Variational online learning of neural dynamics

TL;DR

This paper introduces a flexible online variational learning framework for neural dynamics that efficiently models latent nonlinear states and observations, enabling real-time neural data analysis and potential behavioral interventions.

Contribution

It presents a novel online variational Bayes method for jointly learning neural dynamical systems and observation models with constant complexity, suitable for real-time applications.

Findings

Efficient online learning of neural dynamics with constant time and space complexity.

Ability to incorporate complex observation noise distributions.

Potential for real-time neural data analysis and behavioral modulation.

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. Nonlinear 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. It brings the challenge of learning both latent neural state and the underlying dynamical system because neither is known for neural systems a priori. We developed a flexible online learning framework for latent nonlinear state dynamics and filtered latent states. Using the stochastic gradient variational Bayes approach, our method jointly optimizes the parameters of the nonlinear dynamical system, the observation model, and the black-box recognition model. Unlike previous approaches, our framework can incorporate non-trivial distributions of observation noise and has constant time and space complexity. These features make our approach amenable to real-time applications and the potential to automate analysis and experimental design in ways that testably track and modify behavior using stimuli designed to influence learning.