Template based black-box optimization of dynamic neural fields

Abstract

Due to their strong non-linear behavior, optimizing the parameters of dynamic neural fields is particularly challenging and often relies on expert knowledge and trial and error. In this paper, we study the ability of particle swarm optimization (PSO) and covariance matrix adaptation (CMA-ES) to solve this problem when scenarios specifying the input feeding… (More)
DOI: 10.1016/j.neunet.2013.04.008

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