1. Computational and Systems Biology
  2. Neuroscience

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

  1. Chaoming Wang
  2. Tianqiu Zhang
  3. Xiaoyu Chen
  4. Sichao He
  5. Shangyang Li
  6. Si Wu  Is a corresponding author
  1. Peking University, China
  2. Beijing Jiaotong University, China
https://doi.org/10.7554/eLife.86365
Share this article
Cite this article
  1. Chaoming Wang
  2. Tianqiu Zhang
  3. Xiaoyu Chen
  4. Sichao He
  5. Shangyang Li
  6. 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
  2. Download BibTeX
  3. Download .RIS

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/.

The following previously published data sets were used

Article and author information

Author details

  1. Chaoming Wang

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  2. Tianqiu Zhang

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  3. Xiaoyu Chen

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  4. Sichao He

    Beijing Jiaotong University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  5. Shangyang Li

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  6. Si Wu

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    For correspondence
    siwu@pku.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9650-6935

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.

Reviewing Editor

  1. Marcel Stimberg, Institut de la Vision, France

Version history

  1. Preprint posted: October 28, 2022 (view preprint)
  2. Received: January 21, 2023
  3. Accepted: December 20, 2023
  4. Accepted Manuscript published: December 22, 2023 (version 1)
  5. Version of Record published: January 18, 2024 (version 2)

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.

Metrics

  • 1,008
    views
  • 221
    downloads
  • 3
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Chaoming Wang
  2. Tianqiu Zhang
  3. Xiaoyu Chen
  4. Sichao He
  5. Shangyang Li
  6. 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

Share this article

https://doi.org/10.7554/eLife.86365

Categories and tags

Further reading

    1. Computational and Systems Biology

    Improved clinical data imputation via classical and quantum determinantal point processes

    Skander Kazdaghli, Iordanis Kerenidis ... Philip Teare
    Research Article

    Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.

    1. Computational and Systems Biology

    Heritable epigenetic changes are constrained by the dynamics of regulatory architectures

    Antony M Jose
    Research Article

    Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here, I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors, and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode Caenorhabditis elegans. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.

    1. Computational and Systems Biology
    2. Ecology

    Collaborative hunting in artificial agents with deep reinforcement learning

    Kazushi Tsutsui, Ryoya Tanaka ... Keisuke Fujii
    Research Article

    Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.