Journal of Computational Neuroscience

, Volume 9, Issue 1, pp 85–111 | Cite as

Robust Spectrotemporal Reverse Correlation for the Auditory System: Optimizing Stimulus Design

  • D.J. Klein
  • D.A. Depireux
  • J.Z. Simon
  • S.A. Shamma

Abstract

The spectrotemporal receptive field (STRF) is a functional descriptor of the linear processing of time-varying acoustic spectra by the auditory system. By cross-correlating sustained neuronal activity with the dynamic spectrum of a spectrotemporally rich stimulus ensemble, one obtains an estimate of the STRF. In this article, the relationship between the spectrotemporal structure of any given stimulus and the quality of the STRF estimate is explored and exploited. Invoking the Fourier theorem, arbitrary dynamic spectra are described as sums of basic sinusoidal components—that is, moving ripples. Accurate estimation is found to be especially reliant on the prominence of components whose spectral and temporal characteristics are of relevance to the auditory locus under study and is sensitive to the phase relationships between components with identical temporal signatures. These and other observations have guided the development and use of stimuli with deterministic dynamic spectra composed of the superposition of many temporally orthogonal moving ripples having a restricted, relevant range of spectral scales and temporal rates. The method, termed sum-of-ripples, is similar in spirit to the white-noise approach butenjoys the same practical advantages—which equate to faster and moreaccurate estimation—attributable to the time-domain sum-of-sinusoidsmethod previously employed in vision research. Application of the methodis exemplified with both modeled data and experimental data from ferretprimary auditory cortex (AI).

reverse correlation moving ripples sum-of-sinusoids spectrotemporal receptive field auditory cortex 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • D.J. Klein
    • 1
  • D.A. Depireux
    • 1
  • J.Z. Simon
    • 1
  • S.A. Shamma
    • 2
  1. 1.Institute for Systems ResearchUniversity of Maryland
  2. 2.Institute for Systems ResearchUniversity of Maryland

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