@InProceedings{Supelec543,
author = {Lucian Alecu and Hervé Frezza-Buet},
title = {{Application-driven parameter tuning methodology for dynamic neural field equations}},
year = {2009},
booktitle = {{ Neural Information Processing, ICONIP'09 Proceedings, Part I}},
publisher = {Springer Berlin / Heidelberg},
volume = {5863/2009},
pages = {135-142},
series = {Lecture Notes in Computer Science},
address = {Bangkok (Thailand)},
url = {http://www.metz.supelec.fr/metz/personnel/alecu_luc/papers/iconip09.pdf},
isbn = {978-3-642-10676-7},
doi = {10.1007/978-3-642-10677-4_15},
abstract = {In this paper, a method is introduced in order to qualify the
performance of dynamic neural fields (DNF). The method is
applied
to Amari’s DNF equations, in order to drive the tuning of its
free parameters. An original evaluation procedure is presented,
and then applied to some input evolution scenarios. Such
scenarios define an applicative context, for which the
parameters
with the lowest evaluation are optimal.}
}