Wandering bumps in stochastic neural fields

TL;DR

This paper investigates how noise influences the movement and stability of stationary bumps in neural fields, revealing that noise causes wandering and that heterogeneity can pin bumps, with diffusion affected by heterogeneity frequency.

Contribution

It introduces a stochastic neural field model with noise and heterogeneity, analyzing bump wandering, pinning, and how heterogeneity modulates diffusion behavior.

Findings

Noise induces diffusive wandering of bumps.

Heterogeneous inputs can temporarily pin bumps.

Higher heterogeneity frequency reduces diffusion, approaching homogeneous behavior.

Abstract

We study the effects of noise on stationary pulse solutions (bumps) in spatially extended neural fields. The dynamics of a neural field is described by an integrodifferential equation whose integral term characterizes synaptic interactions between neurons in different spatial locations of the network. Translationally symmetric neural fields support a continuum of stationary bump solutions, which may be centered at any spatial location. Random fluctuations are introduced by modeling the system as a spatially extended Langevin equation whose noise term we take to be multiplicative or additive. For nonzero noise, these bumps are shown to wander about the domain in a purely diffusive way. We can approximate the effective diffusion coefficient using a small noise expansion. Upon breaking the (continuous) translation symmetry of the system using a spatially heterogeneous inputs or synapses, bumps in the stochastic neural field can become temporarily pinned to a finite number of locations in the network. In the case of spatially heterogeneous synaptic weights, as the modulation frequency of this heterogeneity increases, the effective diffusion of bumps in the network approaches that of the network with spatially homogeneous weights.