in index.rst + +### Bug Fixes +- Fixed import paths in `_base.py`: changed references from brainstate to brainpy for consistency (057b872d) +- Fixed test suite issues (95ec2037) +- Fixed test suite for proper unit handling in synapse models + +### Code Quality +- Refactored module assignments to `brainpy.state` for consistency across files (06b2bf4d) +- Refactored method calls in `_base.py`: replaced private methods with public counterparts (210426ab) + +### Testing +- Added comprehensive test suites for all new neuron models +- Added AMPA and GABAa synapse tests +- Added tests for Izhikevich neuron variants +- Added tests for Hodgkin-Huxley model +- All tests passing with proper unit handling + +### Files Modified +- `brainpy/__init__.py`: Updated version to 2.7.1 +- `brainpy/state/_base.py`: Enhanced Dynamics class with improved input handling (447 lines added) +- `brainpy/state/_lif.py`: Added extensive LIF neuron variants (1862 lines total) +- `brainpy/state/_izhikevich.py`: New file with Izhikevich models (407 lines) +- `brainpy/state/_hh.py`: New file with Hodgkin-Huxley model (666 lines) +- `brainpy/state/_synapse.py`: Added BioNMDA model (158 lines) +- `brainpy/state/_projection.py`: Updated for consistency (43 lines modified) +- `brainpy/state/__init__.py`: Updated exports for new models +- Test files added: `_lif_test.py`, `_izhikevich_test.py`, `_hh_test.py`, `_synapse_test.py`, `_base_test.py` +- Documentation updates in `docs_state/index.rst` + +### Removed +- Removed outdated documentation notebooks from `docs_state/`: + - `checkpointing-en.ipynb` and `checkpointing-zh.ipynb` + - `snn_simulation-en.ipynb` and `snn_simulation-zh.ipynb` + - `snn_training-en.ipynb` and `snn_training-zh.ipynb` + +### Notes +- This release significantly expands the `brainpy.state` module with biologically realistic neuron and synapse models +- All new models are fully compatible with the brainstate ecosystem +- Enhanced documentation provides clear usage examples for all models +- The Dynamics class refactoring improves the foundation for future state-based model development + + + + ## Version 3.0.1 **Release Date:** October 2025