Stochastic motion of bumps in planar neural fields
Daniel Poll, Zachary P. Kilpatrick

TL;DR
This paper studies how random noise affects the movement of neural activity bumps in planar neural fields, deriving explicit formulas for their diffusive behavior and how external inputs can stabilize their position.
Contribution
It introduces a stochastic model for bump dynamics in neural fields, deriving explicit diffusion coefficients and analyzing the influence of external inputs on bump stability.
Findings
Bumps exhibit diffusive wandering due to noise, modeled as two-dimensional Brownian motion.
Weak external inputs can pin bumps, preventing diffusion and shaping their positional dynamics.
Analytical results match well with numerical simulations of bump motion.
Abstract
We analyze the effects of spatiotemporal noise on stationary pulse solutions (bumps) in neural field equations on planar domains. Neural fields are integrodifferential equations whose integral kernel describes the strength and polarity of synaptic interactions between neurons at different spatial locations of the network. Fluctuations in neural activity are incorporated by modeling the system as a Langevin equation evolving on a planar domain. Noise causes bumps to wander about the domain in a purely diffusive way. Utilizing a small noise expansion along with a solvability condition, we can derive an effective stochastic equation describing the bump dynamics as two-dimensional Brownian motion. The diffusion coefficient can then be computed explicitly. We also show that weak external inputs can pin the bump so it no longer wanders diffusively. Inputs reshape the effective potential that…
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