@Article{Supelec647,
author = {Lucian Alecu and Hervé Frezza-Buet and Frédéric Alexandre},
title = {{Can self-organization emerge through dynamic neural fields computation\'e }},
journal = {Connection Science},
year = {2011},
volume = {23},
number = {1},
pages = {1-31},
url = {http://dx.doi.org/10.1080/09540091.2010.526194},
doi = {10.1080/09540091.2010.526194},
abstract = {In this paper, dynamic neural fields are used to develop key
features of
a cortically-inpired computational module. Under the
perspective of
designing computational systems that can exhibit the
flexibility and
genericity of the cortical substrate, using neural field as the
competition layer for self-organizing modules has to be
considered. However, despite the fact that they serve as a
biologically-inspired model, applying dynamic neural fields to
drive
self-organization is not straightforward. In
order to address that issue, an original method for evaluating
neural field equations is proposed, based on statistical
measurements
of the field behavior in some scenarios. Limitations of
classical
neural field equations are then quantified, and an original
field
equation is proposed to overcome these difficulties. The
performance of the proposed field model is discussed in
comparison with some previously considered models, leading to
the
promotion of the proposed model as a suitable mean for
processing
competition
in cortex-like computation for cognitive systems.
}
}