TiDHy: Timescale Demixing via Hypernetworks to learn simultaneous dynamics from mixed observations.

ABSTRACT: Neural activity and behavior arise from multiple concurrent time-varying systems, including neuromodulation, neural state, and history; however, most current approaches model these data as one set of dynamics with a single timescale. Here we develop Timescale Demixing via Hypernetworks (TiDHy) as a new computational method to model spatiotemporal data, decomposing them into multiple simultaneous latent dynamical systems that may span orders-of-magnitude different timescales. Specifically, we train a hypernetwork to dynamically reweigh linear combinations of latent dynamics. This approach enables accurate data reconstruction, converges to true latent dynamics, and captures multiple timescales of variation. We first demonstrate that TiDHy can demix dynamics and timescale

SUBMITTER: Abe ETT 

PROVIDER: S-EPMC11838317 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature