@InProceedings{Supelec444,
author = {Lucian Alecu and Herv├ę Frezza-Buet},
title = {An Empirical Evaluation Framework for Qualifying Dynamic Neural Fields},
year = {2008},
booktitle = {Proceedings of the second french conference on Computational Neuroscience, Neurocomp, Marseille},
pages = {(4 pages)},
month = {October},
editor = {Laurent E. Perrinet and Emmanuel DaucÚ},
url = {http://www.metz.supelec.fr/~alecu_luc/papers/neurocomp08.pdf},
isbn = {978-2-9532965-0-1},
abstract = {In this paper, the behavior of dynamic neural fields is studied through the lens of performance. As an alternative to the currently available analytical instruments, an empirical evaluation methodology is proposed in order to examine the dynamic quality of a field. This consists of simulating the field through various key scenarios and compare the observed behavior to an optimal expected one. Some desired effects concerning the evolution of an ideal field are inspected, and a performance criterion is defined accordingly. Practically, this approach implements a generic benchmark framework for qualifying neural fields, allowing to inspect the evolution of the model in different key situations. The presented methodology provides a basis for a methodological evaluation of the computational power of neural fields, when they serve as a basis of decision processes. In a such integrated system, our approach allows to tune the free parameters of the field equation according to the behavior expected from them.}
}