Visual attention using spiking neural maps
Identifieur interne : 005296 ( Hal/Curation ); précédent : 005295; suivant : 005297Visual attention using spiking neural maps
Auteurs : Roberto Vazquez [Mexique] ; Bernard Girau [France] ; Jean-Charles Quinton [France]Source :
Abstract
Visual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noise or distracters. Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model.
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Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Visual attention using spiking neural maps</title><author role="crp"><persName><forename type="first">Roberto</forename><forename type="middle">A.</forename><surname>Vazquez</surname></persName><email>roberto.a.vazquez@gmail.com</email><idno type="halauthor">621205</idno><orgName ref="#struct-367954"></orgName><affiliation ref="#struct-160222"></affiliation></author><author role="aut"><persName><forename type="first">Bernard</forename><surname>Girau</surname></persName><email>girau@loria.fr</email><idno type="halauthor">448757</idno><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email><idno type="halauthor">498336</idno><affiliation ref="#struct-2348"></affiliation></author><editor role="depositor"><persName><forename>Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2011-06-27 17:30:32</date><date type="whenModified">2016-05-19 01:09:12</date><date type="whenReleased">2011-06-28 10:59:09</date><date type="whenProduced">2011-07-31</date><date type="whenEndEmbargoed">2011-06-27</date><ref type="file" target="https://hal.inria.fr/inria-00603929/document"><date notBefore="2011-06-27"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00603929/file/ijcnn_final_PID1818371.pdf"><date notBefore="2011-06-27"></date></ref></edition><respStmt><resp>contributor</resp><name key="149687"><persName><forename>Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00603929</idno><idno type="halUri">https://hal.inria.fr/inria-00603929</idno><idno type="halBibtex">vazquez:inria-00603929</idno><idno type="halRefHtml">International Joint Conference on Neural Networks IJCNN 2011, Jul 2011, San José, United States. IEEE Computational Intelligence Society, 2011, The 2011 International Joint Conference on Neural Networks</idno><idno type="halRef">International Joint Conference on Neural Networks IJCNN 2011, Jul 2011, San José, United States. IEEE Computational Intelligence Society, 2011, The 2011 International Joint Conference on Neural Networks</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Visual attention using spiking neural maps</title><author role="crp"><persName><forename type="first">Roberto</forename><forename type="middle">A.</forename><surname>Vazquez</surname></persName><email>roberto.a.vazquez@gmail.com</email><idno type="halAuthorId">621205</idno><orgName ref="#struct-367954"></orgName><affiliation ref="#struct-160222"></affiliation></author><author role="aut"><persName><forename type="first">Bernard</forename><surname>Girau</surname></persName><email>girau@loria.fr</email><idno type="halAuthorId">448757</idno><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email><idno type="halAuthorId">498336</idno><affiliation ref="#struct-2348"></affiliation></author></analytic><monogr><meeting><title>International Joint Conference on Neural Networks IJCNN 2011</title><date type="start">2011-07-31</date><date type="end">2011-08-05</date><settlement>San José</settlement><country key="US">United States</country></meeting><respStmt><resp>conferenceOrganizer</resp><name>Ali Minai, Hava Siegelmann</name></respStmt><imprint><publisher>IEEE Computational Intelligence Society</publisher><biblScope unit="serie">The 2011 International Joint Conference on Neural Networks</biblScope><date type="datePub">2011-07-31</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="scco.comp">Cognitive science/Computer science</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Visual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noise or distracters. Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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