Topic: latent dynamical system

Large scale neural recordings are typically found to embed lower-dimensional structure that reflects behaviour. Empirically, the best models of such low-dimensional structure -- by several statistical measures -- tend to be those that describe neural recordings via a latent dynamical system. Importantly, as recordings are typically made in one or a subset of areas within the brain, the dynamics that best capture the data cannot in general be expected to be fully autonomous, but may instead be driven by unobserved inputs. Learning the parameters of a probabilistic dynamical system whilst simult

Latent dynamical system (LDS) models have been widely used to characterize the dynamics of high-dimensional neural population activity. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and thus to capture the effects of cell-specific optogenetic perturbations on neural activity and behavior. To address this gap, we introduce the "cell-type dynamical systems" (CTDS) model, which extends latent linear dynamical systems to contain distinct latent variables for each cell