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.. image:: https://github.com/PKU-NIP-Lab/BrainPy/blob/master/docs/images/logo.png
.. image:: docs/images/logo.png
:target: https://github.com/PKU-NIP-Lab/BrainPy
:align: center
:alt: Logo
Expand Down Expand Up @@ -34,17 +34,16 @@ Why to use BrainPy

``BrainPy`` is a lightweight framework based on the latest Just-In-Time (JIT)
compilers (especially `Numba <https://numba.pydata.org/>`_).
The goal of ``BrainPy`` is to provide a highly flexible and efficient
neural simulation framework for Python users.
It endows the users with the fully data/logic flow control.
Besides highly flexible, BrainPy is very fast (see the following comparison figure).
Currently, BrainPy supports JIT acceleration on CPU. In future, we will
support GPU acceleration.
The goal of ``BrainPy`` is to provide a unified simulation and analysis framework
for neuronal dynamics with the feature of high flexibility and efficiency.
BrainPy is flexible because it endows the users with the fully data/logic flow control.
BrainPy is efficient because it supports JIT acceleration on CPUs
(see the following comparison figure. In future, we will support JIT acceleration on GPUs).

.. figure:: https://github.com/PKU-NIP-Lab/NumpyBrain/blob/master/docs/images/speed.png
:alt: Speed of BrainPy
:figclass: align-center
:width: 400px
:width: 250px


Installation
Expand Down Expand Up @@ -80,108 +79,112 @@ Packages recommended to install:

- Numba >= 0.50.0
- TensorFlow >= 2.4


Define a Hodgkin–Huxley neuron model
====================================

.. code-block:: python
import brainpy.numpy as np
import brainpy as bp
def HH(noise=0., E_Na=50., g_Na=120., E_K=-77., g_K=36.,
E_Leak=-54.387, g_Leak=0.03, C=1.0, Vth=20.):
ST = bp.types.NeuState(
{'V': -65., 'm': 0., 'h': 0., 'n': 0., 'sp': 0., 'inp': 0.},
help='Hodgkin–Huxley neuron state.\n'
'"V" denotes membrane potential.\n'
'"n" denotes potassium channel activation probability.\n'
'"m" denotes sodium channel activation probability.\n'
'"h" denotes sodium channel inactivation probability.\n'
'"sp" denotes spiking state.\n'
'"inp" denotes synaptic input.\n'
)
@bp.integrate
def int_m(m, t, V):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
return alpha * (1 - m) - beta * m
@bp.integrate
def int_h(h, t, V):
alpha = 0.07 * np.exp(-(V + 65) / 20.)
beta = 1 / (1 + np.exp(-(V + 35) / 10))
return alpha * (1 - h) - beta * h
@bp.integrate
def int_n(n, t, V):
alpha = 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
beta = 0.125 * np.exp(-(V + 65) / 80)
return alpha * (1 - n) - beta * n
@bp.integrate
def int_V(V, t, m, h, n, Isyn):
INa = g_Na * m ** 3 * h * (V - E_Na)
IK = g_K * n ** 4 * (V - E_K)
IL = g_Leak * (V - E_Leak)
dvdt = (- INa - IK - IL + Isyn) / C
return (dvdt, noise / C)
def update(ST, _t_):
m = np.clip(int_m(ST['m'], _t_, ST['V']), 0., 1.)
h = np.clip(int_h(ST['h'], _t_, ST['V']), 0., 1.)
n = np.clip(int_n(ST['n'], _t_, ST['V']), 0., 1.)
V = int_V(ST['V'], _t_, m, h, n, ST['inp'])
sp = np.logical_and(ST['V'] < Vth, V >= Vth)
ST['sp'] = sp
ST['V'] = V
ST['m'] = m
ST['h'] = h
ST['n'] = n
ST['inp'] = 0.
return bp.NeuType(name='HH',
requires=dict(ST=ST),
steps=update)
Define an AMPA synapse model
============================

