@InProceedings{Supelec535,

author = {Lucian Alecu and HervĂ© Frezza-Buet},

title = {A dynamic neural field mechanism for self-organization},

year = {2009},

booktitle = {Supplement of the online journal BMC Neuroscience, The nineteenth Annual Computational Neuroscience Meeting},

volume = {10},

number = {Suppl 1},

pages = {P273},

address = {Berlin (Germany)},

url = {http://www.metz.supelec.fr/metz/personnel/alecu_luc/papers/cns09.pdf},

doi = {10.1186/1471-2202-10-S1-P273},

abstract = {As introduced by Amari, dynamic neural fields (DNF) are a
mathematical formalism aiming to describe the spatio-temporal
evolution of the electrical potential of a population of cortical
neurons. Various cognitive tasks have been successfully solved
using this paradigm, but nevertheless, tasks requiring learning
and self-organizing abilities have rarely been addressed. Aiming
to extend the applicative area of DNF, we are hereby interested
in using this computational model to implement such
self-organizing mechanisms. Adapting the Kohonen's classical
algorithm for developing self-organizing maps (SOM), we propose a
DNF-driven architecture that may deploy also a self-organizing
mechanism. Benefiting from the biologically-inspired features of
the DNF, the advantage of such structure is that the computation
is fully-distributed among its entities. Unlike the classical SOM
algorithm, that requires a centralized computation of the global
maximum, our proposed architecture implements a distributed
decision computation, based on the local competition mechanism
deployed by neural fields. Once the architecture implemented, we
investigate the capacity of different neural field equations to
solve simple self-organization tasks. Our analysis concludes that
the considered classical equations do not perform satisfactory.
Highlighting the deficiencies of these equations that impeded
them to behave as expected, we propose a new system of equations,
enhancing the current models, in order to handle the observed
undesired effects. In summary, the novelty of these equations
consist in introducing an adaptive term that triggers the
re-inhibition of a so-called “unsustainable” bump of the field's
activity (one that no longer is stimulated by strong input, but
only by strong lateral excitation). As a conclusion, a field
driven by the new equations achieves good results in solving the
considered self-organizing task. Our research opens thus the way
to new approaches that aim using dynamic neural field to solve
more complex cognitive tasks.}

}