Optimization of dynamic neural fields

Christian Igel, Wolfram Erlhagen, Dirk Jancke

Abstract: There is a growing interest in using dynamic neural fields for modeling biological and technical systems, but constructive ways to set up such models are still missing. We discuss gradient-based, evolutionary and hybrid algorithms for data-driven adaptation of neural field parameters. The proposed methods are evaluated using artificial and neuro-physiological data.