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Journal of Computational Neuroscience

, Volume 34, Issue 1, pp 137–161

First Online: 15 July 2012Received: 15 January 2012Revised: 12 May 2012Accepted: 27 June 2012


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.

KeywordsCovariance analysis Spike-triggered average Receptive field Linear-nonlinear model Action Editor: Jonathan David Victor

This work was supported by Consejo Nacional de Investigaciones Científicas y Técnicas, Agencia Nacional de Promoción Científica y Tecnológica, Universidad Nacional de Cuyo, Comisión Nacional de Energía Atómica IS and by the German Initiative of Excellence, the International Human Frontier Science Program Organization, and the Deutsche Forschungsgemeinschaft through the Collaborative Research Center 889 TG.

Sample software code, implemented in C, is available at http:-fisica.cab.cnea.gov.ar-estadistica-ines-stc-software.html.

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Autor: Inés Samengo - Tim Gollisch

Fuente: https://link.springer.com/

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