Topic: latent trajectories

Low dimensional neural manifolds provide a compact characterization of population activity and shared co-variability across brain areas. Nonetheless technical limitations restrict the applicability of these tools to experimental designs without explicit trial structure, and thus cannot be used to understand neural responses during naturalistic behavior. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) ex

Recent studies have demonstrated that brain areas involved in motor skills display common representations of latent trajectories for both stereotypical and generalizable dynamical cortical behavior. However, current research often focuses on analyzing single tasks and individual sessions, missing the opportunity to exploit the structural patterns across datasets. By fitting a unified model to a collection of neural data with shared features, we can increase data usage efficiency and enhance our understanding of common neural computation structures, despite the challenge of distinct populations