Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems

Excerpt

arXiv:2610.01786v1 Announce Type: new Abstract: Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural recordings is important for better understanding neural computation and function. However, traditional approaches based on autocorrelation fitting are difficult to scale to high-dimensional population recordings and can become unreliable when neural dynamics change with behavior. State-space models ha