Spectro-Temporal Response Field Characterization With Dynamic Ripples in Ferret Primary Auditory Cortex

Abstract

To understand the neural representation of broadband, dynamic sounds in primary auditory cortex (AI), we characterize responses using the spectro-temporal response field (STRF). The STRF describes, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectro-temporal envelopes. It is computed from the responses to elementary "ripples," a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions. Here we measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective. We show that for most neurons, the lack of full separability stems from differences between the upward and downward spectral cross-sections but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.

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

“…Such processing is already under study in auditory neurophysiology (Kowalski et al, 1996a,b;Depireux et al, 2001;Miller et al, 2001Miller et al, , 2002Escabí and Schreiner, 2002) and psychoacoustics (Chi et al, 1999), and is also being investigated for various signal-processing tasks, including audio coding (Atlas and Shamma, 2003;Klein et al, 2003) and speech recognition (Hermmansky, 1999;Nadeu et al, 2001;Kleinschmidt and Gelbart, 2002;Kleinschmidt, 2002).…”

“…This second case is of primary interest in this study; we will gauge the proportion of (error-free) STRF power excluded from the SVD approximation. A naive gauge of this is α SV D , the proportion of the STRF measurement's power contained in the SVD remainder (Depireux et al, 2001). Unfortunately, when the level of measurement error is high, α SV D itself will be inflated, because much of the remainder will consist of error.…”

“…For the dynamic-ripple stimuli (Kowalski et al, 1996a;Depireux et al, 2001) shown in Figure 2A, each stimulus is composed of a single spectrotemporal modulation frequency (Fourier component). It can therefore be considered the auditory equivalent to the drifting sinusoidal luminance gratings used in visual neuroscience (Valois and Valois, 1990).…”

“…It is important to note that, due to time constraints, these point-by-point measurements of H were restricted to two cross-sections, as indicated by the gray outlines in left panel (Bottom row). The full H was then constructed from a normalized outer product of these cross-sections (Depireux et al, 2001). …”

“…At one extreme are the dynamic ripple stimuli (Kowalski et al, 1996a,b;Depireux et al, 2001), which each consist of a single spectrotemporal modulation frequency. At the other extreme is spectrotemporally white noise (STWN), which contains many superimposed spectrotemporal modulation frequencies.…”