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arXiv Machine Learning By Siddharth Viswanath, Panayiotis Ketonis, Cassidy All, William Dai, Gabriel Escobedo, Christine Gao, Jeffrey Lee, Joao Felipe Rocha, Andrew J Steindl, Ian Adelstein, Michael Perlmutter, Dhananjay Bhaskar, Smita Krishnaswamy

BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamics

Read the original on arXiv Machine Learning →

BrainDyn is a graph ordinary differential equation (ODE) network that models brain activity as coordinated patterns across connected regions. It uses a cellular sheaf with dynamic restriction maps to mediate region-to-region communication through learned linear transformations, rather than the standard shared feature space and scalar summation of message‑passing networks. Evaluated on resting‑state fMRI, EEG, and simulated NEST data, BrainDyn outperforms competing models in multi‑region forecasting, autoregressive generation of brain transients, and prediction of altered transients after controlled perturbations, while its learned features are expressive, interpretable, and biologically aligned.

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