Variational Online Learning of Neural Dynamics.

ABSTRACT: New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Non-linear state space models provide an interpretable signal processing framework by combining an intuitive dynamical system with a probabilistic observation model, which can provide insights into neural dynamics, neural computation, and development of neural prosthetics and treatment through feedback control. This brings with it the challenge of learning both latent neural state and the underlying dynamical system because neither are known for neural systems a priori. We developed a flexible online learning framework for latent non-linear state dynamics and

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC7591751 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature