Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.

ABSTRACT: Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural-behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavio

SUBMITTER: Sani OG 

PROVIDER: S-EPMC11452342 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature