Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components

Autores
Chen, J. Y.; Marachlian, Emiliano; Assisi, C.; Huerta, R.; Smith, B. H.; Locatelli, Fernando Federico; Bazhenov, M.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.
Fil: Chen, J. Y.. University of California; Estados Unidos
Fil: Marachlian, Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Assisi, C.. University of California; Estados Unidos
Fil: Huerta, R.. University of California at San Diego; Estados Unidos
Fil: Smith, B. H.. Arizona State University; Estados Unidos
Fil: Locatelli, Fernando Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina
Fil: Bazhenov, M.. University of California; Estados Unidos
Materia
olfaction
plasticity
coding
imaging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/60637
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Learning Modifies Odor Mixture Processing to Improve Detection of Relevant ComponentsChen, J. Y.Marachlian, EmilianoAssisi, C.Huerta, R.Smith, B. H.Locatelli, Fernando FedericoBazhenov, M.olfactionplasticitycodingimaginghttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.Fil: Chen, J. Y.. University of California; Estados UnidosFil: Marachlian, Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Assisi, C.. University of California; Estados UnidosFil: Huerta, R.. University of California at San Diego; Estados UnidosFil: Smith, B. H.. Arizona State University; Estados UnidosFil: Locatelli, Fernando Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Bazhenov, M.. University of California; Estados UnidosSociety for Neuroscience2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/60637Chen, J. Y.; Marachlian, Emiliano; Assisi, C.; Huerta, R.; Smith, B. H.; et al.; Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components; Society for Neuroscience; Journal of Neuroscience; 35; 1; 1-2015; 179-1970270-6474CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.jneurosci.org/content/35/1/179.shortinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287141/info:eu-repo/semantics/altIdentifier/doi/10.1523%2FJNEUROSCI.2345-14.2015info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:22:52Zoai:ri.conicet.gov.ar:11336/60637instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:22:52.944CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
title Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
spellingShingle Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
Chen, J. Y.
olfaction
plasticity
coding
imaging
title_short Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
title_full Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
title_fullStr Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
title_full_unstemmed Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
title_sort Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components
dc.creator.none.fl_str_mv Chen, J. Y.
Marachlian, Emiliano
Assisi, C.
Huerta, R.
Smith, B. H.
Locatelli, Fernando Federico
Bazhenov, M.
author Chen, J. Y.
author_facet Chen, J. Y.
Marachlian, Emiliano
Assisi, C.
Huerta, R.
Smith, B. H.
Locatelli, Fernando Federico
Bazhenov, M.
author_role author
author2 Marachlian, Emiliano
Assisi, C.
Huerta, R.
Smith, B. H.
Locatelli, Fernando Federico
Bazhenov, M.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv olfaction
plasticity
coding
imaging
topic olfaction
plasticity
coding
imaging
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.
Fil: Chen, J. Y.. University of California; Estados Unidos
Fil: Marachlian, Emiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Assisi, C.. University of California; Estados Unidos
Fil: Huerta, R.. University of California at San Diego; Estados Unidos
Fil: Smith, B. H.. Arizona State University; Estados Unidos
Fil: Locatelli, Fernando Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina
Fil: Bazhenov, M.. University of California; Estados Unidos
description Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/60637
Chen, J. Y.; Marachlian, Emiliano; Assisi, C.; Huerta, R.; Smith, B. H.; et al.; Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components; Society for Neuroscience; Journal of Neuroscience; 35; 1; 1-2015; 179-197
0270-6474
CONICET Digital
CONICET
url http://hdl.handle.net/11336/60637
identifier_str_mv Chen, J. Y.; Marachlian, Emiliano; Assisi, C.; Huerta, R.; Smith, B. H.; et al.; Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components; Society for Neuroscience; Journal of Neuroscience; 35; 1; 1-2015; 179-197
0270-6474
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.jneurosci.org/content/35/1/179.short
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287141/
info:eu-repo/semantics/altIdentifier/doi/10.1523%2FJNEUROSCI.2345-14.2015
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Society for Neuroscience
publisher.none.fl_str_mv Society for Neuroscience
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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