There is a growing interest in using dynamic neural fields for modeling bio
logical and technical systems, but constructive ways to set up such models
are still missing. We discuss gradient-based, evolutionary and hybrid algor
ithms for data-driven adaptation of neural field parameters. The proposed m
ethods are evaluated using artificial and neuro-physiological data. (C) 200
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