Ian C. Bruce

A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression.

A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. Level-dependent gain (compression), bandwidth, and phase properties were implemented with a control path that varied the gain and bandwidth of tuning in the signal-path filter. By making the bandwidth of the control path broad with respect to the signal path, the wide frequency range of two-tone suppression was included. By making the control-path filter level dependent and tuned to a frequency slightly higher than the signal-path filter, other properties of two-tone suppression were also included. These properties included the asymmetrical growth of suppression above and below the characteristic frequency and the frequency offset of the suppression tuning curve with respect to the excitatory tuning curve. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.

Modeling auditory-nerve responses for high sound pressure levels in the normal and impaired auditory periphery

This paper presents a computational model to simulate normal and impaired auditory-nerve (AN) fiber responses in cats. The model responses match physiological data over a wider dynamic range than previous auditory models. This is achieved by providing two modes of basilar membrane excitation to the inner hair cell (IHC) rather than one. The two modes are generated by two parallel filters, component 1 (C1) and component 2 (C2), and the outputs are subsequently transduced by two separate functions. The responses are then added and passed through the IHC low-pass filter followed by the IHC-AN synapse model and discharge generator. The C1 filter is a narrow-band, chirp filter with the gain and bandwidth controlled by a nonlinear feed-forward control path. This filter is responsible for low and moderate level responses. A linear, static, and broadly tuned C2 filter followed by a nonlinear, inverted and nonrectifying C2 transduction function is critical for producing transition region and high-level effects. Consistent with Kiangs two-factor cancellation hypothesis, the interaction between the two paths produces effects such as the C1/C2 transition and peak splitting in the period histogram. The model responses are consistent with a wide range of physiological data from both normal and impaired ears for stimuli presented at levels spanning the dynamic range of hearing.

A stochastic model of the electrically stimulated auditory nerve: single-pulse response

Most models of neural response to electrical stimulation, such as the Hodgkin-Huxley equations, are deterministic, despite significant physiological evidence for the existence of stochastic activity. For instance, the range of discharge probabilities measured in response to single electrical pulses cannot be explained at all by deterministic models. Furthermore, there is growing evidence that the stochastic component of auditory nerve response to electrical stimulation may be fundamental to functionally significant physiological and psychophysical phenomena. In this paper authors present a simple and computationally efficient stochastic model of single-fiber response to single biphasic electrical pulses, based on a deterministic threshold model of action potential generation. Comparisons with physiological data from cat auditory nerve fibers are made, and it is shown that the stochastic model predicts discharge probabilities measured in response to single biphasic pulses more accurately than does the equivalent deterministic model. In addition, physiological data show an increase in stochastic activity with increasing pulse width of anodic/cathodic biphasic pulses, a phenomenon not present for monophasic stimuli. Those and other data from the auditory nerve are then used to develop a population model of the total auditory nerve, where each fiber is described by the single-fiber model.

An auditory-periphery model of the effects of acoustic trauma on auditory nerve responses

Acoustic trauma degrades the auditory nerves tonotopic representation of acoustic stimuli. Recent physiological studies have quantified the degradation in responses to the vowel /E/ and have investigated amplification schemes designed to restore a more correct tonotopic representation than is achieved with conventional hearing aids. However, it is difficult from the data to quantify how much different aspects of the cochlear pathology contribute to the impaired responses. Furthermore, extensive experimental testing of potential hearing aids is infeasible. Here, both of these concerns are addressed by developing models of the normal and impaired auditory peripheries that are tested against a wide range of physiological data. The effects of both outer and inner hair cell status on model predictions of the vowel data were investigated. The modeling results indicate that impairment of both outer and inner hair cells contribute to degradation in the tonotopic representation of the formant frequencies in the auditory nerve. Additionally, the model is able to predict the effects of frequency-shaping amplification on auditory nerve responses, indicating the models potential suitability for more rapid development and testing of hearing aid schemes.

Representation of the vowel /ε/ in normal and impaired auditory nerve fibers: Model predictions of responses in cats

The temporal response of auditory-nerve (AN) fibers to a steady-state vowel is investigated using a computational auditory-periphery model. The model predictions are validated against a wide range of physiological data for both normal and impaired fibers in cats. The model incorporates two parallel filter paths, component 1 (C1) and component 2 (C2), which correspond to the active and passive modes of basilar membrane vibration, respectively, in the cochlea. The outputs of the two filters are subsequently transduced by two separate functions, added together, and then low-pass filtered by the inner hair cell (IHC) membrane, which is followed by the IHC-AN synapse and discharge generator. The C1 response dominates at low and moderate levels and is responsible for synchrony capture and multiformant responses seen in the vowel responses. The C2 response dominates at high levels and contributes to the loss of synchrony capture observed in normal and impaired fibers. The interaction between C1 and C2 responses explains the behavior of AN fibers in the transition region, which is characterized by two important observations in the vowel responses: First, all components of the vowel undergo the C1/C2 transition simultaneously, and second, the responses to the nonformant components of the vowel become substantial.

