A composite model of the auditory periphery for simulating responses to complex sounds

Robert, Arnaud; Eriksson, Jan

doi:10.1121/1.427935

Cited by 40 publications

(14 citation statements)

References 38 publications

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“…There are now computational AN models (Payton, 1988;Carney, 1993;Giguère & Woodland, 1994a, 1994bPatterson, Allerhand, & Giguère, 1995;Robert & Eriksson, 1999;Zhang, Heinz, Bruce, & Carney, 2001) that can provide more accurate physiological responses over a wider range of stimulus parameters than the analytical models already described. In addition, computational models can simulate AN responses to arbitrary stimuli and thus can be used to study a wider range of psychophysical tasks, such as those involving noise maskers.…”

Section: Combining Computational Models With Signal Detection The-mentioning

confidence: 97%

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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Section: Combining Computational Models With Signal Detection The-mentioning

confidence: 97%

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

2001

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“…In this regard, several efforts had been made to develop computational models ͑Deng and Geisler, 1987;Goldstein, 1990;Kates, 1991;Carney, 1993;Kates, 1995;Robert and Eriksson, 1999;Zhang et al, 2001;Meddis et al, 2001;Bruce et al, 2003;Sumner et al, 2003;Tan and Carney, 2003͒ that integrate data and theories from a wide range of research in the cochlea. Describing and understanding the mechanisms behind the nonlinearities in the cochlea such as compression, two-tone rate suppression, etc., was the main focus of these modeling efforts.…”

Section: Introductionmentioning

confidence: 99%

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

Zilany

Bruce

2006

The Journal of the Acoustical Society of America

185

154

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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 Kiang's 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.

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“…The detailed characteristics of this model have been described elsewhere [26]. Briefly, SPPM consists of an array of filters simulating the response along the basilar membrane of the cochlea, which provide inputs to an array of stochastic spiking neurons, referred to as AN units, representing the inner hair cells and auditory nerve (AN).…”

Section: Neural Network Modelmentioning

confidence: 99%

Artificial Neural Networks simulation of learning of auditory equivalence classes for vowels

Eriksson

Villa²

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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In a series of behavioral experiments rats were trained to discriminate between synthetic vowels characterized by an increase in fundamental frequency correlated with an upward shift in formant frequencies. The results demonstrate that rats are able to generalize the discrimination to new instances of the same vowels and that the performance depended on the relation between fundamental and formant frequencies that they had previously been exposed to. Simulation results using artificial neural networks could reproduce most of the behavioral results and suggest that equivalence classes for vowels are associated with an experience-driven process based on general properties of peripheral auditory coding mixed with elementary learning mechanisms.

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A composite model of the auditory periphery for simulating responses to complex sounds

Cited by 40 publications

References 38 publications

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

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

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

Artificial Neural Networks simulation of learning of auditory equivalence classes for vowels

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