Dynamic neural field with local inhibition
Identifieur interne : 003910 ( Istex/Curation ); précédent : 003909; suivant : 003911Dynamic neural field with local inhibition
Auteurs : Nicolas P. Rougier [France]Source :
- Biological Cybernetics [ 0340-1200 ] ; 2006-03-01.
Abstract
Abstract: A lateral-inhibition type neural field model with restricted connections is presented here and represents an experimental extension of the continuum neural field theory (CNFT) by suppression of the global inhibition. A modified CNFT equation is introduced and allows for a locally defined inhibition to spatially expand within the network and results in a global competition extending far beyond the range of local connections by virtue of diffusion of inhibition. The resulting model is able to attend to a moving stimulus in the presence of a very high level of noise, several distractors or a mixture of both.
Url:
DOI: 10.1007/s00422-005-0034-8
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<front><div type="abstract" xml:lang="en">Abstract: A lateral-inhibition type neural field model with restricted connections is presented here and represents an experimental extension of the continuum neural field theory (CNFT) by suppression of the global inhibition. A modified CNFT equation is introduced and allows for a locally defined inhibition to spatially expand within the network and results in a global competition extending far beyond the range of local connections by virtue of diffusion of inhibition. The resulting model is able to attend to a moving stimulus in the presence of a very high level of noise, several distractors or a mixture of both.</div>
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