brain-py 2.0.1

BrainPy: Brain Dynamics Programming in Python

:clap::clap: CHEERS: A new version of BrainPy (>=2.0.0, long term support) has been released! :clap::clap:

Why use BrainPy

BrainPy is an integrative framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top of JAX). Core functions provided in BrainPy includes

• JIT compilation for class objects.
• Numerical solvers for ODEs, SDEs, and others.
• Dynamics simulation tools for various brain objects, like neurons, synapses, networks, soma, dendrites, channels, and even more.
• Dynamics analysis tools for differential equations, including phase plane analysis and bifurcation analysis, and linearization analysis.
• Seamless integration with deep learning models.
• And more ......

BrainPy is designed to effectively satisfy your basic requirements:

• Pythonic: BrainPy is based on Python language and has a Pythonic coding style.
• Flexible and transparent: BrainPy endows the users with full data/logic flow control. Users can code any logic they want with BrainPy.
• Extensible: BrainPy allows users to extend new functionality just based on Python code. Almost every part of the BrainPy system can be extended to be customized.
• Efficient: All codes in BrainPy can be just-in-time compiled (based on JAX) to run on CPU, GPU, or TPU devices, thus guaranteeing its running efficiency.

How to use BrainPy

Step 1: installation

BrainPy is based on Python (>=3.6), and the following packages are required to be installed to use BrainPy: numpy >= 1.15, matplotlib >= 3.4, and jax >= 0.2.10 (how to install jax?)

BrainPy can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Use the following instructions to install brainpy:

pip install brain-py -U

For the full installation details please see documentation: Quickstart/Installation

Step 2: useful links

• Documentation: https://brainpy.readthedocs.io/
• Bug reports: https://github.com/PKU-NIP-Lab/BrainPy/issues
• Examples from papers: https://brainpy-examples.readthedocs.io/
• Canonical brain models: https://brainmodels.readthedocs.io/

Step 3: inspirational examples

Here we list several examples of BrainPy. For more detailed examples and tutorials please see BrainModels or BrainPy-Examples.

Neuron models

• Leaky integrate-and-fire neuron model, source code
• Exponential integrate-and-fire neuron model, source code
• Quadratic integrate-and-fire neuron model, source code
• Adaptive Quadratic integrate-and-fire model, source code
• Adaptive Exponential integrate-and-fire model, source code
• Generalized integrate-and-fire model, source code
• Hodgkin–Huxley neuron model, source code
• Izhikevich neuron model, source code
• Morris-Lecar neuron model, source code
• Hindmarsh-Rose bursting neuron model, source code

See brainmodels.neurons to find more.

Synapse models

• Voltage jump synapse model, source code
• Exponential synapse model, source code
• Alpha synapse model, source code
• Dual exponential synapse model, source code
• AMPA synapse model, source code
• GABAA synapse model, source code
• NMDA synapse model, source code
• Short-term plasticity model, source code

See brainmodels.synapses to find more.

Network models

• [CANN] (Si Wu, 2008) Continuous-attractor Neural Network
• (Vreeswijk & Sompolinsky, 1996) E/I balanced network
• (Sherman & Rinzel, 1992) Gap junction leads to anti-synchronization
• (Wang & Buzsáki, 1996) Gamma Oscillation
• (Brunel & Hakim, 1999) Fast Global Oscillation
• (Diesmann, et, al., 1999) Synfire Chains
• [Working Memory] (Mi, et. al., 2017) STP for Working Memory Capacity
• [Working Memory] (Bouchacourt & Buschman, 2019) Flexible Working Memory Model
• [Decision Making] (Wang, 2002) Decision making spiking model

Dynamics training

• Train Integrator RNN with BP

• (Sussillo & Abbott, 2009) FORCE Learning

• (Laje & Buonomano, 2013) Robust Timing in RNN

• (Song, et al., 2016): Training excitatory-inhibitory recurrent network

• [Working Memory] (Masse, et al., 2019): RNN with STP for Working Memory

Low-dimensional dynamics analysis

• [1D] Simple systems
• [2D] NaK model analysis
• [3D] Hindmarsh Rose Model
• [Decision Making Model] [2D] Decision making rate model

High-dimensional dynamics analysis

• (Yang, 2020): Dynamical system analysis for RNN
• Continuous-attractor Neural Network
• Gap junction-coupled FitzHugh-Nagumo Model

Citing BrainPy

A nascent version of BrainPy was described in a paper that appeared at ICONIP 2021. We're currently working on covering BrainPy's ideas and capabilities in a more comprehensive and up-to-date paper.

If you use BrainPy for your published research, you can cite BrainPy with:

Wang, C., Jiang, Y., Liu, X., Lin, X., Zou, X., Ji, Z., & Wu, S. (2021, December). A Just-In-Time Compilation Approach for Neural Dynamics Simulation. In International Conference on Neural Information Processing (pp. 15-26). Springer, Cham.

or,

@inproceedings{wang2021just,
  title={A Just-In-Time Compilation Approach for Neural Dynamics Simulation},
  author={Wang, Chaoming and Jiang, Yingqian and Liu, Xinyu and Lin, Xiaohan and Zou, Xiaolong and Ji, Zilong and Wu, Si},
  booktitle={International Conference on Neural Information Processing},
  pages={15--26},
  year={2021},
  organization={Springer}
}

BrainPy 1.x

If you are using brainpy==1.x, you can find documentation, examples, and models through the following links:

• Documentation: https://brainpy.readthedocs.io/en/brainpy-1.x/
• Examples from papers: https://brainpy-examples.readthedocs.io/en/brainpy-1.x/
• Canonical brain models: https://brainmodels.readthedocs.io/en/brainpy-1.x/

The changes from brainpy==1.x to brainpy==2.x can be inspected through API documentation: release notes.