Christopher Versteeg

Separating a brain area's own dynamics from the inputs driving it is hard, because the same recorded activity can arise from many combinations of the two. DynISys models each recorded area as its own low-dimensional dynamical system, lets areas exchange signals only through a few explicit channels, and maps latent states to firing rates through an invertible readout. On simultaneous PMd and M1 recordings from two monkeys, eight latent dimensions per area matched the PSTH reconstruction of MR-LFADS at 128 per area, and the inferred PMd-to-M1 communication timing was consistent with target information traveling from PMd to M1.

Separating a brain area's own dynamics from the inputs driving it is hard, because the same recorded activity can arise from many combinations of the two. DynISys models each recorded area as its own low-dimensional dynamical system, lets areas exchange signals only through a few explicit channels, and maps latent states to firing rates through an invertible readout. On simultaneous PMd and M1 recordings from two monkeys, eight latent dimensions per area matched the PSTH reconstruction of MR-LFADS at 128 per area, and the inferred PMd-to-M1 communication timing was consistent with target information traveling from PMd to M1.