Optimization of dynamic neural fields
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.
dynamic neural fields gradient-based optimization evolutionary optimization population representation primary visual cortex of cat