BrainMass

Whole-brain modeling with differentiable neural mass models

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, fitting neural signal data, and training cognitive tasks.

Installation

From PyPI (recommended)

pip install brainmass

From Source

git clone https://github.com/chaobrain/brainmass.git
cd brainmass
pip install -e .

GPU Support

For CUDA 12 support:

pip install brainmass[cuda12]

For TPU support:

pip install brainmass[tpu]

Ecosystem

For whole brain modeling ecosystem:

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.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Citation

If you use BrainMass in your research, please cite:

@software{brainmass,
  title={BrainMass: Whole-brain modeling with differentiable neural mass models},
  author={BrainMass Developers},
  url={https://github.com/chaobrain/brainmass},
  version={0.0.4},
  year={2025}
}

License

BrainMass is licensed under the Apache License 2.0. See LICENSE for details.

Support

• Issues: GitHub Issues
• Documentation: ReadTheDocs
• Contact: [email protected]

Ehe brain modeling ecosystem

See also the brain simulation ecosystem: https://brainmodeling.readthedocs.io/

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Keywords: neural mass model, brain modeling, computational neuroscience, JAX, differentiable programming