Institute for Systems Research
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Item Ripple Analysis in Ferret Primary Auditory Cortex. II. Topographic and Columnar Distribution of Ripple Response Parameters(1994) Versnel, H.; Kowalski, Nina; Shamma, S.A.; ISRWe examined the columnar and topographic distribution of response parameters using spectral ripples and tonal stimuli in the primary auditory cortex (AI) of the barbiturate-anesthetized ferret. The ripple stimuli consisted of broadband stimuli (1-20 kHz) with sinusoidally modulated spectral envelopes.Responses to ripples were parametrized in terms of characteristic ripple Wo(ripple frequency where the magnitude of the ripple transfer function is maximal, i.e., where the cell responds best) and characteristic phase Fo (intercept of the phase of the ripple transfer function, i.e., phase where the cell responds best). The response area (measured with tones) was parametrized in terms of its excitatory bandwidth at 20 dB above threshold (BW20), and its asymmetry as reflected by the directional sensitivity index (C) to frequency-modulated (FM) tones. Columnar organization for the above four parameters was investigated in 66 single units from 23 penetrations. It was confirmed for Wo, Fo, and the C index, but it appeared to be ambiguous for BW20. The response parameters measured from multiunit recordings corresponded closely to those obtained from single units in the same cluster. In a local region, most cells exhibited closely matched, response fields (RFs, inverse Fourier transformed ripple transfer function) and response areas (measured with two-tone stimuli), and had correspondingly similar response parameters to ripples and tones. The topographic distribution of the response parameters across the surface of AI was studied with multiunit recordings in four animals. In all maps, systematic patterns or clustering of, response parameters could be discerned along the isofrequency planes.
The distribution of the characteristic ripple Wo exhibited two trends. First, along the isofrequency planes, it was largest near the center of AI, gradually decreasing towards the edges of the field where often a secondary maximum was found.
The second trend occurred along the tonotopic axis where the maximum Wo found in an isofrequency range increases with increasing BF. The tonal bandwidth BW20, which was inversely correlated with Wo, exhibited a similar topographic distribution along the tonotopic axis and the isofrequency planes. The distribution of the characteristic ripple phase, Fo which reflects the asymmetry in the response field, showed a systematic order along the isofrequency axis. At the center of AI symmetric responses (Fo 0) predominated. Towards the edges, the RFs became more asymmetric with Fo < 0 caudally, and Fo > 0 rostrally. The asymmetric response types tended to cluster along repeated bands that paralleled the tonotopic axis. The FM directional sensitivity (C index, reflecting asymmetry of tonal response areas) tends to have similar trends along the isofrequency axis as Fo.
Item Representation of Spectral Profiles in the Auditory System, II: Detection of Spectral Peak Shape Changes(1994) Vranic-Sowers, S.; Shamma, S.A.; ISRBased on the ripple analysis model outlined in Part I of this paper, predictions are made for the detection of shape changes in spectral peak profiles. Peak shape is uniquely described in terms of two parameters: bandwidth factor (BWF) which reflects the tuning or sharpness of a peak, and a symmetry factor (SF) which roughly measures the local evenness or oddness of a peak. Using profile analysis methods, thresholds to changes in these parameters (defined as dBWF/BWF and dSF) are measured together with the effects of several manipulations such as using different peak levels, varying spectral component densities, and randomizing the frequencies of the peaks. The new ripple analysis model accounts well for the measured thresholds. Predictions of the three previously published models for the same profiles are also evaluated and discussed.Item Representation of Spectral Profiles in the Auditory System, I: A Ripple Analysis Model(1994) Vranic-Sowers, S.; Shamma, S.A.; ISRA model of profile analysis is proposed in which a spectral profile is assumed to be represented by a weighted sum of sinusoidally modulated spectra (ripples). The analysis is performed by a bank of bandpass filters, each tuned to a particular ripple frequency and ripple phase. The parameters of the model are estimated using data from ripple detection experiments in [Green} 1986; Hillier 1991]. Detection thresholds are computed from the filter outputs and compared with perceptual thresholds, for profile detection experiments with step, single component increment, and the alternating profiles. The model accounts well for the measured thresholds in these experiments. Physiological and psychophysical evidences from the auditory and visual systems in support of this type of a model are also reviewed. The implications of this model for pitch and timbre perception are briefly discussed.Item Representation of Spectral Profiles in the Auditory System Part II: A Ripple Analysis Model(1993) Vranic-Sowers, S.; Shamma, S.A.; ISRBased on experimental results presented in [Vranic-Sowers and Shamma, 1993], and on further physiological and psychoacoustical evidence, it is argued that the auditory system analyzes a spectral profile along two largely independent dimensions. They correspond to the magnitude and phase of a localized Fourier transformation of the profile, closely analogues to the spatial frequency transformations described in the visual system. Within this general framework, a model of profile analysis is proposed in which a spectral profile is assumed to be represented by a weighted sum of sinusoidally modulated spectra (ripples). The analysis is performed by a bank of bandpass