Robust spectrotemporal reverse correlation for the auditory system: Optimizing stimulus design

被引:170
作者
Klein D.J. [1 ]
Depireux D.A. [1 ]
Simon J.Z. [1 ]
Shamma S.A. [1 ]
机构
[1] Institute for Systems Research, University of Maryland, College Park
基金
美国国家科学基金会;
关键词
Auditory cortex; Moving ripples; Receptive field; Reverse correlation; Spectrotemporal; Sum-of-sinusoids;
D O I
10.1023/A:1008990412183
中图分类号
学科分类号
摘要
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 but enjoys the same practical advantages - which equate to faster and more accurate estimation - attributable to the time-domain sum-of-sinusoids method previously employed in vision research. Application of the method is exemplified with both modeled data and experimental data from ferret primary auditory cortex (AI).
引用
收藏
页码:85 / 111
页数:26
相关论文
共 66 条
[41]  
Nelken I., Rotman Y., Yosef O., Responses of auditory-cortex neurons to structural features of natural sounds, Nature, 397, pp. 154-157, (1999)
[42]  
Palm G., Popel B., Volterra representation and Wiener-like identification of nonlinear systems: Scope and limitations, Quarterly Rev. Biophysics, 18, pp. 135-164, (1985)
[43]  
Papoulis A., The Fourier Integral and Its Applications, (1962)
[44]  
Pickles J., An Introduction to the Physiology of Hearing, (1988)
[45]  
Ruggero M., Physiology and coding of sound in the auditory nerve, The Mammalian Auditory Pathway: Neurophysiology, pp. 34-93, (1992)
[46]  
Schafer M., Rubsamen R., Dorrscheidt G., Knipschild M., Setting complex tasks to single units in the avian forebrain. II: Do we really need natural stimuli to describe neuronal response characteristics?, Hearing Res., 57, pp. 231-244, (1992)
[47]  
Schreiner C., Calhoun B., Spectral envelope coding in cat primary auditory cortex: Properties of ripple transfer functions, J. Auditory Neurosci., 1, pp. 39-61, (1995)
[48]  
Shamma S., Speech processing in the auditory system I: The representation of speech sounds in the responses of the auditory nerve, J. Acoustical Soc. America, 78, pp. 1612-1621, (1985)
[49]  
Shamma S., Depireux D., Klein D., Simon J., Representation of dynamic broadband spectra in auditory cortex, Soc. Neurosci. Abstracts, 24, (1998)
[50]  
Shamma S., Versnel H., Kowalski N., Ripple analysis in the ferret primary auditory cortex. I. Response characteristics of single units to sinusoidally ripples spectra, J. Auditory Neurosci., 1, pp. 233-254, (1995)