Stochastic synchronization of neural activity waves

TL;DR

This paper shows that common spatiotemporal noise can synchronize waves in different layers of neural networks, including stationary, traveling, and breather waves, through a stochastic differential equation framework.

Contribution

It introduces a novel analytical approach to understanding noise-induced synchronization of neural activity waves across multiple layers.

Findings

Common noise can synchronize diverse neural waves.

Analytical expression for stability via Lyapunov exponent.

Extension of synchronization theory to wave phenomena.

Abstract

We demonstrate that waves in distinct layers of a neuronal network can become phase-locked by common spatiotemporal noise. This phenomenon is studied for stationary bumps, traveling waves, and breathers. A weak noise expansion is used to derive an effective equation for the position of the wave in each layer, yielding a stochastic differential equation with multiplicative noise. Stability of the synchronous state is characterized by a Lyapunov exponent, which we can compute analytically from the reduced system. Our results extend previous work on limit-cycle oscillators, showing common noise can synchronize waves in a broad class of models.