Competition-Based Model of Pheromone Component Ratio Detection in the Moth
Identifieur interne : 000086 ( Pmc/Curation ); précédent : 000085; suivant : 000087Competition-Based Model of Pheromone Component Ratio Detection in the Moth
Auteurs : Andrei Zavada [Royaume-Uni] ; Christopher L. Buckley [Royaume-Uni] ; Dominique Martinez [France] ; Jean-Pierre Rospars [France] ; Thomas Nowotny [Royaume-Uni]Source :
- PLoS ONE [ 1932-6203 ] ; 2011.
Abstract
For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy.
Url:
DOI: 10.1371/journal.pone.0016308
PubMed: 21373177
PubMed Central: 3040183
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Buckley</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Informatics, University of Sussex, Brighton, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Informatics, University of Sussex, Brighton</wicri:regionArea></affiliation></author><author><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>CORTEX Team - LORIA, Vandoeuvre-lès-Nancy, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>CORTEX Team - LORIA, Vandoeuvre-lès-Nancy</wicri:regionArea></affiliation></author><author><name sortKey="Rospars, Jean Pierre" sort="Rospars, Jean Pierre" uniqKey="Rospars J" first="Jean-Pierre" last="Rospars">Jean-Pierre Rospars</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>UMR 1272 Physiologie de l'insecte, INRA, Versailles, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>UMR 1272 Physiologie de l'insecte, INRA, Versailles</wicri:regionArea></affiliation></author><author><name sortKey="Nowotny, Thomas" sort="Nowotny, Thomas" uniqKey="Nowotny T" first="Thomas" last="Nowotny">Thomas Nowotny</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Informatics, University of Sussex, Brighton, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Informatics, University of Sussex, Brighton</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Ljungberg, H" uniqKey="Ljungberg H">H Ljungberg</name></author><author><name sortKey="Anderson, P" uniqKey="Anderson P">P Anderson</name></author><author><name sortKey="Hansson, Bs" uniqKey="Hansson B">BS Hansson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Anton, S" uniqKey="Anton S">S Anton</name></author><author><name sortKey="Hansson, Bs" uniqKey="Hansson B">BS Hansson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tumlinson, Jh" uniqKey="Tumlinson J">JH Tumlinson</name></author><author><name sortKey="Brennan, Mm" uniqKey="Brennan M">MM Brennan</name></author><author><name sortKey="Doolittle, Re" uniqKey="Doolittle R">RE Doolittle</name></author><author><name sortKey="Mitchell, Er" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21373177</article-id><article-id pub-id-type="pmc">3040183</article-id><article-id pub-id-type="publisher-id">PONE-D-10-02316</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0016308</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology</subject><subj-group><subject>Computational Biology</subject><subj-group><subject>Computational Neuroscience</subject><subj-group><subject>Sensory Systems</subject></subj-group></subj-group><subj-group><subject>Signaling Networks</subject></subj-group></subj-group><subj-group><subject>Neuroscience</subject><subj-group><subject>Sensory Systems</subject><subj-group><subject>Olfactory System</subject></subj-group></subj-group><subj-group><subject>Neural Networks</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Competition-Based Model of Pheromone Component Ratio Detection in the Moth</article-title><alt-title alt-title-type="running-head">Competition Model of Pheromone Ratio Detection</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Zavada</surname><given-names>Andrei</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Buckley</surname><given-names>Christopher L.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Martinez</surname><given-names>Dominique</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Rospars</surname><given-names>Jean-Pierre</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Nowotny</surname><given-names>Thomas</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Informatics, University of Sussex, Brighton, United Kingdom</addr-line></aff><aff id="aff2"><label>2</label><addr-line>CORTEX Team - LORIA, Vandoeuvre-lès-Nancy, France</addr-line></aff><aff id="aff3"><label>3</label><addr-line>UMR 1272 Physiologie de l'insecte, INRA, Versailles, France</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Brezina</surname><given-names>Vladimir</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">Mount Sinai School of Medicine, United States of America</aff><author-notes><corresp id="cor1">* E-mail: <email>t.nowotny@sussex.ac.uk</email></corresp><fn fn-type="con"><p>Conceived and designed the experiments: AZ TN. Performed the experiments: AZ. Analyzed the data: AZ TN. Contributed reagents/materials/analysis tools: AZ TN. Wrote the paper: AZ TN. Designed software: AZ TN. Essential advice DM JR. Mathematical proofs: CLB.</p></fn></author-notes><pub-date pub-type="collection"><year>2011</year></pub-date><pub-date pub-type="epub"><day>16</day><month>2</month><year>2011</year></pub-date><pub-date pub-type="ecorrected"><day>22
</day><month>2</month><year>2011</year></pub-date><volume>6</volume><issue>2</issue><elocation-id>e16308</elocation-id><history><date date-type="received"><day>20</day><month>9</month><year>2010</year></date><date date-type="accepted"><day>22</day><month>12</month><year>2010</year></date></history><permissions><copyright-statement>Zavada et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</copyright-statement></permissions><abstract><p>For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy.</p></abstract><counts><page-count count="12"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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