Randomly spiking dynamic neural fields
Identifieur interne : 003F40 ( Hal/Corpus ); précédent : 003F39; suivant : 003F41Randomly spiking dynamic neural fields
Auteurs : Benoît Chappet De Vangel ; Cesar Torres-Huitzil ; Bernard GirauSource :
Abstract
Bio-inspired neural computation attracts a lot of attention as a possible solution for the future challenges in designing computational resources. Dynamic neural fields (DNF) provide cortically inspired models of neural populations which computation can be applied to a wide variety of tasks, such as perception and sensorimotor control. DNFs are often derived from the continuous neural field theory (CNFT). In spite of the parallel structure and regularity of CNFT models, few studies of hardware implementations have been carried out targeting embedded real-time processing. In this paper, a hardware-friendly model adapted from the CNFT is introduced, namely the RSDNF model (randomly spiking dynamic neural fields). Thanks to their simplified 2D structure, RSDNFs achieve scalable parallel implementations on digital hardware while maintaining the behavioral properties of CNFT models. Spike-based computations within neurons in the field are introduced to reduce inter-neuron connection bandwidth. Additionally, local stochastic spike propagation ensures inhibition and excitation broadcast without a fully connected network. The behavioral soundness and robustness of the model in the presence of noise and distracters is fully validated through software and hardware. A field programmable gate array (FPGA) implementation shows how the RSDNF model ensures a level of density and scalability out of reach for previous hardware implementations of dynamic neural field models.
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Hal:hal-01071862Le document en format XML
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Dynamic neural fields (DNF) provide cortically inspired models of neural populations which computation can be applied to a wide variety of tasks, such as perception and sensorimotor control. DNFs are often derived from the continuous neural field theory (CNFT). In spite of the parallel structure and regularity of CNFT models, few studies of hardware implementations have been carried out targeting embedded real-time processing. In this paper, a hardware-friendly model adapted from the CNFT is introduced, namely the RSDNF model (randomly spiking dynamic neural fields). Thanks to their simplified 2D structure, RSDNFs achieve scalable parallel implementations on digital hardware while maintaining the behavioral properties of CNFT models. Spike-based computations within neurons in the field are introduced to reduce inter-neuron connection bandwidth. Additionally, local stochastic spike propagation ensures inhibition and excitation broadcast without a fully connected network. The behavioral soundness and robustness of the model in the presence of noise and distracters is fully validated through software and hardware. A field programmable gate array (FPGA) implementation shows how the RSDNF model ensures a level of density and scalability out of reach for previous hardware implementations of dynamic neural field models.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Randomly spiking dynamic neural fields</title><author role="aut"><persName><forename type="first">Benoît</forename><surname>Chappet De Vangel</surname></persName><email></email><idno type="idhal">benoit-chappet-de-vangel</idno><idno type="halauthor">1081107</idno><idno type="ORCID">http://orcid.org/0000-0003-4807-0430</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-205107"></affiliation></author><author role="aut"><persName><forename type="first">Cesar</forename><surname>Torres-Huitzil</surname></persName><email>ctorres@tamps.cinvestav.mx</email><idno type="halauthor">1081108</idno><orgName ref="#struct-301749"></orgName><affiliation ref="#struct-104510"></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-205107"></affiliation></author><editor role="depositor"><persName><forename>Bernard</forename><surname>Girau</surname></persName><email>girau@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-10-06 19:36:06</date><date type="whenModified">2015-09-21 11:13:04</date><date type="whenReleased">2014-10-06 19:36:06</date><date type="whenProduced">2014</date></edition><respStmt><resp>contributor</resp><name key="103176"><persName><forename>Bernard</forename><surname>Girau</surname></persName><email>girau@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01071862</idno><idno type="halUri">https://hal.inria.fr/hal-01071862</idno><idno type="halBibtex">chappetdevangel:hal-01071862</idno><idno type="halRefHtml">Journal of Emerging Technologies in Computing Systems, ACM, 2014</idno><idno type="halRef">Journal of Emerging Technologies in Computing Systems, ACM, 2014</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="INRIA-LORRAINE">INRIA Nancy - Grand Est</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="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Randomly spiking dynamic neural fields</title><author role="aut"><persName><forename type="first">Benoît</forename><surname>Chappet De Vangel</surname></persName><idno type="idHal">benoit-chappet-de-vangel</idno><idno type="halAuthorId">1081107</idno><idno type="ORCID">http://orcid.org/0000-0003-4807-0430</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-205107"></affiliation></author><author role="aut"><persName><forename type="first">Cesar</forename><surname>Torres-Huitzil</surname></persName><email>ctorres@tamps.cinvestav.mx</email><idno type="halAuthorId">1081108</idno><orgName ref="#struct-301749"></orgName><affiliation ref="#struct-104510"></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-205107"></affiliation></author></analytic><monogr><idno type="halJournalId" status="INCOMING">97237</idno><title level="j">Journal of Emerging Technologies in Computing Systems</title><imprint><publisher>ACM</publisher><date type="datePub">2014</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-ne">Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Bio-inspired neural computation attracts a lot of attention as a possible solution for the future challenges in designing computational resources. Dynamic neural fields (DNF) provide cortically inspired models of neural populations which computation can be applied to a wide variety of tasks, such as perception and sensorimotor control. DNFs are often derived from the continuous neural field theory (CNFT). In spite of the parallel structure and regularity of CNFT models, few studies of hardware implementations have been carried out targeting embedded real-time processing. In this paper, a hardware-friendly model adapted from the CNFT is introduced, namely the RSDNF model (randomly spiking dynamic neural fields). Thanks to their simplified 2D structure, RSDNFs achieve scalable parallel implementations on digital hardware while maintaining the behavioral properties of CNFT models. Spike-based computations within neurons in the field are introduced to reduce inter-neuron connection bandwidth. Additionally, local stochastic spike propagation ensures inhibition and excitation broadcast without a fully connected network. The behavioral soundness and robustness of the model in the presence of noise and distracters is fully validated through software and hardware. A field programmable gate array (FPGA) implementation shows how the RSDNF model ensures a level of density and scalability out of reach for previous hardware implementations of dynamic neural field models.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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