Laurel H. Carney1
Depts. of Biomedical Engineering and Neuroscience, University of Rochester, Rochester, New York, USA

The goal of this project is to explore representations of pitch using physiological models for auditory-nerve (AN) and midbrain (inferior colliculus, IC) neurons. An established model for ‘central pitch’ (Goldstein, 1973, JASA) requires a robust neural response profile that corresponds to the spectrum of a harmonic tone complex. Representations based on auditory-nerve excitation patterns (rate vs. place profiles) change with sound level and are not robust in background noise. However, f0-related fluctuations in AN responses are robust both across levels and in noise. These peripheral fluctuations ultimately influence responses of IC neurons, for which a key property is amplitude-modulation tuning. Because IC neurons are sensitive to slow fluctuations of their inputs, the fluctuation profiles set up in the periphery map into rate-profiles across IC neurons (Carney, 2018, JARO). Thus, the population responses of IC neurons provide the input required by central pitch models. Here, the representation of periodicity pitch by model midbrain neurons will be tested for several stimuli. Estimates of pitch discrimination thresholds and pitch strength can be made based on model responses. Importantly, the fluctuation profiles in AN responses depend upon inner-ear nonlinearities that are affected by sensorineural hearing loss (SNHL). Specifically, the ‘flattening’ of fluctuations near harmonics in tone complexes, or near spectral peaks of complex sounds, is reduced when cochlear amplification and/or inner-hair-cell sensitivity is reduced. Thus, SNHL reduces the contrast in fluctuation amplitudes across AN frequency channels, and diminishes this mechanism for coding features of complex sounds. Effects of SNHL on pitch discrimination and pitch strength can thus be studied in this physiological-modeling framework. This effort builds on the pitch-discrimination modeling work in Bianchi et al. (2018, JARO), but takes advantage of the central pitch model to map physiological-model responses into decision variables for pitch-related tasks.

Acknowledgements: This work is supported by NIH-NIDCD-R01-010813.