Updated parameters and expanded simulation options for a model of the auditory periphery

A phenomenological model of the auditory periphery in cats was previously developed by Zilany and colleagues [J. Acoust. Soc. Am. 126, 2390-2412 (2009)] to examine the detailed transformation of acoustic signals into the auditory-nerve representation. In this paper, a few issues arising from the responses of the previous version have been addressed. The parameters of the synapse model have been readjusted to better simulate reported physiological discharge rates at saturation for higher characteristic frequencies [Liberman, J. Acoust. Soc. Am. 63, 442-455 (1978)]. This modification also corrects the responses of higher-characteristic frequency (CF) model fibers to low-frequency tones that were erroneously much higher than the responses of low-CF model fibers in the previous version. In addition, an analytical method has been implemented to compute the mean discharge rate and variance from the models synapse output that takes into account the effects of absolute refractoriness.

Robust Formant Tracking for Continuous Speech With Speaker Variability

Several algorithms have been developed for tracking formant frequency trajectories of speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. The algorithm presented in this paper obtains formant frequency estimates from voiced segments of continuous speech by using a time-varying adaptive filterbank to track individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for formant extraction from continuous speech, for both male and female speakers. The algorithm has a low signal delay and provides smooth and accurate estimates for the first four formant frequencies at moderate and high signal-to-noise ratios. Thorough testing of the algorithm has shown that it is robust over a wide range of signal-to-noise ratios for various types of background noises.

A stochastic model of the electrically stimulated auditory nerve: pulse-train response

The single-pulse model of the companion paper [see ibid., vol. 46, no. 6, p. 617-29, 1999] is extended to describe responses to pulse trains by introducing a phenomenological refractory mechanism. Comparisons with physiological data from cat auditory nerve fibers are made for pulse rates between 100 and 800 pulses/s. First, it is shown that both the shape and slope of mean discharge rate curves are better predicted by the stochastic model than by the deterministic model. Second, while interpulse effects such as refractory effects do indeed increase the dynamic range at higher pulse rates, both the physiological data and the model indicate that much of the dynamic range for pulse-train stimuli is due to stochastic activity. Third, it is shown that the stochastic model is able to predict the general magnitude and behavior of variance in discharge rate as a function of pulse rate, while the deterministic model predicts no variance at all.

Auditory nerve model for predicting performance limits of normal and impaired listeners

A computational auditory nerve (AN) model was developed for use in modeling psychophysical experiments with normal and impaired human listeners. In this phenomenological model, many physiologically vulnerable response properties associated with the cochlear amplifier are represented by a single nonlinear control mechanism, including the effects of level-dependent tuning, compression, level-dependent phase, suppression, and fast nonlinear dynamics on the responses of high, medium, and low spontaneous-rate (SR) AN fibers. Several model versions are described that can be used to evaluate the relative effects of these nonlinear properties.

A spiking neuron model of cortical correlates of sensorineural hearing loss: spontaneous firing, synchrony, and tinnitus

Hearing loss due to peripheral damage is associated with cochlear hair cell damage or loss and some retrograde degeneration of auditory nerve fibers. Surviving auditory nerve fibers in the impaired region exhibit elevated and broadened frequency tuning, and the cochleotopic representation of broadband stimuli such as speech is distorted. In impaired cortical regions, increased tuning to frequencies near the edge of the hearing loss coupled with increased spontaneous and synchronous firing is observed. Tinnitus, an auditory percept in the absence of sensory input, may arise under these circumstances as a result of plastic reorganization in the auditory cortex. We present a spiking neuron model of auditory cortex that captures several key features of cortical organization. A key assumption in the model is that in response to reduced afferent excitatory input in the damaged region, a compensatory change in the connection strengths of lateral excitatory and inhibitory connections occurs. These changes allow the model to capture some of the cortical correlates of sensorineural hearing loss, including changes in spontaneous firing and synchrony; these phenomena may explain central tinnitus. This model may also be useful for evaluating procedures designed to segregate synchronous activity underlying tinnitus and for evaluating adaptive hearing devices that compensate for selective hearing loss.