### Browsing by Author "Depireux, Didier A."

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Item Analysis of Dynamic Spectra in Ferret Primary Auditory Cortex: I. Characteristics of Single Unit Responses to Moving Ripple Spectra(1995) Kowalski, Nina; Depireux, Didier A.; Shamma, S.A.; ISRAuditory stimuli referred to as moving ripples are used to characterize the responses of both single and multiple units in the ferret primary auditory cortex (AI). Moving ripples are broadband complex sounds with sinusoidal spectral profiles that drift along the tonotopic axis at a constant velocity. Neuronal responses to moving ripples are locked to the phase of the ripple, i.e., they exhibit the same periodicity as that of the moving ripple profile. Neural responses are characterized as a function of ripple velocity (temporal property) and ripple frequency (spectral property). Transfer functions describing the response to these temporal and spectral modulations are constructed. Temporal transfer functions are inverse Fourier transformed to obtain impulse response functions that reflect the cell's temporal characteristics. Ripple transfer functions are inverse Fourier transformed to obtain the response field, characterizing the cell's response area along the tonotopic axis. These operations assume linearity in the cell's response to moving ripples. Separability of the temporal and ripple transfer functions is established by comparing transfer functions across different test parameters. Response fields measured with either stationary ripples or moving ripples are shown to be similar. Separability implies that the neuron can be modeled as processing spatio-temporal information in two distinct stages. The assumption of linearity implies that each of these stages is a linear operation.The ripples parameters that characterize cortical cells are distributed somewhat evenly, with the characteristic ripple frequencies ranging from 0.2 to over 2 cycles/octave and the characteristic angular frequency typically ranging from 2 to 20 Hz. Many responses exhibit periodicities not found in the spectral envelope of the stimulus. These periodicities are of two types. Slow rebounds with a period of about 150 ms appear with various strengths in about 30 % of the cells. Fast regular firings, with interspike intervals of the order of 10 ms are much less common and may reflect the ability of certain cells to follow the fine structure of the stimulus.

Item Analysis of Dynamic Spectra in Ferret Primary Auditory Cortex: II. Prediction of, Unit Responses to Arbitrary Dynamic Spectra(1995) Kowalski, Nina; Depireux, Didier A.; Shamma, S.A.; ISRResponses 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.Item Dynamics of Neural Responses in Ferret Primary Auditory Cortex: I. Spectro-Temporal Response Field Characterization by Dynamic Ripple Spectra(1999) Depireux, Didier A.; Simon, J.Z.; Klein, David J.; Shamma, S.A.; ISR; CAARTo 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 and predicts the linear response of neurons to sounds with rich spectro-temporal envelopes. It is calculated here from the responses to elementary "ripples," a family of sounds with drifting, sinusoidal, spectral envelopes--the complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples.The collection of responses to all elementary ripples is the spectro-temporal transfer function. 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 compare the responses to upward and downward drifting ripples, assuming separability within each direction, to determine if the total bi-directional 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, not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.

Item Linear stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex(2003) Klein, David J.; Simon, Jonathan Z.; Depireux, Didier A.; Shamma, Shihab A.; Shamma, Shihab A.; ISR; CAARThe spectrotemporal receptive field (STRF) provides a versatile and integrated (spectral and temporal) functional characterization of single cells in primary auditory cortex (AI). We explore in this paper the origin and relationship between several different ways of measuring and analyzing the STRF. Specifically, we demonstrate that STRFs measured using a spectrotemporally diverse array of broadband stimuli --- such as dynamic ripples, spectrotemporally white noise (STWN), and temporally orthogonal ripple combinations (TORCs) --- are very similar, confirming earlier findings that the STRF is a robust linear descriptor of the cell. We also present a new deterministic analysis framework that employs the Fourier series to describe the spectrotemporal modulation frequency content of the stimuli and responses. Additional insights into the STRF measurements, including the nature and interpretation of measurement errors, is presented using the Fourier transform, coupled to singular-value decomposition (SVD), and variability analyses including bootstrap. The results promote the utility of the STRF as a core functional descriptor of neurons in AI.Item Robust Spectro-Temporal Reverse Correlation for the Auditory System: Optimizing Stimulus Design(1999) Klein, David J.; Depireux, Didier A.; Simon, J.Z.; Shamma, S.A.; ISR; CAARThe spectro-temporal receptive field (STRF) is a functionaldescriptor of the linear processing of time-varying acoustic spectra by theauditory system. By cross-correlating sustained neuronal activity with the"dynamic spectrum" of a spectro-temporally rich stimulus ensemble, oneobtains an estimate of the STRF.In this paper, the relationship betweenthe spectro-temporal structure of any given stimulus and the quality ofthe STRF estimate is explored and exploited. Invoking the Fouriertheorem, arbitrary dynamic spectra are described as sums of basicsinusoidal components, i.e., "moving ripples." Accurate estimation isfound to be especially reliant on the prominence of components whosespectral and temporal characteristics are of relevance to the auditorylocus under study, and is sensitive to the phase relationships betweencomponents with identical temporal signatures.

These and otherobservations have guided the development and use of stimuli withdeterministic dynamic spectra composed of the superposition of many"temporally orthogonal" moving ripples having a restricted, relevant rangeof 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).