Institute for Systems Research
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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 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 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 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.