BrainPy
BrainPy is a lightweight framework based on the latest JustInTime (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.
Installation
Install BrainPy
using conda
:
> conda install brainpy c brainpy
Install BrainPy
using pip
:
> pip install git+https://github.com/PKUNIPLab/BrainPy
> # or
> pip install git+https://git.openi.org.cn/OpenI/BrainPy
> # or
> pip install e git://github.com/PKUNIPLab/[email protected]
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
Neurodynamics simulation
HH Neuron ModelThe Hodgkin–Huxley model, or conductancebased 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. 

AMPA Synapse ModelAMPA synapse model. 

Gamma Oscillation ModelImplementation of the paper: Wang, XiaoJing, and György Buzsáki. “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.” Journal of neuroscience 16.20 (1996): 64026413. 

E/I Balance Network 

Continuousattractor NetworkImplementation of the paper: Si Wu, Kosuke Hamaguchi, and Shunichi Amari. "Dynamics and computation of continuous attractors." Neural computation 20.4 (2008): 9941025. 
More neuron examples please see bpmodels/neurons;
More synapse examples please see bpmodels/synapses;
More network examples please see brainpyexamples/networks and brainpyexamples/from_papers.
Neurodynamics analysis
Phase Plane AnalysisPhase plane analysis of the I_{Na,p+}I_{K} model, where "input" is 50., and "Vn_half" is 45.. 

Codimension 1 Bifurcation Analysis (1)Codimension 1 bifurcation analysis of the I_{Na,p+}I_{K} model, in which "input" is varied in [0., 50.]. 

Codimension 2 Bifurcation Analysis (1)Codimension 2 bifurcation analysis of a twovariable neuron model: the I_{Na,p+}I_{K} model, in which "input" is varied in [0., 50.], and "Vn_half" is varied in [50, 40]. 

Codimension 1 Bifurcation Analysis (2)Codimension 1 bifurcation analysis of FitzHugh Nagumo model, in which "a" is equal to 0.7, and "Iext" is varied in [0., 1.]. 

Codimension 2 Bifurcation Analysis (2)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.]. 
More examples please see brainpyexamples/dynamics_analysis.