@InProceedings{Supelec494,
author = {Lucian Alecu and Hervé Frezza-Buet},
title = {{Reconciling neural fields to self-organization}},
year = {2009},
booktitle = {{European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN), Bruges, Belgium}},
pages = {571-576},
month = {April},
editor = {Michel Verleysen},
url = {http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-31.pdf},
isbn = {2-930307-09-9},
abstract = {Despite being successfully used in the design of various
biologically-inspired applications, the paradigm of dynamic
neural fields (DNF) does not seem to have been exploited at its
full potential yet. Partly because of the difficulties
concerning
a comprehensive theoretical study of them, essential aspects as
learning mechanisms have rarely been addressed in the
literature.
In the current paper, we first show that classical DNF equations
fail to offer reliable support for self-organization, unveiling
some behavioural issues that prevent the fields to achieve this
goal. Then, as an alternative to these, we propose a new DNF
equation capable of deploying indeed a self-organizing mechanism
based on neural fields.
}
}