Estimation of Cochlear Frequency Selectivity Using a Convolution Model of Forward-Masked Compound Action Potentials

Abstract

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward-masking have been suggested as an alternative means to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAPs. In this work, we seek to provide a direct estimate of the quality factor characterizing AN frequency tuning using many forward-masked CAP responses. The method is based on a convolution model of the CAP that takes into account the masking of AN populations induced by notched-noise maskers with various notch widths and attenuations. The model produces masking patterns that, once convolved by a unitary response, predict forward-masked CAP waveforms. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across the different masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN-fiber tuning curves, without the need for an empirical correction factor. Beyond the estimation of frequency selectivity, the proposed model proves to be accurate in predicting forward-masked CAP responses, and therefore could be extended to study more complex aspects of cochlear signal processing using a similar experimental approach.