@Article{Supelec843,
author = {Jérémy Fix},
title = {{Template based black-box optimization of dynamic neural fields}},
journal = {Neural Networks},
year = {2013},
volume = {46},
pages = {40--49},
month = {October},
url = {http://dx.doi.org/10.1016/j.neunet.2013.04.008},
doi = {http://dx.doi.org/10.1016/j.neunet.2013.04.008},
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 the field and desired
output profiles are provided. A set of spatial lower and upper
bounds, called templates are introduced to define a set of
desired output profiles. The usefulness of the method is
illustrated on three classical scenarios of dynamic neural
fields: competition, working memory and tracking.}
}