# BrainMass
**Whole-brain modeling with differentiable neural mass models**
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://brainmass.readthedocs.io/)
BrainMass is a Python library for whole-brain computational modeling using differentiable neural mass models. Built on JAX for high-performance computing, it provides tools for simulating brain dynamics, analyzing neural networks, and modeling hemodynamic responses.
## Features
- **Neural Mass Models**: Wilson-Cowan model for excitatory-inhibitory population dynamics
- **Hemodynamic Modeling**: BOLD signal simulation using the Balloon-Windkessel model
- **Network Coupling**: Diffusive and additive coupling mechanisms for brain connectivity
- **Noise Modeling**: Ornstein-Uhlenbeck processes for realistic neural noise
- **JAX-Powered**: GPU acceleration and automatic differentiation
- **Real Brain Data**: Example datasets from HCP and other neuroimaging studies
## Installation
### From PyPI (recommended)
```bash
pip install brainmass
```
### From Source
```bash
git clone https://github.com/chaobrain/brainmass.git
cd brainmass
pip install -e .
```
### GPU Support
For CUDA 12 support:
```bash
pip install brainmass[cuda12]
```
For TPU support:
```bash
pip install brainmass[tpu]
```
### Ecosystem
For whole brain modeling ecosystem:
```bash
pip install BrainX
# GPU support
pip install BrainX[cuda12]
# TPU support
pip install BrainX[tpu]
```
## Dependencies
Core dependencies:
- `jax`: High-performance computing and automatic differentiation
- `numpy`: Numerical computations
- `brainstate`: State management and neural dynamics
- `brainunit`: Unit system for neuroscience
- `brainscale`: Online learning support
- `braintools`: Additional analysis tools
Optional dependencies:
- `matplotlib`: Plotting and visualization
- `nevergrad`: Parameter optimization
## Documentation
Full documentation is available at [brainmass.readthedocs.io](https://brainmass.readthedocs.io/).
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## Citation
If you use BrainMass in your research, please cite:
```bibtex
@software{brainmass,
title={BrainMass: Whole-brain modeling with differentiable neural mass models},
author={BrainMass Developers},
url={https://github.com/chaobrain/brainmass},
version={0.0.1},
year={2025}
}
```
## License
BrainMass is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for details.
## Support
- **Issues**: [GitHub Issues](https://github.com/chaobrain/brainmass/issues)
- **Documentation**: [ReadTheDocs](https://brainmass.readthedocs.io/)
- **Contact**: chao.brain@qq.com
---
**Keywords**: neural mass model, brain modeling, computational neuroscience, JAX, differentiable programming
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"description": "# BrainMass\n\n**Whole-brain modeling with differentiable neural mass models**\n\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://www.python.org/downloads/)\n[](https://brainmass.readthedocs.io/)\n\nBrainMass is a Python library for whole-brain computational modeling using differentiable neural mass models. Built on JAX for high-performance computing, it provides tools for simulating brain dynamics, analyzing neural networks, and modeling hemodynamic responses.\n\n## Features\n\n- **Neural Mass Models**: Wilson-Cowan model for excitatory-inhibitory population dynamics\n- **Hemodynamic Modeling**: BOLD signal simulation using the Balloon-Windkessel model\n- **Network Coupling**: Diffusive and additive coupling mechanisms for brain connectivity\n- **Noise Modeling**: Ornstein-Uhlenbeck processes for realistic neural noise\n- **JAX-Powered**: GPU acceleration and automatic differentiation\n- **Real Brain Data**: Example datasets from HCP and other neuroimaging studies\n\n## Installation\n\n### From PyPI (recommended)\n```bash\npip install brainmass\n```\n\n### From Source\n```bash\ngit clone https://github.com/chaobrain/brainmass.git\ncd brainmass\npip install -e .\n```\n\n### GPU Support\nFor CUDA 12 support:\n```bash\npip install brainmass[cuda12]\n```\n\nFor TPU support:\n```bash\npip install brainmass[tpu]\n```\n\n### Ecosystem\n\nFor whole brain modeling ecosystem:\n```bash\npip install BrainX \n\n# GPU support\npip install BrainX[cuda12]\n\n# TPU support\npip install BrainX[tpu]\n```\n\n\n## Dependencies\n\nCore dependencies:\n- `jax`: High-performance computing and automatic differentiation\n- `numpy`: Numerical computations\n- `brainstate`: State management and neural dynamics\n- `brainunit`: Unit system for neuroscience\n- `brainscale`: Online learning support\n- `braintools`: Additional analysis tools\n\n Optional dependencies:\n- `matplotlib`: Plotting and visualization\n- `nevergrad`: Parameter optimization\n\n## Documentation\n\nFull documentation is available at [brainmass.readthedocs.io](https://brainmass.readthedocs.io/).\n\n## Contributing\n\nWe welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n## Citation\n\nIf you use BrainMass in your research, please cite:\n\n```bibtex\n@software{brainmass,\n title={BrainMass: Whole-brain modeling with differentiable neural mass models},\n author={BrainMass Developers},\n url={https://github.com/chaobrain/brainmass},\n version={0.0.1},\n year={2025}\n}\n```\n\n## License\n\nBrainMass is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for details.\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/chaobrain/brainmass/issues)\n- **Documentation**: [ReadTheDocs](https://brainmass.readthedocs.io/)\n- **Contact**: chao.brain@qq.com\n\n---\n\n**Keywords**: neural mass model, brain modeling, computational neuroscience, JAX, differentiable programming\n",
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