Elemental gesture dynamics are encoded by song premotor cortical neurons

Autores
Amador, Ana; Sanz Perl Hernandez, Yonatan; Mindlin, Bernardo Gabriel; Margoliash, Daniel
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantitative biomechanical models can identify control parameters that are used during movements, and movement parameters that are encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics and upper-vocal-tract filtering to the songs of zebra finches. This reduces the dimensionality of singing dynamics, described as trajectories (motor gestures) in a space of syringeal pressure and tension. Here we assess model performance by characterizing the auditory response ‘replay’ of song premotor HVC neurons to the presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed within a few milliseconds of the extreme time points of the gesture trajectories. Thus, the HVC precisely encodes vocal motor output through activity at the times of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons is used as a ‘forward’ model, representing the sequence of gestures in song to make predictions on expected behaviour and evaluate feedback.
Fil: Amador, Ana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Margoliash, Daniel. University of Chicago; Estados Unidos
Materia
Zebra Finch
Sensorimotor
Nonlinear Dynamical Model
Birdsong
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/2478
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Elemental gesture dynamics are encoded by song premotor cortical neuronsAmador, AnaSanz Perl Hernandez, YonatanMindlin, Bernardo GabrielMargoliash, DanielZebra FinchSensorimotorNonlinear Dynamical ModelBirdsonghttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Quantitative biomechanical models can identify control parameters that are used during movements, and movement parameters that are encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics and upper-vocal-tract filtering to the songs of zebra finches. This reduces the dimensionality of singing dynamics, described as trajectories (motor gestures) in a space of syringeal pressure and tension. Here we assess model performance by characterizing the auditory response ‘replay’ of song premotor HVC neurons to the presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed within a few milliseconds of the extreme time points of the gesture trajectories. Thus, the HVC precisely encodes vocal motor output through activity at the times of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons is used as a ‘forward’ model, representing the sequence of gestures in song to make predictions on expected behaviour and evaluate feedback.Fil: Amador, Ana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Margoliash, Daniel. University of Chicago; Estados UnidosNature Publishing Group2013-02-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/2478Amador, Ana; Sanz Perl Hernandez, Yonatan; Mindlin, Bernardo Gabriel; Margoliash, Daniel; Elemental gesture dynamics are encoded by song premotor cortical neurons; Nature Publishing Group; Nature; 495; 7439; 27-2-2013; 59-640028-0836enginfo:eu-repo/semantics/altIdentifier/doi/doi:10.1038/nature11967info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/nature/journal/v495/n7439/full/nature11967.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:19:47Zoai:ri.conicet.gov.ar:11336/2478instacron: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 12:19:48.234CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Elemental gesture dynamics are encoded by song premotor cortical neurons
title Elemental gesture dynamics are encoded by song premotor cortical neurons
spellingShingle Elemental gesture dynamics are encoded by song premotor cortical neurons
Amador, Ana
Zebra Finch
Sensorimotor
Nonlinear Dynamical Model
Birdsong
title_short Elemental gesture dynamics are encoded by song premotor cortical neurons
title_full Elemental gesture dynamics are encoded by song premotor cortical neurons
title_fullStr Elemental gesture dynamics are encoded by song premotor cortical neurons
title_full_unstemmed Elemental gesture dynamics are encoded by song premotor cortical neurons
title_sort Elemental gesture dynamics are encoded by song premotor cortical neurons
dc.creator.none.fl_str_mv Amador, Ana
Sanz Perl Hernandez, Yonatan
Mindlin, Bernardo Gabriel
Margoliash, Daniel
author Amador, Ana
author_facet Amador, Ana
Sanz Perl Hernandez, Yonatan
Mindlin, Bernardo Gabriel
Margoliash, Daniel
author_role author
author2 Sanz Perl Hernandez, Yonatan
Mindlin, Bernardo Gabriel
Margoliash, Daniel
author2_role author
author
author
dc.subject.none.fl_str_mv Zebra Finch
Sensorimotor
Nonlinear Dynamical Model
Birdsong
topic Zebra Finch
Sensorimotor
Nonlinear Dynamical Model
Birdsong
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Quantitative biomechanical models can identify control parameters that are used during movements, and movement parameters that are encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics and upper-vocal-tract filtering to the songs of zebra finches. This reduces the dimensionality of singing dynamics, described as trajectories (motor gestures) in a space of syringeal pressure and tension. Here we assess model performance by characterizing the auditory response ‘replay’ of song premotor HVC neurons to the presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed within a few milliseconds of the extreme time points of the gesture trajectories. Thus, the HVC precisely encodes vocal motor output through activity at the times of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons is used as a ‘forward’ model, representing the sequence of gestures in song to make predictions on expected behaviour and evaluate feedback.
Fil: Amador, Ana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Margoliash, Daniel. University of Chicago; Estados Unidos
description Quantitative biomechanical models can identify control parameters that are used during movements, and movement parameters that are encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics and upper-vocal-tract filtering to the songs of zebra finches. This reduces the dimensionality of singing dynamics, described as trajectories (motor gestures) in a space of syringeal pressure and tension. Here we assess model performance by characterizing the auditory response ‘replay’ of song premotor HVC neurons to the presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed within a few milliseconds of the extreme time points of the gesture trajectories. Thus, the HVC precisely encodes vocal motor output through activity at the times of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons is used as a ‘forward’ model, representing the sequence of gestures in song to make predictions on expected behaviour and evaluate feedback.
publishDate 2013
dc.date.none.fl_str_mv 2013-02-27
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/11336/2478
Amador, Ana; Sanz Perl Hernandez, Yonatan; Mindlin, Bernardo Gabriel; Margoliash, Daniel; Elemental gesture dynamics are encoded by song premotor cortical neurons; Nature Publishing Group; Nature; 495; 7439; 27-2-2013; 59-64
0028-0836
url http://hdl.handle.net/11336/2478
identifier_str_mv Amador, Ana; Sanz Perl Hernandez, Yonatan; Mindlin, Bernardo Gabriel; Margoliash, Daniel; Elemental gesture dynamics are encoded by song premotor cortical neurons; Nature Publishing Group; Nature; 495; 7439; 27-2-2013; 59-64
0028-0836
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/doi:10.1038/nature11967
info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/nature/journal/v495/n7439/full/nature11967.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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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