Skip to main content

Ecosystem for Brain Simulation.

Project description

Brain Simulation Ecosystem (BrainX)

PyPI version Read the Docs Continuous Integration

Header image of Brain Modeling Ecosystem.

Overview

The BrainX ecosystem provides comprehensive framework for brain simulation and modeling. It provides tools and libraries for researchers to model, simulate, train, and analyze neural systems at different scales.

Core components in this ecosystem includes:

  • BrainPy: Modeling of point neuron-based spiking neural networks (SNNs), comes from Prof. Si Wu's lab at Peking University.

  • BrainUnit: Comprehensive physical units and unit-aware mathematical system for brain dynamics.

  • BrainCell: Intuitive, parallel, and efficient simulation for biologically detailed brain cell modeling. Collaborated with Prof. Songting Li's lab at Shanghai Jiao Tong University.

  • BrainMass: Whole-brain modeling with differentiable neural mass models.

  • BrainState: State-based IR compilation for efficient simulation of brain models on CPUs, GPUs, and TPUs.

  • BrainTaichi: The first-generation framework for customizing event-driven operators based on Taichi Lang syntax.

  • BrainEvent: Enabling event-driven computations in brain dynamics.

  • BrainTrace: Enabling scalable online learning for brain dynamics: $O(N)$ complexity for SNNs, and $O(N^2)$ for RNN computations.

  • BrainTools: Commonly used tools for brain dynamics programming, for example checkpointing.

  • More components may be added in the future.

Installation

The ecosystem can be installed with the following command:

pip install BrainX -U

This command installs the core package and pins specific versions of the component projects known to work together, ensuring compatibility based on integration tests.

On CPU platforms, the following command can be used to install the ecosystem with all components:

pip install BrainX[cpu] -U

On GPU platforms, the following command can be used to install the ecosystem with all components:

pip install BrainX[cuda12] -U

pip install BrainX[cuda13] -U

On TPU platforms, the following command can be used to install the ecosystem with all components:

pip install BrainX[tpu] -U

For development, you might want to clone the repository and install it in editable mode:

git clone https://github.com/chaobrain/brain-modeling-ecosystem.git
cd brain-modeling-ecosystem
pip install -e .

Documentation

For detailed documentation, tutorials, and examples, visit our Documentation Portal.

Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for more information on how to get involved.

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

Citation

If you use the BrainX Ecosystem in your research, please cite it appropriately. Refer to the citation guide on our documentation portal.

Support

If you have questions, encounter issues, or need support, please:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

brainx-2026.1.19.tar.gz (8.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

brainx-2026.1.19-py3-none-any.whl (8.8 kB view details)

Uploaded Python 3

File details

Details for the file brainx-2026.1.19.tar.gz.

File metadata

  • Download URL: brainx-2026.1.19.tar.gz
  • Upload date:
  • Size: 8.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for brainx-2026.1.19.tar.gz
Algorithm Hash digest
SHA256 70db28cb9e1e83dd5e059a86fb4e45a66e4b14c7956245328b3e6fdbb1c081e1
MD5 4fc6d5b429367b8e5b4fb95e9a32a7eb
BLAKE2b-256 12aab404e96a56235aedc26ca111ec37196e862f8bd5ad2fc85775afe2feae25

See more details on using hashes here.

File details

Details for the file brainx-2026.1.19-py3-none-any.whl.

File metadata

  • Download URL: brainx-2026.1.19-py3-none-any.whl
  • Upload date:
  • Size: 8.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for brainx-2026.1.19-py3-none-any.whl
Algorithm Hash digest
SHA256 3bd0ff5f45281348c50f49f69f6adea88585a78a656d4ca8e80574bf9d94a261
MD5 169216bc54c1b1e32e5b3ef7208ca765
BLAKE2b-256 9a4b1e900df498fc468122a0b5570c5b33c8febc70e7a9c03e8cf8719d7b8811

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page