Shihab A. Shamma
Elec. Eng. Dept. and Inst. for Systems Res., Univ. of Maryland, College Park, MD 20742
A fundamental goal in auditory cortical physiology has been to understand how the spectral profile is represented in the firing rate of cortical cells. Recent experimental findings shed light on two basic properties of this representation in AI: (1) Responses to sounds with broadband spectra (such as speech and most environmental sounds) superimpose linearly. Thus if a complex arbitrary spectral profile is viewed as composed of elementary spectral profiles, then AI responses to such a profile can be reconstructed from the sum of the responses to these simpler elementary profiles. This seems to be true both for stationary and dynamic spectra. (2) AI units are rather selective to the spectral and temporal parameters of the acoustic profile. Specifically, when tested with elementary spectral profiles that are sinusoidally shaped against the logarithmic frequency axis (or so called rippled spectra), and that slide against this axis at various velocities, AI units are found to be tuned around different ripple densities, ripple phases, and ripple velocities. The above two findings suggest that AI performs a Fourier-like analysis of the spectral profile into a composite of weighted sinusoidally shaped spectra (ripples). Such an analysis is analogous to that of visual scenes found in the primary visual cortex (VI).