BrainPy documentation

BrainPy is a highly flexible and extensible framework targeting on the high-performance Brain Dynamics Programming (BDP). Among its key ingredients, BrainPy supports:

• JIT compilation and automatic differentiation for class objects.

• Numerical methods for ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), fractional differential equations (FDEs), etc.

• Dynamics building with the modular and composable programming interface.

• Dynamics simulation for various brain objects with parallel supports.

• Dynamics training with various machine learning algorithms, like FORCE learning, ridge regression, back-propagation, etc.

• Dynamics analysis for low- and high-dimensional systems, including phase plane analysis, bifurcation analysis, linearization analysis, and fixed/slow point finding.

• And more others ……

Comprehensive examples of BrainPy please see:

• BrainPyExamples: https://brainpy-examples.readthedocs.io/

The code of BrainPy is open-sourced at GitHub:

• BrainPy: https://github.com/PKU-NIP-Lab/BrainPy

Quickstart

• Installation
• Simulating a Brain Dynamics Model
• Training a Brain Dynamics Model
• Analyzing a Brain Dynamics Model

BDP Tutorials

• Math Basics
  • brainpy.math Overview
  • Arrays and Variables
  • Just-In-Time Compilation
  • Control Flows
• Model Building
  • Dynamical System Specification
  • Building Neuron Models
  • Building Synapse Models
  • Building Network Models
  • Building General Dynamical Systems
• Model Simulation
  • Simulation DSRunner
  • Parallel Simulation
• Model Training
  • Building Training Models
  • Training with Offline Algorithms
  • Training with Online Algorithms
  • Training with Back-propagation Algorithms
  • Introduction to Echo State Network
• Model Analysis
  • Low-dimensional Analyzers
  • High-dimensional Analyzers
  • Analysis of a Decision-making Model

Toolboxes

• Numerical Solvers for Ordinary Differential Equations
• Numerical Solvers for Stochastic Differential Equations
• Numerical Solvers for Fractional Differential Equations
• Numerical Solvers for Delay Differential Equations
• Joint Differential Equations
• Synaptic Connections
• Synaptic Weights
• Gradient Descent Optimizers
• Saving and Loading

Advanced Tutorials

• More Details for Variables
• Fundamental Base and Collector Objects
• JIT Compilation for Class Objects
• Automatic Differentiation for Class Variables
• Low-level Operator Customization
• Interoperation with other JAX frameworks

API Documentation

• brainpy.base module
• brainpy.math module
• brainpy.dyn module
• brainpy.train module
• brainpy.analysis module
• brainpy.integrators module
• brainpy.datasets module
• brainpy.algorithms module
• brainpy.inputs module
• brainpy.connect module
• brainpy.initialize module
• brainpy.losses module
• brainpy.optimizers module
• brainpy.measure module
• brainpy.running module
• brainpy.tools module
• Release notes (brainpy)
• Release notes (brainpylib)

Indices and tables

• Index

• Module Index

• Search Page