Spatiotemporal pattern coding using Neural Fields: Optimal parameter estimation
Identifieur interne : 002E33 ( Main/Exploration ); précédent : 002E32; suivant : 002E34Spatiotemporal 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.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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Le document en format XML
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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><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Cerda, Mauricio" sort="Cerda, Mauricio" uniqKey="Cerda M" first="Mauricio" last="Cerda">Mauricio Cerda</name></region><name sortKey="Girau, Bernard" sort="Girau, Bernard" uniqKey="Girau B" first="Bernard" last="Girau">Bernard Girau</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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