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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Self-organizing neural maps for multi-modal associations</title><author><name sortKey="Lefort, Mathieu" sort="Lefort, Mathieu" uniqKey="Lefort M" first="Mathieu" last="Lefort">Mathieu Lefort</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Boniface, Yann" sort="Boniface, Yann" uniqKey="Boniface Y" first="Yann" last="Boniface">Yann Boniface</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Girau, Bernard" sort="Girau, Bernard" uniqKey="Girau B" first="Bernard" last="Girau">Bernard Girau</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmc">3240218</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3240218</idno><idno type="RBID">PMC:3240218</idno><idno type="doi">10.1186/1471-2202-12-S1-P125</idno><idno type="pmid">NONE</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">000034</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000034</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Self-organizing neural maps for multi-modal associations</title><author><name sortKey="Lefort, Mathieu" sort="Lefort, Mathieu" uniqKey="Lefort M" first="Mathieu" last="Lefort">Mathieu Lefort</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Boniface, Yann" sort="Boniface, Yann" uniqKey="Boniface Y" first="Yann" last="Boniface">Yann Boniface</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Girau, Bernard" sort="Girau, Bernard" uniqKey="Girau B" first="Bernard" last="Girau">Bernard Girau</name><affiliation><nlm:aff id="I1">LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</nlm:aff></affiliation></author></analytic><series><title level="j">BMC Neuroscience</title><idno type="eISSN">1471-2202</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="O Egan, Jk" uniqKey="O Egan J">JK O’Regan</name></author><author><name sortKey="Noe, A" uniqKey="Noe A">A Noë</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mcgurk, H" uniqKey="Mcgurk H">H Mcgurk</name></author><author><name sortKey="Macdonald, J" uniqKey="Macdonald J">J Macdonald</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bonath, B" uniqKey="Bonath B">B Bonath</name></author><author><name sortKey="Noesselt, T" uniqKey="Noesselt T">T Noesselt</name></author><author><name sortKey="Martinez, A" uniqKey="Martinez A">A Martinez</name></author><author><name sortKey="Mishra, J" uniqKey="Mishra J">J Mishra</name></author><author><name sortKey="Schwiecker, K" uniqKey="Schwiecker K">K Schwiecker</name></author><author><name sortKey="Heinze, Hj" uniqKey="Heinze H">HJ Heinze</name></author><author><name sortKey="Hillyard, Sa" uniqKey="Hillyard S">SA Hillyard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bienenstock, El" uniqKey="Bienenstock E">EL Bienenstock</name></author><author><name sortKey="Cooper, Ln" uniqKey="Cooper L">LN Cooper</name></author><author><name sortKey="Munro, Pw" uniqKey="Munro P">PW Munro</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bear, Mf" uniqKey="Bear M">MF Bear</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Amari, S" uniqKey="Amari S">S Amari</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="abstract"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Neurosci</journal-id><journal-title-group><journal-title>BMC Neuroscience</journal-title></journal-title-group><issn pub-type="epub">1471-2202</issn><publisher><publisher-name>BioMed Central</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">3240218</article-id><article-id pub-id-type="publisher-id">1471-2202-12-S1-P125</article-id><article-id pub-id-type="doi">10.1186/1471-2202-12-S1-P125</article-id><article-categories><subj-group subj-group-type="heading"><subject>Poster Presentation</subject></subj-group></article-categories><title-group><article-title>Self-organizing neural maps for multi-modal associations</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Lefort</surname><given-names>Mathieu</given-names></name><xref ref-type="aff" rid="I1">1</xref><email>mathieu.lefort@loria.fr</email></contrib><contrib contrib-type="author" id="A2"><name><surname>Boniface</surname><given-names>Yann</given-names></name><xref ref-type="aff" rid="I1">1</xref></contrib><contrib contrib-type="author" id="A3"><name><surname>Girau</surname><given-names>Bernard</given-names></name><xref