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

Abstract

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.

Frequency selectivity in Old-World monkeys corroborates sharp cochlear tuning in humans

“…For example, the neural bases for human perceptual abilities, such as frequency or vowel discrimination, over a large range of intensities are poorly understood and much debated (45,46), but quantitative models are invariably based on the frequency selectivity measured in other animals. The debate as to whether cochlear frequency selectivity is insufficiently sharp and needs to be complemented with a temporal code to operate over the greater than 100-dB range of intensities over which humans hear is fundamental to the understanding of the effects of cochlear pathology, and thus for developing algorithms for auditory prostheses to relieve hearing loss and deafness.…”

Predictions of formant-frequency discrimination in noise based on model auditory-nerve responses

“…(1)-(3) and using the description of sensitivity based on a single AN fiber rate function [e.g., Siebert (1970); see Heinz et al (2001a) for derivation], the normalized sensitivity squared for the AN model population is [Eq. 5.34 in Heinz (2000)] (4) where M is the total number of fibers in the population; r i (t|f,w) is the response of the ith AN model fiber to the wth vowel-plus-masker with the second formant frequency, f. T is the duration of the stimulus waveform.…”

“…Siebert (1965Siebert ( ,1968) developed a strategy for predicting limits of level discrimination for pure tones based on a very simple description of the auditory periphery. Heinz et al (2001a) extended Siebert's approach to allow the use of recent computational models for AN responses. Since both approaches include analyzing population responses and combining information across fibers tuned to different frequencies, they provide appropriate tools to study formant-frequency encoding.…”

Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of Auditory Nerve Responses

“…The tonotopic arrangement of hair cells and primary afferents allows sound frequency to be encoded using the number of spikes by a rate-place code. Alternatively, some sound frequencies can be encoded using the precise timing of spikes by phase-locking to the stimulus waveform (Kiang, 1965;Rose et al, 1967;Heinz et al, 2001). Furthermore, firing rate, synchronization, and phase cues can all encode changes in sound level (Kiang, 1965;Anderson et al, 1971;Johnson, 1980;Colburn et al, 2003).…”

“…Furthermore, firing rate, synchronization, and phase cues can all encode changes in sound level (Kiang, 1965;Anderson et al, 1971;Johnson, 1980;Colburn et al, 2003). As in all other sensory systems, trial-to-trial variability in spike count and timing imposes limitations on discrimination performance (Heinz et al, 2001).…”