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A dynamic neural field mechanism for self-organization

BMC Neuroscience200910 (Suppl 1) :P273

  • Published:


  • Cognitive Task
  • Inspire Feature
  • Local Competition
  • Neural Field
  • Competition Mechanism
As introduced by Amari [1], 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 [2] 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, which 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 equations (those of Amari [1] and Folias [3]) do not perform satisfactory, as seen in Figure 1, panels b and c. Highlighting 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 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 but strong lateral excitation). As a conclusion, a field driven by the new equations achieves good results in solving the considered 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 cognitive tasks.
Figure 1
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.

Authors’ Affiliations

CORTEX, INRIA Nancy Grand-Est, 615 rue du Jardin Botanique, Villers-lès-Nancy, 54600, France
IMS, SUPELEC Metz, 2 rue Edouard Belin, Metz, 57070, France


  1. Amari S: Dynamics of pattern formation in lateral inhibition type neural fields. Biological Cybernetics. 1977, 27: 77-87. 10.1007/BF00337259.PubMedView ArticleGoogle Scholar
  2. Kohonen T: Self-Organization and Associative Memory, volume 8 of Springer Series in Information Sciences. 1989, Springer-VerlagView ArticleGoogle Scholar
  3. Folias SE, Bressloff PC: Breathers in two-dimensional neural media. Physical Review Letters. 2005, 95.Google Scholar


© Alecu and Frezza-Buet; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.