Analysis of Dynamic Spectra in Ferret Primary Auditory Cortex: II. Prediction of, Unit Responses to Arbitrary Dynamic Spectra

Abstract

Responses of single units and unit clusters were recorded in the ferret primary auditory cortex (AI) using broadband complex dynamic spectra. Previous work (Kowalski et al 1995) demonstrated that simpler spectra consisting of single moving ripples (i.e., sinusoidally modulated spectral profiles that travel at a constant velocity along the logarithmic frequency axis) could be used effectively to characterize the response fields and transfer functions of AI cells. An arbitrary complex dynamic spectral profile can be thought of conceptually as being composed of a weighted sum of moving ripple spectra. Such a decomposition can be computed from a two-dimensional spectro- temporal Fourier transform of the dynamic spectral profile with moving ripples as the basis function. Therefore, if AI units were essentially linear satisfying the superposition principle, then their responses to arbitrary dynamic spectra could be predicted from the responses to single moving ripples, i.e., from the units response fields and transfer functions. This conjecture was tested and confirmed with data from 293 combinations of moving ripples, involving complex spectra composed of up to 15 moving ripples of different ripple frequencies and velocities. For each case, response predictions based on the unit transfer functions were compared to measured responses. The correlation between predicted and measured responses was found to be consistently high (84% with rho > 0.6). The distribution of response parameters suggest that AI cells may encode the profile of a dynamic spectrum by performing a multiscale spectro-temporal decomposition of the dynamic spectral profile in a largely linear manner.