BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming
- Peking University, China
- Beijing Jiaotong University, China
Abstract
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 (CPU), Graphics Processing Unit (GPU), and Tensor Processing Unit (TPU), 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
Data availability
BrainPy is distributed via the pypi package index (https://pypi.org/project/brainpy/) and is publicly released on GitHub (https://github.com/brainpy/BrainPy/) under the license of GNU General Public License v3.0. Its documentation is hosted on the free documentation hosting platform Read the Docs (https://brainpy.readthedocs.io/). Rich examples and illustrations of BrainPy are publicly available at the website of https://brainpy-examples.readthedocs.io/. The source codes of these examples are available at https://github.com/brainpy/examples/. All the codes to reproduce the results in the paper can be found at the following GitHub repository: https://github.com/brainpy/brainpy-paper-reproducibility/.
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A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortexhttps://core-nets.org, A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb Cortex. 2014 Jan;24(1):17-36. Epub 2012 Sep 25.
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Author details
Funding
Peking University (2021ZD0200204)
- Si Wu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Wang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming
eLife 12:e86365.
https://doi.org/10.7554/eLife.86365
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