Quantifying the information in auditory-nerve responses for level discrimination

Abstract

An analytical approach for quantifying the information in auditory-nerve (AN) fiber responses for the task of level discrimination is described. A simple analytical model for AN responses is extended to include temporal response properties, including the nonlinear-phase effects of the cochlear amplifier. Use of simple analytical models for AN discharge patterns allows quantification of the contributions of level-dependent aspects of the patterns to level discrimination. Specifically, the individual and combined contributions of the information contained in discharge rate, synchrony, and relative phase cues are explicitly examined for level discrimination of tonal stimuli. It is shown that the rate information provided by individual AN fibers is more constrained by increases in variance with increases in rate than by saturation. As noted in previous studies, there is sufficient average-rate information within a narrow-CF region to account for robust behavioral performance over a wide dynamic range; however, there is no model based on a simple limitation or use of AN information consistent with parametric variations in performance. This issue is explored in the current study through analysis of performance based on different aspects of AN patterns. For example, we show that performance predicted from use of all rate information degrades significantly as level increases above low-medium levels, inconsistent with Weber's Law. At low frequencies, synchrony information extends the range over which behavioral performance can be explained by 10-15 dB, but only at low levels. In contrast to rate and synchrony, nonlinear-phase cues are shown to provide robust information at medium and high levels in near-CF fibers for low-frequency stimuli. The level dependence of the discharge rate and phase properties of AN fibers are influenced by the compressive nonlinearity of the inner ear. Evaluating the role of the compressive nonlinearity in level coding is important for understanding neural encoding mechanisms and because of its association with the cochlear amplifier, which is a fragile aspect of the ear believed to be affected in common forms of hearing impairment.