ArticleeLife2023
BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Hippocampal theta sweeps indicate goal direction during navigation.Nature neuroscience · 2026Article
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- Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function.bioRxiv : the preprint server for biology · 2026Article
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- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Plastic Arbor: A modern simulation framework for synaptic plasticity-From single synapses to networks of morphological neurons.PLoS computational biology · 2026Article
- Model-agnostic linear-memory online learning in spiking neural networks.Nature communications · 2026Article
- Article
- Modeling macroscopic brain dynamics with brain-inspired computing architecture.Nature communications · 2025Article
- Nonlinear feedback modulation contributes to the optimization of flexible decision-making.eLife · 2025Article
- Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration.Neuroinformatics · 2025Article
- Integrating physical units into high-performance AI-driven scientific computing.Nature communications · 2025Article
- A systems model of alternating theta sweeps via firing rate adaptation.Current biology : CB · 2025Article
- Biophysical modeling and experimental analysis of the dynamics of C. elegans body-wall muscle cells.PLoS computational biology · 2025Article
- High-order sensory processing nanocircuit based on coupled VONature communications · 2024Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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, 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.