Skip to main content

BrainPy-Models: An example package accompany with BrainPy.

Project description


LICENSE Documentation Conda

Note: We welcome your contributions for model implementations.

BrainPy-Models is a repository accompany with BrainPy, which is a framework for spiking neural network simulation. With BrainPy, we implements the most canonical and effective neuron models and synapse models, and show them in BrainPy-Models.

Here, users can directly import our models into your network, and also can learn examples of how to use BrainPy from Documentations.

We provide the following models:

Neuron models Synapse models Learning rules Networks
Leaky integrate-and-fire model Alpha Synapse STDP Continuous attractor network
Hodgkin-Huxley model AMPA / NMDA BCM rule E/I balance network
Izhikevich model GABA_A / GABA_B Oja's rule gamma oscillations
Morris--Lecar model Exponential Decay Synapse
Generalized integrate-and-fire Difference of Two Exponentials
Exponential integrate-and-fire Short-term plasticity
Quadratic integrate-and-fire Gap junction
adaptive Exponential IF Voltage jump
adaptive Quadratic IF
Hindmarsh--Rose model
Wilson-Cowan model


Install from source code:

python install

Install BrainPy-Models using conda:

conda install bpmodels -c brainpy 

Install BrainPy-Models using pip:

pip install bpmodels

The following packages need to be installed to use BrainPy-Models:

  • Python >= 3.7
  • Matplotlib >= 2.0
  • brainpy-simulator >= 0.3.0

Quick Start

The use of bpmodels is very convenient, let's take an example of the implementation of the E-I balanced network.

We start by importing the brainpy and bpmodels packages and set profile.

import brainpy as bp
import bpmodels
import numpy as np
import matplotlib.pyplot as plt

# set profile
bp.profile.set(jit=True, device='cpu',

The E-I balanced network is based on leaky Integrate-and-Fire (LIF) neurons connecting with single exponential decay synapses. As showed in the table above, bpmodels provides pre-defined LIF neuron model and exponential synapse model, so we can use bpmodels.neurons.get_LIF and bpmodels.synapses.get_exponential to get the pre-defined models.

V_rest = -52.
V_reset = -60.
V_th = -50.

neu = bpmodels.neurons.get_LIF(V_rest=V_rest, V_reset = V_reset, V_th=V_th, noise=0., mode='scalar')

syn = bpmodels.synapses.get_exponential(tau_decay = 2., mode='scalar')
# build network
num_exc = 500
num_inh = 500
prob = 0.1

JE = 1 / np.sqrt(prob * num_exc)
JI = 1 / np.sqrt(prob * num_inh)

group = bp.NeuGroup(neu, geometry=num_exc + num_inh, monitors=['spike'])

group.ST['V'] = np.random.random(num_exc + num_inh) * (V_th - V_rest) + V_rest

exc_conn = bp.SynConn(syn,
exc_conn.ST['w'] = JE

inh_conn = bp.SynConn(syn,
exc_conn.ST['w'] = -JI

net = bp.Network(group, exc_conn, inh_conn), inputs=(group, 'ST.input', 3.))

# visualization
fig, gs = bp.visualize.get_figure(4, 1, 2, 10)

fig.add_subplot(gs[:3, 0])
bp.visualize.raster_plot(net.ts, group.mon.spike, xlim=(50, 450))

fig.add_subplot(gs[3, 0])
rates = bp.measure.firing_rate(group.mon.spike, 5.)
plt.plot(net.ts, rates)
plt.xlim(50, 450)

Then you would expect to see the following output:


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for bpmodels, version 0.2.3
Filename, size File type Python version Upload date Hashes
Filename, size bpmodels-0.2.3.tar.gz (43.2 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page