Spatiotemporal pattern coding using Neural Fields: Optimal parameter estimation
Identifieur interne : 002701 ( Hal/Checkpoint ); précédent : 002700; suivant : 002702Spatiotemporal pattern coding using Neural Fields: Optimal parameter estimation
Auteurs : Mauricio Cerda [France] ; Bernard Girau [France]Source :
Abstract
In this work we present the parameter optimization of a distributed model for the classification of temporal sequences. The model we present codes information with a population of units, and it can be applied to discriminate between several spatiotemporal sequences. The implementation of this classification technique strongly depends on how the the connection weights are set according to the sequence we want to code. This ``learning'' phase depends on several parameters, for which we present a detailed analysis in this work. In the first part, we give some examples and we retrieve the model parameters in 1D by extending other existing studies. Later on we extend the analysis into 2D, proposing at the end of the report a strategy to code any given spatiotemporal sequence that can be described as a set local spatiotemporal trajectories. The derivation of parameters is performed analytically and numerical simulations are performed to verify our results.
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type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="it"><term>computational neuroscience</term><term>dynamical systems</term><term>neural fields</term><term>pattern classification</term><term>spatiotemporal sequences</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this work we present the parameter optimization of a distributed model for the classification of temporal sequences. The model we present codes information with a population of units, and it can be applied to discriminate between several spatiotemporal sequences. The implementation of this classification technique strongly depends on how the the connection weights are set according to the sequence we want to code. This ``learning'' phase depends on several parameters, for which we present a detailed analysis in this work. In the first part, we give some examples and we retrieve the model parameters in 1D by extending other existing studies. Later on we extend the analysis into 2D, proposing at the end of the report a strategy to code any given spatiotemporal sequence that can be described as a set local spatiotemporal trajectories. The derivation of parameters is performed analytically and numerical simulations are performed to verify our results.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Spatiotemporal pattern coding using Neural Fields: Optimal parameter estimation</title><author role="aut"><persName><forename type="first">Mauricio</forename><surname>Cerda</surname></persName><email>cerdavim@loria.fr</email><idno type="halauthor">361392</idno><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Bernard</forename><surname>Girau</surname></persName><email>Bernard.Girau@loria.fr</email><idno type="halauthor">579887</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2348"></affiliation></author><editor role="depositor"><persName><forename>Mauricio</forename><surname>Cerda</surname></persName><email>cerdavim@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2011-02-15 13:24:23</date><date type="whenModified">2016-05-19 01:09:23</date><date type="whenReleased">2011-02-15 14:06:59</date><date type="whenProduced">2010-02</date><date type="whenEndEmbargoed">2011-02-15</date><ref type="file" target="https://hal.inria.fr/inria-00566166/document"><date notBefore="2011-02-15"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00566166/file/RR-7543.pdf"><date notBefore="2011-02-15"></date></ref></edition><respStmt><resp>contributor</resp><name key="121135"><persName><forename>Mauricio</forename><surname>Cerda</surname></persName><email>cerdavim@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00566166</idno><idno type="halUri">https://hal.inria.fr/inria-00566166</idno><idno type="halBibtex">cerda:inria-00566166</idno><idno type="halRefHtml">[Research Report] RR-7543, INRIA. 2010</idno><idno type="halRef">[Research Report] RR-7543, INRIA. 2010</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-RRRT">Rapports de recherche et Technique de l'Inria</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</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="0">Not set</note><note type="report" n="6">Research Report</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Spatiotemporal pattern coding using Neural Fields: Optimal parameter estimation</title><author role="aut"><persName><forename type="first">Mauricio</forename><surname>Cerda</surname></persName><email>cerdavim@loria.fr</email><idno type="halAuthorId">361392</idno><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Bernard</forename><surname>Girau</surname></persName><email>Bernard.Girau@loria.fr</email><idno type="halAuthorId">579887</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2348"></affiliation></author></analytic><monogr><idno type="reportNumber">RR-7543</idno><imprint><date type="datePub">2010-02</date></imprint><authority type="institution">INRIA</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="it">neural fields</term><term xml:lang="it">pattern classification</term><term xml:lang="it">dynamical systems</term><term xml:lang="it">computational neuroscience</term><term xml:lang="it">spatiotemporal sequences</term></keywords><classCode scheme="acm" n="I.5.1.3"></classCode><classCode scheme="acm" n="I.2.6.2"></classCode><classCode scheme="acm" n="I.2.10"></classCode><classCode scheme="acm" n="I.2.10.6"></classCode><classCode scheme="acm" n="I.2.10.9"></classCode><classCode scheme="halDomain" n="info.info-ne">Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</classCode><classCode scheme="halTypology" n="REPORT">Reports</classCode></textClass><abstract xml:lang="en">In this work we present the parameter optimization of a distributed model for the classification of temporal sequences. The model we present codes information with a population of units, and it can be applied to discriminate between several spatiotemporal sequences. The implementation of this classification technique strongly depends on how the the connection weights are set according to the sequence we want to code. This ``learning'' phase depends on several parameters, for which we present a detailed analysis in this work. In the first part, we give some examples and we retrieve the model parameters in 1D by extending other existing studies. Later on we extend the analysis into 2D, proposing at the end of the report a strategy to code any given spatiotemporal sequence that can be described as a set local spatiotemporal trajectories. The derivation of parameters is performed analytically and numerical simulations are performed to verify our results.</abstract><abstract xml:lang="fr">Dans ce rapport nous présentons l'optimisation du paramétrage d'un modèle distribué de discrimination de séquences temporelles. Le modèle que nous présentons code l'information au moyen d'une population d'unités, et il peut être appliqué à la discrimination de plusieurs séquences spatio-temporelles. L'implantation de cette technique de discrimination dépend fortement de la façon dont les poids de connexion sont fixés en fonction de la séquence que nous voulons coder. Cette phase d'apprentissage dépend de plusieurs paramètres, pour lesquels ce rapport présente une analyse détaillée. Dans la première partie, nous donnons quelques exemples et nous montrons comment déduire les paramètres du modèle dans sa forme 1D à partir d'autres travaux existants. Ensuite nous étendons l'analyse au cas 2D, en proposant à la fin du rapport une stratégie pour coder n'importe quelle séquence temporelle pouvant être décrite comme un ensemble de trajectoires spatio-temporelles locales. Les paramètres sont déterminés analytiquement et des simultations numériques sont réalisées pour vérifier nos résultats.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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