Codes for figures in BrainPy paper

These codes are used to reproduce figures in our BrainPy eLife publication.

Instructions

First, please follow the official installation link to install BrainPy package.

bash pip install brainpy pip install brainpylib pip install brainpy-datasets

Second, to reproduce the figures, please use the command of

bash python figure-xxx.py

Citation

If BrainPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following papers:

Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li, Si Wu (2023) BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming eLife 12:e86365 https://doi.org/10.7554/eLife.86365

bibtex @article {10.7554/eLife.86365, article_type = {journal}, title = {BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming}, author = {Wang, Chaoming and Zhang, Tianqiu and Chen, Xiaoyu and He, Sichao and Li, Shangyang and Wu, Si}, editor = {Stimberg, Marcel}, volume = 12, year = 2023, month = {dec}, pub_date = {2023-12-22}, pages = {e86365}, citation = {eLife 2023;12:e86365}, doi = {10.7554/eLife.86365}, url = {https://doi.org/10.7554/eLife.86365}, 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}, journal = {eLife}, issn = {2050-084X}, publisher = {eLife Sciences Publications, Ltd}, }