ref-type="aff" rid="I1">1</xref></contrib></contrib-group><aff id="I1"><label>1</label>LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France</aff><pub-date pub-type="collection"><year>2011</year></pub-date><pub-date pub-type="epub"><day>18</day><month>7</month><year>2011</year></pub-date><volume>12</volume><issue>Suppl 1</issue><supplement><named-content content-type="supplement-title">Twentieth Annual Computational Neuroscience Meeting: CNS*2011</named-content><named-content content-type="supplement-editor">Jean-Marc Fellous and Astrid Prinz</named-content><named-content content-type="supplement-sponsor">Publication of this supplement has been supported by Neuralynx, Springer, INCF and the Bernstein Network Computational Neuroscience</named-content><ext-link ext-link-type="uri" xlink:href="http://www.biomedcentral.com/content/pdf/1471-2202-12-S1-info.pdf">http://www.biomedcentral.com/content/pdf/1471-2202-12-S1-info.pdf</ext-link></supplement><fpage>P125</fpage><lpage>P125</lpage><permissions><copyright-statement>Copyright ©2011 Lefort et al; licensee BioMed Central Ltd.</copyright-statement><copyright-year>2011</copyright-year><copyright-holder>Lefort et al; licensee BioMed Central Ltd.</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:href="http://www.biomedcentral.com/1471-2202/12/S1/P125"></self-uri><conference><conf-date>23-28 July 2011</conf-date><conf-name>Twentieth Annual Computational Neuroscience Meeting: CNS*2011</conf-name><conf-loc>Stockholm, Sweden</conf-loc></conference></article-meta></front><body><sec><title></title><p>To move in a complex and dynamic environment, according to the active perception theory, an agent needs to learn the multi modal correlations between its actions and the changes they induce in the environment [<xref ref-type="bibr" rid="B1">1</xref>]. Human beings interact with their environment through several distant organs, whose sensory flow processing are influencing each others as, for example, in the Mc Gurk effect [<xref ref-type="bibr" rid="B2">2</xref>]. In the functional view of the cortex, each sensory area processes a specific sensory flow and associative areas integrate these flows in a consistent representation of the world that influences in return uni-modal perceptions as for the ventriloquist effect [<xref ref-type="bibr" rid="B3">3</xref>]. At a mesoscopic level, the cortex shows a generic structure composed of multi-layer cortical columns. We propose a bio inspired model of perceptive map which, using a continuous and unsupervised learning, self-organizes to map a sensory data flow. Thus, using a spatial competition mechanism, a perceptive map provides an activity bump representing the current perception. In a multi modal architecture, by connecting multiple perceptive maps to an associative map with reciprocal spatially constrained connectivity, the spatial organization of all perceptions have to relax the multi modal constraints (see figure <xref ref-type="fig" rid="F1">1A</xref>). Thus, this multimodal architecture provides a way to learn multi modal correlations with generalization.</p><fig id="F1" position="float"><label>Figure 1</label><caption><p><bold>A</bold> Example of use of our perceptive map model in a multi modal architecture with two sensors and one actuator. <bold>B</bold> Generic structure of one cortical column in a perceptive map.</p></caption><graphic xlink:href="1471-2202-12-S1-P125-1"></graphic></fig><p>More precisely, each perceptive map consists of generic cortical columns with computing and learning which are local and decentralized (see figure<xref ref-type="fig" rid="F1">1 B</xref>). Sensitive layer receives the feed-forward flow coming from a sensor and uses the BCM learning rule [<xref ref-type="bibr" rid="B4">4</xref>]. This synaptic learning rule is based on the hebbian one and is able to autonomously raise a selectivity to one stimulus of the upcoming flow. From a computational point of view, the BCM rule uses a sliding threshold between long term potentiation and long term depression, which has been confirmed by biological evidences [<xref ref-type="bibr" rid="B5">5</xref>]. The cortical layer receives feedback influence coming from the associative map, whose activity represents the multi modal perception. The perceptive layer is based on the neural field theory [<xref ref-type="bibr" rid="B6">6</xref>] and it receives the sensitive activity modulated by the cortical one. Thanks to the competitive mechanism introduced by the lateral connectivity with a difference of Gaussian shape, an activity bump emerges at the map level, where the activity is the most spatially consistent, representing a consensus between the local sensation and the multi modal constraints.