Stochastic Resonance with Colored Noise for Neural Signal Detection
Identifieur interne : 002A01 ( Pmc/Curation ); précédent : 002A00; suivant : 002A02Stochastic Resonance with Colored Noise for Neural Signal Detection
Auteurs : Fabing Duan [République populaire de Chine] ; François Chapeau-Blondeau [France] ; Derek Abbott [Australie]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity–a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.
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DOI: 10.1371/journal.pone.0091345
PubMed: 24632853
PubMed Central: 3954722
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Stochastic Resonance with Colored Noise for Neural Signal Detection</title><author><name sortKey="Duan, Fabing" sort="Duan, Fabing" uniqKey="Duan F" first="Fabing" last="Duan">Fabing Duan</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Institute of Complexity Science, Qingdao University, Qingdao, P. R. China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Complexity Science, Qingdao University, Qingdao</wicri:regionArea></affiliation></author><author><name sortKey="Chapeau Blondeau, Francois" sort="Chapeau Blondeau, Francois" uniqKey="Chapeau Blondeau F" first="François" last="Chapeau-Blondeau">François Chapeau-Blondeau</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers</wicri:regionArea></affiliation></author><author><name sortKey="Abbott, Derek" sort="Abbott, Derek" uniqKey="Abbott D" first="Derek" last="Abbott">Derek Abbott</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24632853</idno><idno type="pmc">3954722</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954722</idno><idno type="RBID">PMC:3954722</idno><idno type="doi">10.1371/journal.pone.0091345</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">002B51</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002B51</idno><idno type="wicri:Area/Pmc/Curation">002A01</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">002A01</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Stochastic Resonance with Colored Noise for Neural Signal Detection</title><author><name sortKey="Duan, Fabing" sort="Duan, Fabing" uniqKey="Duan F" first="Fabing" last="Duan">Fabing Duan</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Institute of Complexity Science, Qingdao University, Qingdao, P. R. China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Complexity Science, Qingdao University, Qingdao</wicri:regionArea></affiliation></author><author><name sortKey="Chapeau Blondeau, Francois" sort="Chapeau Blondeau, Francois" uniqKey="Chapeau Blondeau F" first="François" last="Chapeau-Blondeau">François Chapeau-Blondeau</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers</wicri:regionArea></affiliation></author><author><name sortKey="Abbott, Derek" sort="Abbott, Derek" uniqKey="Abbott D" first="Derek" last="Abbott">Derek Abbott</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity–a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">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">24632853</article-id><article-id pub-id-type="pmc">3954722</article-id><article-id pub-id-type="publisher-id">PONE-D-13-48229</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0091345</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 and Life Sciences</subject><subj-group><subject>Biophysics</subject><subj-group><subject>Biophysical Simulations</subject><subject>Biophysics Theory</subject></subj-group></subj-group><subj-group><subject>Biotechnology</subject><subj-group><subject>Bioengineering</subject></subj-group></subj-group><subj-group><subject>Computational Biology</subject><subj-group><subject>Computational Neuroscience</subject><subj-group><subject>Single Neuron Function</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Engineering and Technology</subject><subj-group><subject>Control Engineering</subject></subj-group><subj-group><subject>Signal Processing</subject><subj-group><subject>Statistical Signal Processing</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Physical Sciences</subject><subj-group><subject>Mathematics</subject><subj-group><subject>Algebra</subject><subj-group><subject>Linear Algebra</subject></subj-group></subj-group><subj-group><subject>Applied Mathematics</subject></subj-group></subj-group><subj-group><subject>Physics</subject></subj-group></subj-group></article-categories><title-group><article-title>Stochastic Resonance with Colored Noise for Neural Signal Detection</article-title><alt-title alt-title-type="running-head">Stochastic Resonance with Colored Noise</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Duan</surname><given-names>Fabing</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chapeau-Blondeau</surname><given-names>François</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Abbott</surname><given-names>Derek</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Institute of Complexity Science, Qingdao University, Qingdao, P. R. China</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France</addr-line></aff><aff id="aff3"><label>3</label><addr-line>Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia, Australia</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Chacron</surname><given-names>Maurice J.</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>McGill University, Canada</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>fabing.duan@gmail.com</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>DA is a PLOS ONE Editorial Board member, and the authors here confirm that this does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: FCB DA. Performed the experiments: FD. Analyzed the data: FD FCB DA. Contributed reagents/materials/analysis tools: FCB DA. Wrote the paper: FD FCB DA. Proofreading: FCB DA.</p></fn></author-notes><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>14</day><month>3</month><year>2014</year></pub-date><volume>9</volume><issue>3</issue><elocation-id>e91345</elocation-id><history><date date-type="received"><day>16</day><month>11</month><year>2013</year></date><date date-type="accepted"><day>10</day><month>2</month><year>2014</year></date></history><permissions><copyright-year>2014</copyright-year><copyright-holder>Duan et al</copyright-holder><license><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><p>We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity–a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.</p></abstract><funding-group><funding-statement>This work is sponsored by the NSF of Shandong Province (No. ZR2010FM006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="7"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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