A neural-counting model incorporating refractoriness and spread of excitation. I. Application to intensity discrimination

Teich, Malvin C.; Lachs, Gerard

doi:10.1121/1.383647

Cited by 31 publications

(25 citation statements)

References 19 publications

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“…Similar deviations from the power law have been observed at low SLs by several authors (e.g.,Luce and Green, 1974;Rabinowitz et al, 1976; Zwislocki and Jordan, 1986). Indeed from near threshold to 90 dB SL, the value of iX//I is likely to decrease by a factor of 6 or more, in agreement with the"exact" near-miss law and the predictions of the extended version of the counting model(Teich and Lachs, 1979;Lachs et al, 1984). Furthermore, without any additional modeling considerations, the steeper function obtained from the "exact" relation is more consistent with the excitation-pattern model than the firstorder near miss.…”

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confidence: 75%

Limitations of first-order approximations for calculations using intensity jnd’s

Hellman

1986

The Journal of the Acoustical Society of America

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The way in which the noninfinitesimal size of the intensity jnd affects calculations using power functions for pure-tone loudness and neural count is examined. The results of the commonly used first-order differential for representing the change in the power functions due to a jnd in intensity are compared with the second-order approximation and the exact formula. Examples are taken from a variety of psychophysical studies dealing with loudness and intensity discrimination. The second-order and exact formulas are shown to be more consistent with experimental data than the first-order differential.

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supporting

confidence: 75%

Limitations of first-order approximations for calculations using intensity jnd’s

Hellman

1986

The Journal of the Acoustical Society of America

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“…Potentially signi cant differences between the computational AN model and Siebert's (1965Siebert's ( , 1968 analytical model include (1) the use of gamma-tone lters rather than triangular lters, (2) the form of the variation in lter bandwidth with CF, and (3) the distribution of CF across the population of AN bers. Our results, and those of Teich and Lachs (1979), demonstrate that the near-miss does not require Weber's law to hold in narrow frequency regions (an assumption that is often made in psychophysical models; e.g., Florentine & Buus, 1981). However, the simpli ed AN model used in this study has several signi cant limitations that preclude any de nitive conclusions regarding level encoding.…”

Section: Encoding Of Levelmentioning

confidence: 47%

Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

2001

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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. The use of computational models allows predictions to be made over a wider parameter range and with more complete descriptions of AN responses than in analytical models. Performance based on AN discharge times (all-information) is compared to performance based only on discharge counts (rate-place). After the method is verified over the range of parameters for which previous analytical models are applicable, the parameter space is then extended. For example, a computational model of AN activity that extends to high frequencies is used to explore the common belief that rate-place information is responsible for frequency encoding at high frequencies due to the rolloff in AN phase locking above 2 kHz. This rolloff is thought to eliminate temporal information at high frequencies. Contrary to this belief, results of this analysis show that rate-place predictions for frequency discrimination are inconsistent with human performance in the dependence on frequency for high frequencies and that there is significant temporal information in the AN up to at least 10 kHz. In fact, the all-information predictions match the functional dependence of human performance on frequency, although optimal performance is much better than human performance. The use of computational AN models in this study provides new constraints on hypotheses of neural encoding of frequency in the auditory system; however, the method is limited to simple tasks with deterministic stimuli. A companion article in this issue ("Evaluating Auditory Performance Limits: II") describes an extension of this approach to more complex tasks that include random variation of one parameter, for example, random-level variation, which is often used in psychophysics to test neural encoding hypotheses.

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“…The model differs principally from the one developed in [1]- [3] in that it includes a memoryless receptor saturation function and spontaneous events, as well as the outer-and middle-ear transmission function and the cochlear mapping function across channels. As indicated in Section I, the model considered in [4] is a single-channel version of the system shown in Fig.…”

Section: Elements Of the Modelmentioning

confidence: 99%

“…1 has been presented in detail in [4]; the formulas are presented here with a minimum of discussion. The inner-ear multiple-pole tuning mechanism is taken to be a linear filter whose response to a pure-tone input at frequency IT is given (see [1]) by…”

Section: A Single-channel Elementsmentioning

confidence: 99%

“…W E DEMONSTRATED in Part I [1] of this series of papers ([1]- [4]) that pure-tone intensity discrimination could be satisfactorily described in terms of an energy-based neural-counting model incorporating refractoriness and spread of excitation. In Part II [2] we showed that the same linear filter refractoriness model (LFRM) also provided satisfactory results for fitting data obtained in pure-tone loudness estimation experiments.…”

Section: Introductionmentioning

confidence: 99%

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A neural-counting model based on physiological characteristics of the peripheral auditory system. V. Application to loudness estimation and intensity discrimination

Lachs

Al‐Shaikh

et al. 1984

IEEE Trans. Syst., Man, Cybern.

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s-The psychophysical properties of a multiple-channel neuralcounting model are investigated. Each channel represents. a peripheral afferent fiber (or a group of such fibers) and consists of a cascade of signal-processing transformations, each of which has a physiological correlate in the auditory system. The acoustic signal (which may be a pure tone or Gaussian noise) is passed by our mathematical construct through the following series of transformations: an outer-and middle-ear transmission function, an inner-ear multiple-pole linear-filter tuning mechanism, a nonlinear receptor saturation function, and a refractoriness-modified Poisson transduction mechanism (which leads to a sub-Poisson neural spike count). Spontaneous neural activity is independently incorporated into each channel by means of an additive refractoriness-modified Poisson process. A union process at a more distal center in the nervous system is generated by a parallel collection of such channels with a density (in frequency) determined by the cochlear mapping function. The statistics of the union count (in a fixed time) are then processed at a decision center in a manner that depends on the psychophysical paradigm under consideration. This random count number is assumed to contain all of the information for the examples we consider. Our model has been used to calculate psychophysical functions for the following paradigms: pure-tone loudness estimation, pure-tone and variable-bandwidth noise intensity discrimination, and variable-bandwidth noise loudness summation. The theoretical results, which are determined in large part by spread of excitation, are in good agreement with human psychophysical data, provided that the parameters of the theoretical model are appropriately chosen. It has been found that a suitable choice of parameters is both physiologically sensible and self-consistent. As a further indication of the consistency of the model, the same general parametric dependencies as neurophysiological isointensity contours for peripheral afferent fibers in the squirrel monkey are exhibited by the single-channel theoretical count mean, which is calculated as a function Manuscript .of stimulus level and frequency. The single-channel count mean-to-variance ratio is in accord with laboratory data. Finally, the roles of the various components comprising our theoretical system are discussed, and our model is compared with related constructs.

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A neural-counting model incorporating refractoriness and spread of excitation. I. Application to intensity discrimination

Cited by 31 publications

References 19 publications

Limitations of first-order approximations for calculations using intensity jnd’s

Limitations of first-order approximations for calculations using intensity jnd’s

Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

A neural-counting model based on physiological characteristics of the peripheral auditory system. V. Application to loudness estimation and intensity discrimination

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