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dc.contributor.author
Samengo, Ines
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dc.contributor.author
Gollisch, Tim
dc.date.available
2016-12-20T19:01:12Z
dc.date.issued
2013-02
dc.identifier.citation
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
dc.identifier.issn
0929-5313
dc.identifier.uri
http://hdl.handle.net/11336/9836
dc.description.abstract
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 Gaussiandistribution. 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 spiketriggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription.<br />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<br />and compare it to other prescriptions given in the literature.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Covariance Analysis
dc.subject
Spike-Triggered Average
dc.subject
Receptive Field
dc.subject
Linear-Nonlinear Model
dc.subject.classification
Otras Ciencias Biológicas
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dc.subject.classification
Ciencias Biológicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-12-19T18:19:47Z
dc.identifier.eissn
1573-6873
dc.journal.volume
34
dc.journal.number
1
dc.journal.pagination
137-161
dc.journal.pais
Alemania
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dc.journal.ciudad
Berlín
dc.description.fil
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
dc.description.fil
Fil: Gollisch, Tim . Universitat of Gottingen; Alemania
dc.journal.title
Journal Of Computational Neuroscience
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10827-012-0411-y
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10827-012-0411-y
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