• Why to use BrainPy
• Installation
• Neurodynamics simulation
• Neurodynamics analysis

Note: BrainPy is a project under development. More features are coming soon. Contributions are welcome.

Why to use BrainPy

BrainPy is an integrative framework for computational neuroscience and brain-inspired computation. Three core functions are provided in BrainPy:

• General numerical solvers for ODEs and SDEs (future will support DDEs and FDEs).
• Neurodynamics simulation tools for brain objects, such like neurons, synapses and networks (future will support soma and dendrites).
• Neurodynamics analysis tools for differential equations, including phase plane analysis and bifurcation analysis (future will support continuation analysis and sensitive analysis).

Moreover, BrainPy can effectively satisfy your basic requirements: 1. Easy to learn and use, because it is only based on Python language and has little dependency requirements; 2. Highly flexible and transparent, because it endows the users with the fully data/logic flow control; 3. Simulation can be guided with the analysis, because the same code in BrainPy can not only be used for simulation, but also for dynamics analysis; 4. Efficient running speed, because BrainPy is compatitable with the latest JIT compilers (or any other computing backend you prefer).

Installation

Install BrainPy using pip:

> pip install brainpy-simulator

Install BrainPy 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:

• NumPy >= 1.13
• Matplotlib >= 3.0

Neurodynamics simulation

	HH Neuron Model 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.
	AMPA Synapse Model AMPA synapse model.
	Gamma Oscillation Model 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.
	E/I Balance Network Implementation 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.
	Continuous-attractor Network 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.

More neuron examples please see BrainPy-Models/neurons;

More synapse examples please see BrainPy-Models/synapses;

More network examples please see BrainPy-Models/from_papers.

Neurodynamics analysis

	Phase Plane Analysis Phase plane analysis of the INa,p+-IK model, where "input" is 50., and "Vn_half" is -45..
	Codimension 1 Bifurcation Analysis (1) Codimension 1 bifurcation analysis of the INa,p+-IK model, in which "input" is varied in [0., 50.].
	Codimension 2 Bifurcation Analysis (1) 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].
	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 BrainPy-Models/dynamics_analysis.