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
-
(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]
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Release files for brain-py 1.1.5
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