BrainPy is a flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top of JAX, Taichi, Numba, and others). It provides an integrative ecosystem for brain dynamics programming, including brain dynamics building, simulation, training, analysis, etc.
- Website (documentation and APIs): https://brainpy.readthedocs.io/en/latest
- Source: https://github.com/brainpy/BrainPy
- Bug reports: https://github.com/brainpy/BrainPy/issues
- Source on OpenI: https://git.openi.org.cn/OpenI/BrainPy
BrainPy is based on Python (>=3.8) and can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms.
For detailed installation instructions, please refer to the documentation: Quickstart/Installation
We provide a docker image for BrainPy. You can use the following command to pull the image:
$ docker pull brainpy/brainpy:latestThen, you can run the image with the following command:
$ docker run -it --platform linux/amd64 brainpy/brainpy:latestWe provide a Binder environment for BrainPy. You can use the following button to launch the environment:
- BrainPy: The solution for the general-purpose brain dynamics programming.
- brainpy-examples: Comprehensive examples of BrainPy computation.
- brainpy-datasets: Neuromorphic and Cognitive Datasets for Brain Dynamics Modeling.
- 《神经计算建模实战》 (Neural Modeling in Action)
- 第一届神经计算建模与编程培训班 (First Training Course on Neural Modeling and Programming)
- 第二届神经计算建模与编程培训班 (Second Training Course on Neural Modeling and Programming)
BrainPy is developed by a team in Neural Information Processing Lab at Peking University, China. Our team is committed to the long-term maintenance and development of the project.
If you are using brainpy, please consider citing the corresponding papers.
We highlight the key features and functionalities that are currently under active development.
We also welcome your contributions (see Contributing to BrainPy).
- model and data parallelization on multiple devices for dense connection models
- model parallelization on multiple devices for sparse spiking network models
- data parallelization on multiple devices for sparse spiking network models
- pipeline parallelization on multiple devices for sparse spiking network models
- multi-compartment modeling
- measurements, analysis, and visualization methods for large-scale spiking data
- Online learning methods for large-scale spiking network models
- Classical plasticity rules for large-scale spiking network models