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| Models | Description |
| A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009) | |
| "... Here, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. Analysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. This integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. ... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ..." | |
| Auditory nerve model for predicting performance limits (Heinz et al 2001) | |
| 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, see paper for details. Several model versions are described that can be used to evaluate the relative effects of these nonlinear properties. | |
| Auditory nerve model with linear tuning (Heinz et al 2001) | |
| A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. Please see paper for details. | |
| Auditory nerve response model (Tan, Carney 2003) | |
| A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing. | |
| Auditory nerve response model (Zhang et al 2001) | |
| A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. 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. | |
| Auditory nerve spontaneous rate histograms (Jackson and Carney 2005) | |
| Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence. | |
| Cat auditory nerve model (Zilany and Bruce 2006, 2007) | |
| "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 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." | |
| Cochlear implant models (Bruce et al. 1999a, b, c, 2000) | |
| "In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation." | |
| Encoding and discrimination of vowel-like sounds (Tan and Carney 2005) | |
| "The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as a function of the center frequency of the complex cannot be explained by changes in the level of the stimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943–1955 (1995)]. Rather, a complex pattern of sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when the center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope, greatest sensitivity occurs when the center frequency is aligned with a harmonic. In this study, the thresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to these trends in psychophysical thresholds. Single-fiber and population model responses were evaluated in terms of both average discharge rate and the combination of rate and timing information. ..." | |
| Long-term adaptation with power-law dynamics (Zilany et al. 2009) | |
| ... A model of rate adaptation at the synapse between inner hair cells and auditory-nerve (AN) fibers that includes both exponential and power-law dynamics is presented here. Exponentially adapting components with rapid and short-term time constants, which are mainly responsible for shaping onset responses, are followed by two parallel paths with power-law adaptation that provide slowly and rapidly adapting responses. ... The proposed model is capable of accurately predicting several sets of AN data, including amplitude-modulation transfer functions, long-term adaptation, forward masking, and adaptation to increments and decrements in the amplitude of an ongoing stimulus. | |
| Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004) | |
| "A phenomenological model with time-varying excitation and inhibition was developed to study possible neural mechanisms underlying changes in the representation of temporal envelopes along the auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J. Acoust. Soc. Am. 109, 648–670 (2001) was used to provide inputs to higher hypothetical processing centers. Model responses were compared directly to published physiological data at three levels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus. ..." | |
| Models for diotic and dichotic detection (Davidson et al. 2009) | |
| Several psychophysical models for masked detection were evaluated using reproducible noises. The data were hit and false-alarm rates from three psychophysical studies of detection of 500-Hz tones in reproducible noise under diotic (N0S0) and dichotic (N0Spi) conditions with four stimulus bandwidths (50, 100, 115, and 2900 Hz). Diotic data were best predicted by an energy-based multiple-detector model that linearly combined stimulus energies at the outputs of several critical-band filters. The tone-plus-noise trials in the dichotic data were best predicted by models that linearly combined either the average values or the standard deviations of interaural time and level differences; however, these models offered no predictions for noise-alone responses. ...". The Breebart et al. 2001 and the Dau et al. 1996 models are supplied at the Carney lab web site. | |
| Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012) | |
| A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. | |
| Predicting formant-frequency discrimination in noise (Tan and Carney 2006) | |
| "To better understand how the auditory system extracts speech signals in the presence of noise, discrimination thresholds for the second formant frequency were predicted with simulations of auditory-nerve responses. These predictions employed either average-rate information or combined rate and timing information, and either populations of model fibers tuned across a wide range of frequencies or a subset of fibers tuned to a restricted frequency range. In general, combined temporal and rate information for a small population of model fibers tuned near the formant frequency was most successful in replicating the trends reported in behavioral data for formant-frequency discrimination. ..." | |
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