brain-py 2.1.0

BrainPy: Brain Dynamics Programming in Python

BrainPy is a flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top of JAX). It provides an integrative ecosystem for brain dynamics programming, including brain dynamics simulation, training, analysis, etc.

• Website (documentation and APIs): https://brainpy.readthedocs.io/en/latest
• Source: https://github.com/PKU-NIP-Lab/BrainPy
• Bug reports: https://github.com/PKU-NIP-Lab/BrainPy/issues
• Source on OpenI: https://git.openi.org.cn/OpenI/BrainPy
• Canonical brain models: https://brainmodels.readthedocs.io/
• Examples from literature: https://brainpy-examples.readthedocs.io/

Install

BrainPy is based on Python (>=3.6) and can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version of BrainPy:

$ pip install brain-py -U

The following packages are required for BrainPy:

numpy >= 1.15 and jax >= 0.2.10 (how to install jax?)

For detailed installation instructions, please refer to the documentation: Quickstart/Installation

Examples

import brainpy as bp

1. E-I balance network

class EINet(bp.dyn.Network):
  def __init__(self):
    E = bp.dyn.LIF(3200, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)
    I = bp.dyn.LIF(800, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)
    E.V[:] = bp.math.random.randn(3200) * 2 - 60.
    I.V[:] = bp.math.random.randn(800) * 2 - 60.
        
    E2E = bp.dyn.ExpCOBA(E, E, bp.conn.FixedProb(prob=0.02), E=0., g_max=0.6, tau=5.)
    E2I = bp.dyn.ExpCOBA(E, I, bp.conn.FixedProb(prob=0.02), E=0., g_max=0.6, tau=5.)
    I2E = bp.dyn.ExpCOBA(I, E, bp.conn.FixedProb(prob=0.02), E=-80., g_max=6.7, tau=10.)
    I2I = bp.dyn.ExpCOBA(I, I, bp.conn.FixedProb(prob=0.02), E=-80., g_max=6.7, tau=10.)
        
    super(EINet, self).__init__(E2E, E2I, I2E, I2I, E=E, I=I)
    

net = EINet()
runner = bp.dyn.DSRunner(net)
runner(100.)

2. Echo state network

i = bp.nn.Input(3)
r = bp.nn.Reservoir(100)
o = bp.nn.LinearReadout(3)

net = i >> r >> o

# Ridge Regression
trainer = bp.nn.RidgeTrainer(net, beta=1e-5)

# FORCE Learning
trainer = bp.nn.FORCELearning(net, alpha=1.)

3. Next generation reservoir computing

i = bp.nn.Input(3)
r = bp.nn.NVAR(delay=2, order=2)
o = bp.nn.LinearReadout(3)

net = i >> r >> o

trainer = bp.nn.RidgeTrainer(net, beta=1e-5)

4. Recurrent neural network

i = bp.nn.Input(3)
l1 = bp.nn.VanillaRNN(100)
l2 = bp.nn.VanillaRNN(200)
o = bp.nn.Dense(10)

net = i >> l1 >> l2 >> o

trainer = bp.nn.BPTT(net, 
                     loss='cross_entropy_loss',
                     optimizer=bp.optim.Adam(0.01))

5. Analyzing a low-dimensional FitzHugh–Nagumo neuron model

bp.math.enable_x64()

model = bp.dyn.FHN(1)
analyzer = bp.analysis.PhasePlane2D(model,
                                    target_vars={'V': [-3, 3], 'w': [-3., 3.]},
                                    pars_update={'I_ext': 0.8}, 
                                    resolutions=0.01)
analyzer.plot_nullcline()
analyzer.plot_vector_field()
analyzer.plot_fixed_point()
analyzer.plot_trajectory({'V': [-2.8], 'w': [-1.8]}, duration=100.)
analyzer.show_figure()

For more functions and examples, please refer to the documentation and examples.

License

GNU General Public License v3.0