brain-py 1.1.0b0

BrainPy: A flexible and extensible framework for brain modeling


[](https://github.com/PKU-NIP-Lab/BrainPy) [](https://brainpy.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/brainpy-simulator) [](https://travis-ci.com/PKU-NIP-Lab/BrainPy)

# 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](https://github.com/google/jax) and [Numba](https://github.com/numba/)). Core functions provided in BrainPy includes

• JIT compilation for class objects.

• Numerical solvers for ODEs, SDEs, DDEs, FDEs, 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, continuation analysis and sensitive 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 numerical integration with the ability to do numerical analysis. In such a way, the same code in BrainPy can not only be used for simulation, but also for dynamics analysis.

• Efficient running speed: All codes in BrainPy can be just-in-time compiled (based on [JAX](https://github.com/google/jax) and [Numba](https://github.com/numba/)) to run on CPU or GPU 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 >= 3.3

The installation details please see documentation: [Quickstart/Installation](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html)

Method 1: install BrainPy by using pip:

`bash > pip install -U brain-py `

Method 2: install BrainPy from source:

`bash > pip install git+https://github.com/PKU-NIP-Lab/BrainPy > > # or > pip install git+https://git.openi.org.cn/OpenI/BrainPy > > # or > pip install -e git://github.com/PKU-NIP-Lab/BrainPy.git@V1.0.0 `

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 “dnn” module

• Numba >= 0.52, needed for JIT compilation on “numpy” backend

• SymPy >= 1.4, needed for dynamics “analysis” module and Exponential Euler method

## Step 2: useful links

• Documentation: https://brainpy.readthedocs.io/

• Source code: https://github.com/PKU-NIP-Lab/BrainPy or https://git.openi.org.cn/OpenI/BrainPy

• Bug reports: https://github.com/PKU-NIP-Lab/BrainPy/issues or Email to [e-mail]

• Examples from papers: https://brainmodels.readthedocs.io/en/latest/from_papers.html

## Step 3: comprehensive examples

Here list several examples of BrainPy. More detailed examples and tutorials please see [BrainModels](https://brainmodels.readthedocs.io).

### Neuron models

• [Hodgkin–Huxley neuron model](https://github.com/PKU-NIP-Lab/BrainModels/blob/main/brainmodels/tensor_backend/neurons/HodgkinHuxley_model.py)

### Synapse models

• [AMPA synapse model](https://github.com/PKU-NIP-Lab/BrainModels/blob/main/brainmodels/tensor_backend/synapses/AMPA_synapse.py)

### Network models

• [Gamma oscillation network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Wang_1996_gamma_oscillation.html)

• [E/I balanced network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Vreeswijk_1996_EI_net.html)

• [Continuous attractor network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Wu_2008_CANN.html)

### Low-dimension dynamics analysis

• [Phase plane analysis of the I_Na,p-I_K model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/NaK_model_analysis.html)

• [Codimension 1 bifurcation analysis of FitzHugh Nagumo model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/FitzHugh_Nagumo_analysis.html)

• [Codimension 2 bifurcation analysis of FitzHugh Nagumo model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/FitzHugh_Nagumo_analysis.html#Codimension-2-bifurcation-analysis)

### Learning through back-propagation