BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming

Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simul...

Full description

Saved in:
Bibliographic Details
Published in:eLife 2023-12, Vol.12
Main Authors: Wang, Chaoming, Zhang, Tianqiu, Chen, Xiaoyu, He, Sichao, Li, Shangyang, Wu, Si
Format: Article
Language:English
Subjects:
brain modeling
brain simulation
brain simulator
Central processing units
Computational and Systems Biology
computational neuroscience
Distance learning
Fourier transforms
Infrastructure
Interfaces
just-in-time compilation
Linear algebra
Machine learning
Microprocessors
Neuroscience
Online tutorials
Ordinary differential equations
Simulation
spiking neural networks
Tools and Resources
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.86365