filters, each tuned to a particular ripple frequency and ripple phase. The parameters of the model are estimated using data from ripple detection experiments in [Green, 1986; Hillier, 1991]. Perceptual thresholds are then computed from the filter outputs and compared with thresholds measured for peak profile experiments, and for detection tasks with step, single component increment, and the alternating profiles.Item Representation of Spectral Profiles in the Auditory Systems(1992) Vranic, S.; Shamma, S.A.; ISRThis paper explores the question of how spectral profiles (such as spectral peaks) might be represented and perceived in the auditory system. Using profile analysis methods, we measured listeners' sensitivities to changes in spectral peak shapes that were uniquely described in terms of two parameters: a symmetry factor (SF) which roughly measure the local evenness or oddness of a peak, and a bandwidth factor (BWF) which reflects the tuning or sharpness of a peak. The effects of several manipulations on the perceptual thresholds were also tested; they include using different peak levels, varying spectral component densities, and randomizing the frequencies of the peaks. The basic result that emerges is that threasholds to changes in SF and BWF are constant regardless of peak shape. Thus, for the detection of SF changes, dSF thresholds are approximately constant regardless of a peak's SF and BWF. The only exception occurs towards the narrowest peaks where detection thresholds rise. For the detection of BWF changes, all dBWF/BWF thresholds remain constant regardless of peak shape. A fundamental conclusion arising from these data is that peak profiles are represented along two sensitive and largely independent axes: peak bandwidth and symmetry factors. More generally, however, it is argued that for an arbitrary spectral profile these two axes simply correspond to the magnitude and phase of a fourier transformation (or more precisely, of a Wavelet transformation) of the profile, closely analogous to the spatial frequency transformations described in the visual system. Further physiological and phychophysical evidence in support of this hypothesis is discussed.Item Zero-Crossing and Noise Suppression in Auditory Wavelet Transformations(1992) Wang, K.; Shamma, S.A.; ISRA common sequence of operations in the early stages of most biological sensory systems is a wavelet transform followed by a compressive nonlinearity. In this paper, we explore the contribution of these operations to the formation of robust and perceptually significant representations in the auditory system. It is demonstrated that the neural representation of a complex signal such as speech is derived from a highly reduced version of its wavelet transform, specifically, from the distribution of its locally averaged zero-crossing rates along the temporal and scale axes. It is shown analytically that such encoding of the wavelet transform results in mutual suppressive interactions across its different scale representations. Suppression in turn endows the representation with enhanced spectral peaks and superior robustness in noisy environments. Examples using natural speech vowels are presented to illustrate the results. Finally, we discuss the relevance of these findings to conventional subband coding of speech signals.Item Minimum Mean Square Error Estimation of Connectivity in Biological Neural Networks(1991) Yang, X.; Shamma, S.A.; ISRA minimum mean square error (MMSE) estimation scheme is employed to identify the synaptic connectivity in neural networks. This new approach can substantially reduce the amount of data and the computational cost involved in the conventional correlation methods, and is suitable for both nonstationary and stationary neuronal firings. Two algorithms are proposed to estimate the synaptic connectivities recursively, one for nonlinear filtering, the other for linear filtering. In addition, the lower and upper bounds for the MMSE estimator are determined. It is shown that the estimators are consistent in quadratic mean. We also demonstrate that the conventional crossinterval histogram is an asymptotic linear MMSE estimator with an inappropriate initial value. Finally, simulations of both the nonlinear and linear (Kalman filter) estimates demonstrate that the true connectivity values are approached asymptotically.Item Spectral Gradient Columns in Primary Auditory Cortex: Physiological and Psychoacoustical Correlates(1991) Shamma, S.A.; Vranic, S.; Wiser, P.; ISRMapping of the spatial distribution of responses in the primary auditory cortex (AI) reveal that both the gradient of the acoustic spectrum and sensitivity to FM sweep direction are encoded in an orderly manner along the isofrequency planes of AI. Psychoacoustical tests also demonstrate a potential perceptual correlate of the gradient maps, namely the threshold stability and heightened sensitivity of human subjects to the detection of changes in the symmetry of spectral peaks.Item Auditory Representations of Acoustic Signals(1991) Yang, X.; Wang, K.; Shamma, S.A.; ISRAn analytically tractable framework is presented to describe neural processing in the early stages of the auditory system. Algorithms are developed to assess the integrity of the acoustic spectrum at all processing stages. The algorithms employ wavelet representations, multiresolution processing, and the method of convex projections to reconstruct close replica of the input stimulus. Reconstructions using natural speech sounds demonstrate minimal loss of information along the auditory pathway. Furthermore, close inspections of the final auditory patterns reveals spectral enhancements and noise suppression that have close perceptual correlates. Finally, the auditory representations are shown to be versatile for many applications, including automatic speech recognition and low bit-rate data compression.