Detecting State Changes in Functional Neuronal Connectivity using Factorial Switching Linear Dynamical Systems

TL;DR

This paper introduces a factorial switching linear dynamical system to detect multiple simultaneous state changes in neuronal connectivity, improving understanding of brain activity dynamics.

Contribution

It presents a novel factorial hidden Markov model and scalable inference algorithm that captures joint and independent subnetworks in neuronal activity.

Findings

Successfully recovers ground-truth neuronal connectivity structures.

Provides insights into neuronal maturation from microelectrode recordings.

Abstract

A key question in brain sciences is how to identify time-evolving functional connectivity, such as that obtained from recordings of neuronal activity over time. We wish to explain the observed phenomena in terms of latent states which, in the case of neuronal activity, might correspond to subnetworks of neurons within a brain or organoid. Many existing approaches assume that only one latent state can be active at a time, in contrast to our domain knowledge. We propose a switching dynamical system based on the factorial hidden Markov model. Unlike existing approaches, our model acknowledges that neuronal activity can be caused by multiple subnetworks, which may be activated either jointly or independently. A change in one part of the network does not mean that the entire connectivity pattern will change. We pair our model with scalable variational inference algorithm, using a concrete relaxation of the underlying factorial hidden Markov model, to effectively infer the latent states and model parameters. We show that our algorithm can recover ground-truth structure and yield insights about the maturation of neuronal activity in microelectrode array recordings from in vitro neuronal cultures.