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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/9836
Ver los metadatos del registro completo
id |
CONICETDig_f396196f5efd740c36d92a9bb2901344 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/9836 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
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 |
_version_ |
1844613911258595328 |
score |
13.070432 |