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Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex


Klein, David J; Simon, Jonathan Z; Depireux, Didier A; Shamma, Shihab A (2006). Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex. Journal of Computational Neuroscience, 20(2):111-136.

Abstract

The spectrotemporal receptive field (STRF) provides a versatile and integrated, spectral and temporal, functional characterization of single cells in primary auditory cortex (AI). In this paper, we explore the origin of, and relationship between, different ways of measuring and analyzing an STRF. We demonstrate that STRFs measured using a spectrotemporally diverse array of broadband stimuli—such as dynamic ripples, spectrotemporally white noise, and temporally orthogonal ripple combinations (TORCs)—are very similar, confirming earlier findings that the STRF is a robust linear descriptor of the cell. We also present a new deterministic analysis framework that employs the Fourier series to describe the spectrotemporal modulations contained in the stimuli and responses. Additional insights into the STRF measurements, including the nature and interpretation of measurement errors, is presented using the Fourier transform, coupled to singular-value decomposition (SVD), and variability analyses including bootstrap. The results promote the utility of the STRF as a core functional descriptor of neurons in AI

Abstract

The spectrotemporal receptive field (STRF) provides a versatile and integrated, spectral and temporal, functional characterization of single cells in primary auditory cortex (AI). In this paper, we explore the origin of, and relationship between, different ways of measuring and analyzing an STRF. We demonstrate that STRFs measured using a spectrotemporally diverse array of broadband stimuli—such as dynamic ripples, spectrotemporally white noise, and temporally orthogonal ripple combinations (TORCs)—are very similar, confirming earlier findings that the STRF is a robust linear descriptor of the cell. We also present a new deterministic analysis framework that employs the Fourier series to describe the spectrotemporal modulations contained in the stimuli and responses. Additional insights into the STRF measurements, including the nature and interpretation of measurement errors, is presented using the Fourier transform, coupled to singular-value decomposition (SVD), and variability analyses including bootstrap. The results promote the utility of the STRF as a core functional descriptor of neurons in AI

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:National licences > 142-005
Dewey Decimal Classification:Unspecified
Scopus Subject Areas:Life Sciences > Sensory Systems
Life Sciences > Cognitive Neuroscience
Life Sciences > Cellular and Molecular Neuroscience
Language:English
Date:1 April 2006
Deposited On:29 Nov 2018 16:41
Last Modified:20 Sep 2023 01:40
Publisher:Springer
ISSN:0929-5313
OA Status:Green
Publisher DOI:https://doi.org/10.1007/s10827-005-3589-4
  • Content: Published Version
  • Language: English
  • Description: Nationallizenz 142-005