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BrainPy
is an integrative framework for computational neuroscience and brain-inspired computation. Three core functions are provided in BrainPy:
Moreover, BrainPy
is designed to effectively satisfy your basic requirements:
BrainPy
is a backend-independent neural simulator. Users can define models with any backend they prefer. Intrinsically, BrainPy supports the array/tensor-oriented backends such like NumPy, PyTorch, and TensorFlow, it also supports the JIT compilers such as Numba on CPU or CUDA devices. Extending BrainPy to support other backend frameworks you prefer is very easy. The details please see documents coming soon.
Install BrainPy
by using pip
:
> pip install brainpy-simulator>=1.0.0rc1
Install BrainPy
by using conda
:
> conda install brainpy-simulator -c brainpy
Install BrainPy
from source:
> 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
BrainPy
is based on Python (>=3.7), and the following packages are required to be installed to use BrainPy
:
Here list several simple examples for neurodynamics simulation and analysis. Comprehensive examples and tutorials please see BrainModels.
HH Neuron ModelThe Hodgkin–Huxley neuron model. |
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AMPA Synapse ModelThe AMPA synapse model. |
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Gamma Oscillation ModelImplementation 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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E/I Balance NetworkImplementation of the paper: Van Vreeswijk, Carl, and Haim Sompolinsky. “Chaos in neuronal networks with balanced excitatory and inhibitory activity.” Science 274.5293 (1996): 1724-1726. |
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Continuous-attractor NetworkImplementation 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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Phase Plane AnalysisPhase plane analysis of the INa,p+-IK model, where "input" is 50., and "Vn_half" is -45.. |
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Codimension 1 Bifurcation AnalysisCodimension 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 AnalysisCodimension 2 bifurcation analysis of FitzHugh Nagumo model, in which "a" is varied in [0.5, 1.0], and "Iext" is varied in [0., 1.]. |
Brain Dynamics Programming in Python
https://brainpy.readthedocs.io/
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