Special Session 33: Modeling and Data Analysis for Complex Systems and Dynamics

Traveling Front Solution Stability in a Lateral Inhibition Network in the Neural Field Model

Yixin Guo
Drexel University
USA
Co-Author(s):    Dominik Macaluso
Abstract:
We investigate the stability for traveling front solutions of the neural field model. This model has been studied intensively for propagating patterns with saturating Heaviside gain of neuron firing activity. Previous work has shown the existence of traveling fronts of the neural field model in a more complex setting using a non-saturating piecewise linear gain. We aim to study the stability of traveling fronts of the neural field model through the Evan's function. We attain the Evan's function of traveling fronts using an integration of analytical derivations and computational approach for the neural field model with previously uninvestigated piecewise linear gain. Using this approach, we are able to determine the stability for any given traveling fronts of the neural field model