brainmass documentation#
brainmass implements neural mass models with brainstate, enabling whole-brain modeling with differentiable programming and JAX.
Features#
13+ neural mass models from phenomenological oscillators to physiological population models, covering EEG, MEG, and fMRI applications
Fit model parameters to empirical data using gradient-based (JAX) or gradient-free (Nevergrad) optimization
Built-in BOLD hemodynamics and EEG/MEG lead-field models for linking neural activity to neuroimaging signals
Automatic dimensional analysis with brainunit prevents unit errors in scientific computing
Installation#
pip install -U brainmass[cpu]
pip install -U brainmass[cuda12]
pip install -U brainmass[cuda13]
pip install -U brainmass[tpu]
See Installation for detailed instructions.
Where to Start#
Start with the Quickstart Tutorial for a 5-minute introduction
Browse the Examples Gallery for practical applications
Check the API Reference for detailed documentation
Read the Developer Guide to get started
Documentation Structure#
Step-by-step guides for common tasks
Jupyter notebooks with practical applications
Complete API documentation
Contributing and extending brainmass
BrainX Ecosystem#
brainmass is one part of our brain modeling ecosystem.