Bio Med CentralPage 1 of 2 page number not for citation purposes BMC Neuroscience Open Access Poster presentation A dynamic neural field mechanism for self-organization Lucian Alecu*1,2
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BMC Neuroscience
Open Access
Poster presentation
A dynamic neural field mechanism for self-organization
Lucian Alecu*1,2 and Hervé Frezza-Buet2
Address: 1 CORTEX, INRIA Nancy Grand-Est, 615 rue du Jardin Botanique, Villers-lès-Nancy, 54600, France and 2 IMS, SUPELEC Metz, 2 rue
Edouard Belin, Metz, 57070, France
Email: Lucian Alecu* - Lucian.Alecu@Supelec.fr; Hervé Frezza-Buet - Herve.Frezza-Buet@Supelec.fr
* Corresponding author
As introduced by Amari [1], dynamic neural fields (DNF)
are a mathematical formalism aiming to describe the
spa-tio-temporal evolution of the electrical potential of a
pop-ulation of cortical neurons Various cognitive tasks have
been successfully solved using this paradigm, but
never-theless, tasks requiring learning and self-organizing
abili-ties 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-organ-izing mechanisms Adapting the Kohonen's classical
algo-rithm [2] for developing self-organizing maps (SOM), we
propose a DNF-driven architecture that may deploy also a
self-organizing mechanism Benefiting from the
biologi-cally 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,
which requires a centralized computation of the global
maximum, our proposed architecture implements a
dis-tributed decision computation, based on the local
compe-tition mechanism deployed by neural fields Once the
architecture implemented, we investigate the capacity of
different neural field equations to solve simple
self-organ-ization tasks Our analysis concludes that the considered
equations (those of Amari [1] and Folias [3]) do not
per-form satisfactory, as seen in Figure 1, panels b and c
High-lighting the deficiencies of these equations that impeded
them to behave as expected, we propose a new system of
equations, enhancing that proposed by Folias [3] in order
to handle the observed undesired effects In summary, the
novelty of these equations consist in introducing an
adap-tive 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 but strong lateral excitation) As a conclusion, a field driven by the new equations achieves good results in solving the consid-ered self-organizing task (as seen in Figure 1d) Our research thus opens the way to new approaches that aim using dynamic neural field to solve more complex cogni-tive tasks
References
1. Amari S: Dynamics of pattern formation in lateral inhibition
type neural fields Biological Cybernetics 1977, 27:77-87.
2. Kohonen T: Self-Organization and Associative Memory, volume 8 of
Springer Series in Information Sciences Springer-Verlag; 1989
3. Folias SE, Bressloff PC: Breathers in two-dimensional neural
media Physical Review Letters 2005:95.
from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
Berlin, Germany 18–23 July 2009
Published: 13 July 2009
BMC Neuroscience 2009, 10(Suppl 1):P273 doi:10.1186/1471-2202-10-S1-P273
<supplement> <title> <p>Eighteenth Annual Computational Neuroscience Meeting: CNS*2009</p> </title> <editor>Don H Johnson</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1471-2202-10-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf</url> </supplement>
This abstract is available from: http://www.biomedcentral.com/1471-2202/10/S1/P273
© 2009 Alecu and Frezza-Buet; licensee BioMed Central Ltd
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Solving a one-dimensional self-organizing task, aiming to learn the herein shown coronal shape (inner radius 0.5, outer radius 1.0), with the support provided by the 3-layer architecture described in the document
Figure 1
Solving a one-dimensional self-organizing task, aiming to learn the herein shown coronal shape (inner radius 0.5, outer radius 1.0), with the support provided by the 3-layer architecture described in the document From
left to right: a Kohonen classical SOM; b Amari DNF; c Folias DNF; d the new DNF system of equations