![Logo](https://raw.githubusercontent.com/xiangweizeng/BrainPy/master/docs/images/logo.png)
![travis](https://camo.githubusercontent.com/9a4d811191cde0d79f61607c2eb4512cf8c99044365e1579b34db3b62ff791ec/68747470733a2f2f7472617669732d63692e6f72672f504b552d4e49502d4c61622f427261696e50792e7376673f6272616e63683d6d6173746572)
Note: BrainPy is a project under development. More features are coming soon. Contributions are welcome.
BrainPy
is a lightweight framework based on the latest Just-In-Time (JIT) compilers (especially Numba). The goal of BrainPy
is to provide a unified simulation and analysis framework for neuronal dynamics with the feature of high flexibility and efficiency. BrainPy is flexible because it endows the users with the fully data/logic flow control. BrainPy is efficient because it supports JIT acceleration on CPUs and GPUs.
![Speed Comparison with Brian2](https://raw.githubusercontent.com/xiangweizeng/BrainPy/master/docs/images/speed.png)
![Scaling of BrainPy](https://raw.githubusercontent.com/xiangweizeng/BrainPy/master/docs/images/speed_scaling.png)
Install BrainPy
using conda
:
> conda install brainpy -c brainpy
Install BrainPy
using pip
:
> 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@V0.2.5
The following packages need to be installed to use BrainPy
:
- Python >= 3.7
- NumPy >= 1.13
- SymPy >= 1.2
- Numba >= 0.50.0
- Matplotlib >= 3.0
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The Hodgkin–Huxley model, or conductance-based model,
is a mathematical model that describes how action potentials
in neurons are initiated and propagated. It is a set of nonlinear
differential equations that approximates the electrical characteristics
of excitable cells such as neurons and cardiac myocytes.
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AMPA synapse model.
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Implementation of the paper: Wang, Xiao-Jing, and György Buzsáki. “Gamma oscillation by
synaptic inhibition in a hippocampal interneuronal network
model.” Journal of neuroscience 16.20 (1996): 6402-6413.
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Implementation of the paper: Si Wu, Kosuke Hamaguchi, and Shun-ichi Amari. "Dynamics and
computation of continuous attractors." Neural
computation 20.4 (2008): 994-1025.
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More neuron examples please see bpmodels/neurons;
More synapse examples please see bpmodels/synapses;
More network examples please see brainpy-examples/networks and brainpy-examples/from_papers.
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Phase plane analysis of the INa,p+-IK model, where
"input" is 50., and "Vn_half" is -45..
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Codimension 1 bifurcation analysis of the INa,p+-IK model,
in which "input" is varied in [0., 50.].
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Codimension 2 bifurcation analysis of a two-variable neuron model:
the INa,p+-IK model, in which "input" is varied
in [0., 50.], and "Vn_half" is varied in [-50, -40].
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Codimension 1 bifurcation analysis of FitzHugh Nagumo model, in which
"a" is equal to 0.7, and "Iext" is varied in [0., 1.].
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Codimension 2 bifurcation analysis of FitzHugh Nagumo model, in which "a"
is varied in [0.5, 1.0], and "Iext" is varied in [0., 1.].
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More examples please see brainpy-examples/dynamics_analysis.