Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli

Autores
Samengo, Ines; Gollisch, Tim
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.
Fil: Samengo, Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Energia Atomica. Gerencia del Area de Investigaciones y Aplicaciones no Nucleares. Gerencia de Fisica (CAB); Argentina. Comisión Nacional de Energía Atómica. Gerencia del Area de Energía Nuclear. Instituto Balseiro; Argentina
Fil: Gollisch, Tim. Universitat of Gottingen; Alemania
Materia
Covariance Analysis
Spike-Triggered Average
Receptive Field
Linear-Nonlinear Model
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:11336/9836

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spelling Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuliSamengo, InesGollisch, TimCovariance AnalysisSpike-Triggered AverageReceptive FieldLinear-Nonlinear Modelhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.Fil: Samengo, Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Energia Atomica. Gerencia del Area de Investigaciones y Aplicaciones no Nucleares. Gerencia de Fisica (CAB); Argentina. Comisión Nacional de Energía Atómica. Gerencia del Area de Energía Nuclear. Instituto Balseiro; ArgentinaFil: Gollisch, Tim. Universitat of Gottingen; AlemaniaSpringer2013-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/9836Samengo, Ines; Gollisch, Tim; Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli; Springer; Journal Of Computational Neuroscience; 34; 1; 2-2013; 137-1610929-53131573-6873enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10827-012-0411-yinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10827-012-0411-yinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3558678/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:21Zoai:ri.conicet.gov.ar:11336/9836instacron: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:34982025-09-29 10:06:21.987CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
title Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
spellingShingle Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
Samengo, Ines
Covariance Analysis
Spike-Triggered Average
Receptive Field
Linear-Nonlinear Model
title_short Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
title_full Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
title_fullStr Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
title_full_unstemmed Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
title_sort Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
dc.creator.none.fl_str_mv Samengo, Ines
Gollisch, Tim
author Samengo, Ines
author_facet Samengo, Ines
Gollisch, Tim
author_role author
author2 Gollisch, Tim
author2_role author
dc.subject.none.fl_str_mv Covariance Analysis
Spike-Triggered Average
Receptive Field
Linear-Nonlinear Model
topic Covariance Analysis
Spike-Triggered Average
Receptive Field
Linear-Nonlinear Model
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.
Fil: Samengo, Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Energia Atomica. Gerencia del Area de Investigaciones y Aplicaciones no Nucleares. Gerencia de Fisica (CAB); Argentina. Comisión Nacional de Energía Atómica. Gerencia del Area de Energía Nuclear. Instituto Balseiro; Argentina
Fil: Gollisch, Tim. Universitat of Gottingen; Alemania
description The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.
publishDate 2013
dc.date.none.fl_str_mv 2013-02
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/11336/9836
Samengo, Ines; Gollisch, Tim; Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli; Springer; Journal Of Computational Neuroscience; 34; 1; 2-2013; 137-161
0929-5313
1573-6873
url http://hdl.handle.net/11336/9836
identifier_str_mv Samengo, Ines; Gollisch, Tim; Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli; Springer; Journal Of Computational Neuroscience; 34; 1; 2-2013; 137-161
0929-5313
1573-6873
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10827-012-0411-y
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10827-012-0411-y
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3558678/
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 Springer
publisher.none.fl_str_mv Springer
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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