Reactive searching and infotaxis in odor source localization.
Identifieur interne : 000066 ( PubMed/Curation ); précédent : 000065; suivant : 000067Reactive searching and infotaxis in odor source localization.
Auteurs : Nicole Voges [France] ; Antoine Chaffiol [France] ; Philippe Lucas [France] ; Dominique Martinez [France]Source :
- PLoS computational biology [ 1553-7358 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical : Pheromones.
- analysis : Odors.
- Algorithms, Animals, Appetitive Behavior, Artificial Intelligence, Computational Biology, Female, Flight, Animal, Male, Models, Biological, Moths, Robotics.
Abstract
Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.
DOI: 10.1371/journal.pcbi.1003861
PubMed: 25330317
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uniqKey="Lucas P" first="Philippe" last="Lucas">Philippe Lucas</name><affiliation wicri:level="1"><nlm:affiliation>INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles</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:affiliation>CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="doi">10.1371/journal.pcbi.1003861</idno><idno 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last="Chaffiol">Antoine Chaffiol</name><affiliation wicri:level="1"><nlm:affiliation>Inserm, UMR S968, Institut de la Vision, Paris, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Inserm, UMR S968, Institut de la Vision, Paris</wicri:regionArea></affiliation></author><author><name sortKey="Lucas, Philippe" sort="Lucas, Philippe" uniqKey="Lucas P" first="Philippe" last="Lucas">Philippe Lucas</name><affiliation wicri:level="1"><nlm:affiliation>INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles</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:affiliation>CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Appetitive Behavior</term><term>Artificial Intelligence</term><term>Computational Biology</term><term>Female</term><term>Flight, Animal</term><term>Male</term><term>Models, Biological</term><term>Moths</term><term>Odors (analysis)</term><term>Pheromones</term><term>Robotics</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Animaux</term><term>Biologie informatique</term><term>Comportement appétitif</term><term>Femelle</term><term>Intelligence artificielle</term><term>Modèles biologiques</term><term>Mâle</term><term>Papillons de nuit</term><term>Phéromones</term><term>Robotique</term><term>Vol animal</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Pheromones</term></keywords><keywords scheme="MESH" qualifier="analysis" xml:lang="en"><term>Odors</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Appetitive Behavior</term><term>Artificial Intelligence</term><term>Computational Biology</term><term>Female</term><term>Flight, Animal</term><term>Male</term><term>Models, Biological</term><term>Moths</term><term>Robotics</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Animaux</term><term>Biologie informatique</term><term>Comportement appétitif</term><term>Femelle</term><term>Intelligence artificielle</term><term>Modèles biologiques</term><term>Mâle</term><term>Papillons de nuit</term><term>Phéromones</term><term>Robotique</term><term>Vol animal</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">25330317</PMID><DateCreated><Year>2014</Year><Month>10</Month><Day>21</Day></DateCreated><DateCompleted><Year>2015</Year><Month>11</Month><Day>16</Day></DateCompleted><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>10</Issue><PubDate><Year>2014</Year><Month>Oct</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>Reactive searching and infotaxis in odor source localization.</ArticleTitle><Pagination><MedlinePgn>e1003861</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1003861</ELocationID><Abstract><AbstractText>Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Voges</LastName><ForeName>Nicole</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chaffiol</LastName><ForeName>Antoine</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Inserm, UMR S968, Institut de la Vision, Paris, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lucas</LastName><ForeName>Philippe</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Martinez</LastName><ForeName>Dominique</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>CNRS, 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RefType="Cites"><RefSource>J Vis Exp. 2014;(90):e51704</RefSource><PMID Version="1">25145980</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Comp Physiol A. 1991 Oct;169(4):427-40</RefSource><PMID Version="1">1779417</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 1996 May;75(5):1806-14</RefSource><PMID Version="1">8734581</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Physiol. 1959 Oct;148:574-91</RefSource><PMID Version="1">14403679</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Eur J Neurosci. 2005 Dec;22(12):3147-60</RefSource><PMID Version="1">16367781</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nature. 2007 Jan 25;445(7126):406-9</RefSource><PMID Version="1">17251974</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Chem Ecol. 2008 Jul;34(7):854-66</RefSource><PMID Version="1">18581182</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nature. 2008 Jul 31;454(7204):600-6</RefSource><PMID Version="1">18615015</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Exp Biol. 2009 Apr;212(Pt 8):1191-201</RefSource><PMID Version="1">19329752</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biol. 2009;8(2):21</RefSource><PMID Version="1">19232128</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Comp Neurol. 2010 Feb 1;518(3):366-88</RefSource><PMID Version="1">19950256</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Chem Senses. 2010 Oct;35(8):705-15</RefSource><PMID Version="1">20601375</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biosystems. 2011 Mar;103(3):348-54</RefSource><PMID Version="1">21078362</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 2011 Feb;105(2):834-45</RefSource><PMID Version="1">21160009</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2012 Apr;198(4):295-307</RefSource><PMID Version="1">22227850</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Exp Biol. 2012 May 15;215(Pt 10):1670-80</RefSource><PMID Version="1">22539734</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinspir Biomim. 2013 Mar;8(1):016008</RefSource><PMID Version="1">23385386</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2013;8(4):e61220</RefSource><PMID Version="1">23613816</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2013 Jul 9;110(28):11261-6</RefSource><PMID Version="1">23803855</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2013 Nov;199(11):1037-52</RefSource><PMID Version="1">23749329</PMID></CommentsCorrections><CommentsCorrections RefType="ErratumIn"><RefSource>PLoS Comput Biol. 2014 Nov;10(11):e1004019</RefSource></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001070">Appetitive Behavior</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001185">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019295">Computational Biology</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005260">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005426">Flight, 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