High-capacity embedding of synfire chains in a cortical network model
Identifieur interne : 000147 ( Ncbi/Merge ); précédent : 000146; suivant : 000148High-capacity embedding of synfire chains in a cortical network model
Auteurs : Chris Trengove [Belgique] ; Cees Van Leeuwen [Belgique] ; Markus Diesmann [Allemagne]Source :
- Journal of Computational Neuroscience [ 0929-5313 ] ; 2012.
Abstract
Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.
Url:
DOI: 10.1007/s10827-012-0413-9
PubMed: 22878688
PubMed Central: 3605496
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Trengove</name><affiliation><nlm:aff id="Aff1">Integrated Simulation of Living Matter Group, RIKEN, Computational Science Research Program, Wako, Saitama Japan</nlm:aff><wicri:noCountry code="subfield">Saitama Japan</wicri:noCountry></affiliation><affiliation><nlm:aff id="Aff2">Laboratory for Perceptual Dynamics, RIKEN, Brain Science Institute, Wako, Saitama Japan</nlm:aff><wicri:noCountry code="subfield">Saitama Japan</wicri:noCountry></affiliation><affiliation><nlm:aff id="Aff3">Laboratory for Computational Neurophysics, RIKEN, Brain Science Institute, Wako, Saitama Japan</nlm:aff><wicri:noCountry code="subfield">Saitama Japan</wicri:noCountry></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff4">Perceptual Dynamics Laboratory, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium</nlm:aff><country xml:lang="fr">Belgique</country><wicri:regionArea>Perceptual Dynamics Laboratory, University of Leuven, Tiensestraat 102, 3000, 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when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Abeles, M" uniqKey="Abeles M">M Abeles</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Abeles, M" uniqKey="Abeles M">M Abeles</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Abeles, M" uniqKey="Abeles M">M Abeles</name></author><author><name sortKey="Hayon, G" uniqKey="Hayon G">G Hayon</name></author><author><name sortKey="Lehmann, D" uniqKey="Lehmann D">D Lehmann</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Amit, Dj" uniqKey="Amit D">DJ Amit</name></author><author><name sortKey="Brunel, N" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Comput Neurosci</journal-id><journal-id journal-id-type="iso-abbrev">J Comput Neurosci</journal-id><journal-title-group><journal-title>Journal of Computational Neuroscience</journal-title></journal-title-group><issn pub-type="ppub">0929-5313</issn><issn pub-type="epub">1573-6873</issn><publisher><publisher-name>Springer US</publisher-name><publisher-loc>Boston</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22878688</article-id><article-id pub-id-type="pmc">3605496</article-id><article-id pub-id-type="publisher-id">413</article-id><article-id pub-id-type="doi">10.1007/s10827-012-0413-9</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>High-capacity embedding of synfire chains in a cortical network model</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Trengove</surname><given-names>Chris</given-names></name><address><phone>+81-48-467-9644</phone><fax>+81-48-467-9670</fax><email>trengove.c@gmail.com</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref><xref ref-type="aff" rid="Aff3"></xref><xref ref-type="aff" rid="Aff4"></xref></contrib><contrib contrib-type="author"><name><surname>van Leeuwen</surname><given-names>Cees</given-names></name><xref ref-type="aff" rid="Aff2"></xref><xref ref-type="aff" rid="Aff4"></xref></contrib><contrib contrib-type="author"><name><surname>Diesmann</surname><given-names>Markus</given-names></name><xref ref-type="aff" rid="Aff3"></xref><xref ref-type="aff" rid="Aff5"></xref></contrib><aff id="Aff1"><label></label>Integrated Simulation of Living Matter Group, RIKEN, Computational Science Research Program, Wako, Saitama Japan</aff><aff id="Aff2"><label></label>Laboratory for Perceptual Dynamics, RIKEN, Brain Science Institute, Wako, Saitama Japan</aff><aff id="Aff3"><label></label>Laboratory for Computational Neurophysics, RIKEN, Brain Science Institute, Wako, Saitama Japan</aff><aff id="Aff4"><label></label>Perceptual Dynamics Laboratory, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium</aff><aff id="Aff5"><label></label>Institute of Neuroscience and Medicine, Computational and Systems Neuroscience (INM-6), Research Center Juelich, Juelich, Germany</aff></contrib-group><author-notes><fn fn-type="com"><p><bold>Action Editor: Stefano Fusi</bold></p></fn></author-notes><pub-date pub-type="epub"><day>11</day><month>8</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>11</day><month>8</month><year>2012</year></pub-date><pub-date pub-type="ppub"><month>4</month><year>2013</year></pub-date><volume>34</volume><issue>2</issue><fpage>185</fpage><lpage>209</lpage><history><date date-type="received"><day>8</day><month>11</month><year>2011</year></date><date date-type="rev-recd"><day>18</day><month>4</month><year>2012</year></date><date date-type="accepted"><day>2</day><month>7</month><year>2012</year></date></history><permissions><copyright-statement>© The Author(s) 2012</copyright-statement></permissions><abstract id="Abs1"><p>Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Recurrent network dynamics</kwd><kwd>Feedforward network</kwd><kwd>Synchrony</kwd><kwd>Synaptic conductance</kwd><kwd>Synfire chain</kwd><kwd>Storage capacity</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Springer Science+Business Media New York 2013</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list><country><li>Allemagne</li><li>Belgique</li></country></list><tree><country name="Belgique"><noRegion><name sortKey="Trengove, Chris" sort="Trengove, Chris" uniqKey="Trengove C" first="Chris" last="Trengove">Chris Trengove</name></noRegion><name sortKey="Van Leeuwen, Cees" sort="Van Leeuwen, Cees" uniqKey="Van Leeuwen C" first="Cees" last="Van Leeuwen">Cees Van Leeuwen</name></country><country name="Allemagne"><noRegion><name sortKey="Diesmann, Markus" sort="Diesmann, Markus" uniqKey="Diesmann M" first="Markus" last="Diesmann">Markus Diesmann</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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