</p><p>To obtain a self-organization of the sensitive layer at the map level, the perceptive layer modulates the BCM activity, so that the spatial consistency of the activity bump is propagated to the selectivity organization. We have also introduced an unlearning term in the BCM learning rule in order to forget the current selectivity if it is not consistent with the received modulation. This unlearning mechanism provides plasticity to the self-organization in order to adapt to the multi modal constraints.</p></sec></body><back><ref-list><ref id="B1"><mixed-citation publication-type="journal"><name><surname>O’Regan</surname><given-names>JK</given-names></name><name><surname>Noë</surname><given-names>A</given-names></name><article-title>A sensorimotor account of vision and visual consciousness</article-title><source>Behavioral and brain sciences</source><year>2001</year><volume>24</volume><issue>05</issue><fpage>939</fpage><lpage>973</lpage><pub-id pub-id-type="doi">10.1017/S0140525X01000115</pub-id></mixed-citation></ref><ref id="B2"><mixed-citation publication-type="journal"><name><surname>Mcgurk</surname><given-names>H</given-names></name><name><surname>Macdonald</surname><given-names>J</given-names></name><article-title>Hearing lips and seeing voices</article-title><source>Nature</source><year>1976</year><volume>264</volume><issue>5588</issue><fpage>746</fpage><lpage>748</lpage><pub-id pub-id-type="doi">10.1038/264746a0</pub-id><pub-id pub-id-type="pmid">1012311</pub-id></mixed-citation></ref><ref id="B3"><mixed-citation publication-type="journal"><name><surname>Bonath</surname><given-names>B</given-names></name><name><surname>Noesselt</surname><given-names>T</given-names></name><name><surname>Martinez</surname><given-names>A</given-names></name><name><surname>Mishra</surname><given-names>J</given-names></name><name><surname>Schwiecker</surname><given-names>K</given-names></name><name><surname>Heinze</surname><given-names>HJ</given-names></name><name><surname>Hillyard</surname><given-names>SA</given-names></name><article-title>Neural basis of the ventriloquist illusion</article-title><source>Current Biology</source><year>2007</year><volume>17</volume><issue>19</issue><fpage>1697</fpage><lpage>1703</lpage><pub-id pub-id-type="doi">10.1016/j.cub.2007.08.050</pub-id><pub-id pub-id-type="pmid">17884498</pub-id></mixed-citation></ref><ref id="B4"><mixed-citation publication-type="journal"><name><surname>Bienenstock</surname><given-names>EL</given-names></name><name><surname>Cooper</surname><given-names>LN</given-names></name><name><surname>Munro</surname><given-names>PW</given-names></name><article-title>Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex</article-title><source>Journal of Neuroscience</source><year>1982</year><volume>2</volume><issue>1</issue><fpage>32</fpage><pub-id pub-id-type="pmid">7054394</pub-id></mixed-citation></ref><ref id="B5"><mixed-citation publication-type="journal"><name><surname>Bear</surname><given-names>MF</given-names></name><article-title>Mechanism for a sliding synaptic modification threshold</article-title><source>Neuron</source><year>1995</year><volume>15</volume><issue>1</issue><fpage>1</fpage><lpage>4</lpage><pub-id pub-id-type="doi">10.1016/0896-6273(95)90056-X</pub-id><pub-id pub-id-type="pmid">7619513</pub-id></mixed-citation></ref><ref id="B6"><mixed-citation publication-type="journal"><name><surname>Amari</surname><given-names>S</given-names></name><article-title>Dynamics of pattern formation in lateral-inhibition type neural fields</article-title><source>Biological Cybernetics</source><year>1977</year><volume>27</volume><issue>2</issue><fpage>77</fpage><lpage>87</lpage><pub-id pub-id-type="doi">10.1007/BF00337259</pub-id><pub-id pub-id-type="pmid">911931</pub-id></mixed-citation></ref></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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