<p align="center">
<img alt="Header image of BrainPy - brain dynamics programming in Python." src="https://github.com/brainpy/BrainPy/blob/master/images/logo.png" width=80%>
</p>
<p align="center">
<a href="https://pypi.org/project/brainpy/"><img alt="Supported Python Version" src="https://img.shields.io/pypi/pyversions/brainpy"></a>
<a href="https://github.com/brainpy/BrainPy"><img alt="LICENSE" src="https://anaconda.org/brainpy/brainpy/badges/license.svg"></a>
<a href="https://brainpy.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation" src="https://readthedocs.org/projects/brainpy/badge/?version=latest"></a>
<a href="https://badge.fury.io/py/brainpy"><img alt="PyPI version" src="https://badge.fury.io/py/brainpy.svg"></a>
<a href="https://github.com/brainpy/BrainPy/actions/workflows/CI.yml"><img alt="Continuous Integration" src="https://github.com/brainpy/BrainPy/actions/workflows/CI.yml/badge.svg"></a>
<a href="https://github.com/brainpy/BrainPy/actions/workflows/CI-models.yml"><img alt="Continuous Integration with Models" src="https://github.com/brainpy/BrainPy/actions/workflows/CI-models.yml/badge.svg"></a>
</p>
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](https://github.com/google/jax), [Taichi](https://github.com/taichi-dev/taichi), [Numba](https://github.com/numba/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
## Installation
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](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html)
### Using BrainPy with docker
We provide a docker image for BrainPy. You can use the following command to pull the image:
```bash
$ docker pull brainpy/brainpy:latest
```
Then, you can run the image with the following command:
```bash
$ docker run -it --platform linux/amd64 brainpy/brainpy:latest
```
### Using BrainPy with Binder
We provide a Binder environment for BrainPy. You can use the following button to launch the environment:
[](https://mybinder.org/v2/gh/brainpy/BrainPy-binder/main)
## Ecosystem
- **[BrainPy](https://github.com/brainpy/BrainPy)**: The solution for the general-purpose brain dynamics programming.
- **[brainpy-examples](https://github.com/brainpy/examples)**: Comprehensive examples of BrainPy computation.
- **[brainpy-datasets](https://github.com/brainpy/datasets)**: Neuromorphic and Cognitive Datasets for Brain Dynamics Modeling.
- [《神经计算建模实战》 (Neural Modeling in Action)](https://github.com/c-xy17/NeuralModeling)
- [第一届神经计算建模与编程培训班 (First Training Course on Neural Modeling and Programming)](https://github.com/brainpy/1st-neural-modeling-and-programming-course)
- [第二届神经计算建模与编程培训班 (Second Training Course on Neural Modeling and Programming)](https://github.com/brainpy/2nd-neural-modeling-and-programming-course)
## Citing
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](https://brainpy.readthedocs.io/en/latest/tutorial_FAQs/citing_and_publication.html).
## Ongoing development plans
We highlight the key features and functionalities that are currently under active development.
We also welcome your contributions
(see [Contributing to BrainPy](https://brainpy.readthedocs.io/en/latest/tutorial_advanced/contributing.html)).
- [x] 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
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It provides an integrative ecosystem for brain dynamics programming, including brain dynamics **building**, **simulation**, **training**, **analysis**, etc. \r\n\r\n- **Website (documentation and APIs)**: https://brainpy.readthedocs.io/en/latest\r\n- **Source**: https://github.com/brainpy/BrainPy\r\n- **Bug reports**: https://github.com/brainpy/BrainPy/issues\r\n- **Source on OpenI**: https://git.openi.org.cn/OpenI/BrainPy\r\n\r\n\r\n\r\n## Installation\r\n\r\nBrainPy 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. \r\n\r\nFor detailed installation instructions, please refer to the documentation: [Quickstart/Installation](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html)\r\n\r\n\r\n### Using BrainPy with docker\r\n\r\nWe provide a docker image for BrainPy. You can use the following command to pull the image:\r\n```bash\r\n$ docker pull brainpy/brainpy:latest\r\n```\r\n\r\nThen, you can run the image with the following command:\r\n```bash\r\n$ docker run -it --platform linux/amd64 brainpy/brainpy:latest\r\n```\r\n\r\n\r\n### Using BrainPy with Binder\r\n\r\nWe provide a Binder environment for BrainPy. You can use the following button to launch the environment:\r\n\r\n[](https://mybinder.org/v2/gh/brainpy/BrainPy-binder/main)\r\n\r\n\r\n## Ecosystem\r\n\r\n- **[BrainPy](https://github.com/brainpy/BrainPy)**: The solution for the general-purpose brain dynamics programming. \r\n- **[brainpy-examples](https://github.com/brainpy/examples)**: Comprehensive examples of BrainPy computation. \r\n- **[brainpy-datasets](https://github.com/brainpy/datasets)**: Neuromorphic and Cognitive Datasets for Brain Dynamics Modeling.\r\n- [\u300a\u795e\u7ecf\u8ba1\u7b97\u5efa\u6a21\u5b9e\u6218\u300b (Neural Modeling in Action)](https://github.com/c-xy17/NeuralModeling)\r\n- [\u7b2c\u4e00\u5c4a\u795e\u7ecf\u8ba1\u7b97\u5efa\u6a21\u4e0e\u7f16\u7a0b\u57f9\u8bad\u73ed (First Training Course on Neural Modeling and Programming)](https://github.com/brainpy/1st-neural-modeling-and-programming-course)\r\n- [\u7b2c\u4e8c\u5c4a\u795e\u7ecf\u8ba1\u7b97\u5efa\u6a21\u4e0e\u7f16\u7a0b\u57f9\u8bad\u73ed (Second Training Course on Neural Modeling and Programming)](https://github.com/brainpy/2nd-neural-modeling-and-programming-course)\r\n\r\n\r\n## Citing \r\n\r\nBrainPy is developed by a team in Neural Information Processing Lab at Peking University, China. \r\nOur team is committed to the long-term maintenance and development of the project. \r\n\r\nIf you are using ``brainpy``, please consider citing [the corresponding papers](https://brainpy.readthedocs.io/en/latest/tutorial_FAQs/citing_and_publication.html). \r\n\r\n\r\n\r\n## Ongoing development plans\r\n\r\nWe highlight the key features and functionalities that are currently under active development. \r\n\r\nWe also welcome your contributions \r\n(see [Contributing to BrainPy](https://brainpy.readthedocs.io/en/latest/tutorial_advanced/contributing.html)). \r\n\r\n- [x] model and data parallelization on multiple devices for dense connection models\r\n- [ ] model parallelization on multiple devices for sparse spiking network models\r\n- [ ] data parallelization on multiple devices for sparse spiking network models\r\n- [ ] pipeline parallelization on multiple devices for sparse spiking network models\r\n- [ ] multi-compartment modeling\r\n- [ ] measurements, analysis, and visualization methods for large-scale spiking data\r\n- [ ] Online learning methods for large-scale spiking network models\r\n- [ ] Classical plasticity rules for large-scale spiking network models\r\n\r\n",
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