Topic: Neuroscientific data

Neuroscientific data exhibits complex, nonlinear dynamics, varying in time and over trials. A growing body of work has focused on capturing nonlinear dynamics, doing so in increasingly complex ways for modeling and inference. Meanwhile, capturing the dependency on experimental conditions surrounding the neural recordings remains understudied, and few methods leverage the correlation structure across experimental conditions. This poses difficulties for model estimation as well as any analyses comparing and interpolating between conditions. In this work, we develop the Gaussian Process Linear Dy