Why to use BrainPy

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

• JIT compilation for functions and 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 has the speed benefit on JIT compilation.
• And more ......

BrainPy is designed to effectively satisfy your basic requirements:

• Easy to learn and use: BrainPy is only based on Python language and has little dependency requirements.
• Flexible and transparent: BrainPy endows the users with the fully data/logic flow control. Users can code any logic they want with BrainPy.
• Extensible: BrainPy allow users to extend new functionality just based on Python coding. For example, we extend the same code with the ability to do numerical analysis (whatever low- or high-dimensional system).
• Efficient: All codes in BrainPy can be just-in-time compiled (based on JAX and Numba) 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

The installation details please see documentation: Quickstart/Installation

Method 1: install BrainPy by using pip:

To install the stable release of BrainPy, please use

> pip install -U brain-py

Method 2: install BrainPy from source:

> pip install git+https://github.com/PKU-NIP-Lab/BrainPy
>
> # or
>
> git clone https://github.com/PKU-NIP-Lab/BrainPy
> cd BrainPy
> python setup.py install

Other dependencies: you want to get the full supports by BrainPy, please install the following packages:

• JAX >= 0.2.10, needed for "jax" backend and many other supports (how to install jax?)
• Numba >= 0.52, needed for JIT compilation on "numpy" backend (how to install numba?)
• SymPy >= 1.4, needed for dynamics "analysis" module and Exponential Euler method (how to install sympy?)

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/en/latest/
• Canonical brain models: https://brainmodels.readthedocs.io/en/latest/

Step 3: inspirational examples

Here list several examples of BrainPy. 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 Learning

• 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-dimension dynamics analysis

• 1D system bifurcation
• Codimension 1 bifurcation analysis of FitzHugh Nagumo model
• Codimension 2 bifurcation analysis of FitzHugh Nagumo model
• [Decision Making Model] (Wong & Wang, 2006) Decision making rate model

High-dimension dynamics analysis

• [Fixed point finder]