We demonstrated CLDS on a range of synthetic tasks and datasets, showing how to link dynamical structure to behaviorally relevant variables in a transparent way. [5/6]

CLDS = linear dynamical system in latent state (x), whose coefficients depend nonlinearly on task conditions (u) through Gaussian processes (GP)

CLDS leverages conditions to approximate the full nonlinear dynamics with locally linear LDSs, bridging the benefits of linear and nonlinear models. [3/5]