.. code-block:: python
def AMPA(g_max=0.10, E=0., tau_decay=2.0):
requires = dict(
ST=bp.types.SynState(
['s'], help='AMPA synapse state.'),
pre=bp.types.NeuState(
['sp'], help='Pre-synaptic state must have "sp" item.'),
post=bp.types.NeuState(
['V', 'inp'], help='Post-synaptic neuron must have "V" and "inp" items.')
)
@bp.integrate(method='euler')
def ints(s, t):
return - s / tau_decay
def update(ST, _t_, pre):
s = ints(ST['s'], _t_)
s += pre['sp']
ST['s'] = s
@bp.delayed
def output(ST, post):
post_val = - g_max * ST['s'] * (post['V'] - E)
post['inp'] += post_val
return bp.SynType(name='AMPA',
requires=requires,
steps=(update, output),
vector_based=False)
- PyTorch >= 1.7


Neurodynamics simulation
========================


.. raw:: html

<table border="0">
<tr>
<td border="0" width="30%">
<a href="examples/neurons/HH_model.py">
<img src="docs/images/HH_neuron.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/neurons/HH_model.py">HH Neuron Model</a></h3>
<p>The Hodgkin–Huxley model, or conductance-based model,
is a mathematical model that describes how action potentials
in neurons are initiated and propagated. It is a set of nonlinear
differential equations that approximates the electrical characteristics
of excitable cells such as neurons and cardiac myocytes.</p>
</td>
</tr>
<tr>
<td border="0" width="30%">
<a href="examples/synapses/AMPA_vector.py">
<img src="docs/images/AMPA_model.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/synapses/AMPA_vector.py">AMPA Synapse Model</a></h3>
<p>AMPA synapse model.</p>
</td>
</tr>
<tr>
<td border="0" width="30%">
<a href="examples/networks/gamma_oscillation.py">
<img src="docs/images/gamma_oscillation.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/networks/gamma_oscillation.py">Gamma Oscillation Model</a></h3>
<p>Implementation of the paper: <i> Wang, Xiao-Jing, and György Buzsáki. “Gamma oscillation by
synaptic inhibition in a hippocampal interneuronal network
model.” Journal of neuroscience 16.20 (1996): 6402-6413. </i>
</p>
</td>
</tr>
<tr>
<td border="0" width="30%">
<a href="examples/networks/EI_balance_network.py">
<img src="docs/images/EI_balance_net.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/networks/EI_balance_network.py">E/I Balance Network</a></h3>
</td>
</tr>
<tr>
<td border="0" width="30%">
<a href="examples/networks/CANN_1D.py">
<img src="docs/images/CANN1d.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/networks/CANN_1D.py">Continuous-attractor Network</a></h3>
<p>Implementation of the paper: <i> Si Wu, Kosuke Hamaguchi, and Shun-ichi Amari. "Dynamics and
computation of continuous attractors." Neural
computation 20.4 (2008): 994-1025. </i>
</p>
</td>
</tr>
</table>


Network examples please see `networks <https://github.com/PKU-NIP-Lab/BrainPy/tree/master/examples/networks>`_.

More neuron examples please see `neurons <https://github.com/PKU-NIP-Lab/BrainPy/tree/master/examples/neurons>`_.

More synapse examples please see `synapses <https://github.com/PKU-NIP-Lab/BrainPy/tree/master/examples/synapses>`_.


Neurodynamics analysis
======================


.. raw:: html

<table border="0">
<tr>
<td border="0" width="30%">
<a href="examples/dynamics_analysis/phase_portrait.py">
<img src="docs/images/phase_plane_analysis.png">
</a>
</td>
<td border="0" valign="top">
<h3><a href="examples/dynamics_analysis/phase_portrait.py">Phase Plane Analysis</a></h3>
<p>Phase plane analysis of a two-dimensional system model.</p>
</td>
</tr>
</table>


More examples please see
`dynamics analysis <https://github.com/PKU-NIP-Lab/BrainPy/tree/master/examples/dynamics_analysis>`_.


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