Learning Structured Neural Dynamics From Single Trial Population Recording

Josue Nassar, Scott W. Linderman, Yuan Zhao, Mónica F. Bugallo, Il Memming Park

Abstract: To understand the complex nonlinear dynamics of neural circuits, we fit a structured state-space model called tree-structured recurrent switching linear dynamical system (TrSLDS) to noisy high-dimensional neural time series. TrSLDS is a multi-scale hierarchical generative model for the state-space dynamics where each node of the latent tree captures locally linear dynamics. TrSLDS can be learned efficiently and in a fully Bayesian manner using Gibbs sampling. We showcase TrSLDS' potential of inferring low-dimensional interpretable dynamical systems on a variety of examples.