Neural field equations with time-periodic external inputs and some applications to visual processing

TL;DR

This paper develops a control-theoretic framework for neural field equations with periodic inputs, explaining visual afterimages and illusions, and providing approximations for neural interactions under flickering stimuli.

Contribution

It introduces a novel input-output control approach to analyze neural fields with time-periodic stimuli, overcoming limitations of classical bifurcation methods.

Findings

Neural dynamics converge to periodic states under periodic inputs.

Approximate integral kernels for neural interactions are derived.

Width of illusory contours relates to flickering frequency and inhibition strength.

Abstract

The aim of this work is to present a mathematical framework for the study of flickering inputs in visual processing tasks. When combined with geometric patterns, these inputs influence and induce interesting psychophysical phenomena, such as the MacKay and the Billock-Tsou effects, where the subjects perceive specific afterimages typically modulated by the flickering frequency. Due to the symmetry-breaking structure of the inputs, classical bifurcation theory and multi-scale analysis techniques are not very effective in our context. We thus take an approach based on the input-output framework of control theory for Amari-type neural fields. This allows us to prove that, when driven by periodic inputs, the dynamics converge to a periodic state. Moreover, we study under which assumptions these nonlinear dynamics can be effectively linearised, and in this case we present a precise approximation of the integral kernel for short-range excitatory and long-range inhibitory neuronal interactions. Finally, for inputs concentrated at the center of the visual field with a flickering background, we directly relate the width of the illusory contours appearing in the afterimage with both the flickering frequency and the strength of the inhibition.