Updated parameters and expanded simulation options for a model of the auditory periphery

Abstract

A phenomenological model of the auditory periphery in cats was previously developed by Zilany and colleagues [J. Acoust. Soc. Am. 126, 2390-2412 (2009)] to examine the detailed transformation of acoustic signals into the auditory-nerve representation. In this paper, a few issues arising from the responses of the previous version have been addressed. The parameters of the synapse model have been readjusted to better simulate reported physiological discharge rates at saturation for higher characteristic frequencies [Liberman, J. Acoust. Soc. Am. 63, 442-455 (1978)]. This modification also corrects the responses of higher-characteristic frequency (CF) model fibers to low-frequency tones that were erroneously much higher than the responses of low-CF model fibers in the previous version. In addition, an analytical method has been implemented to compute the mean discharge rate and variance from the model's synapse output that takes into account the effects of absolute refractoriness.

Midbrain Synchrony to Envelope Structure Supports Behavioral Sensitivity to Single-Formant Vowel-Like Sounds in Noise

“…The contribution of band-enhanced modulation tuning in the IC to average-rate coding of vowel-like sounds was further supported by the modeling results. Phenomenological models of the mammalian auditory nerve and IC (Zilany et al 2014;Mao and Carney 2015) were able to reproduce the averagerate representation of formant frequency observed in our IC recordings, without any adjustment of model parameters, from a primarily synchrony-based representation in the peripheral input stage. Bandenhanced modulation tuning in this implementation of the IC model arises from simple band-pass filtering of the time-varying instantaneous discharge rate of a model auditory nerve fiber, but equivalently, could arise through putative fast excitation coupled with stronger, delayed inhibition from lemniscal input fibers with similar BFs (Nelson and Carney 2004;Nelson and Carney 2007;Mao and Carney 2015).…”

“…The auditory nerve model (Zilany et al 2014) has been rigorously tested against neurophysiological responses to a broad range of stimuli including tones, broadband noise, amplitude-modulated tones, and synthetic vowels. The model captures many of the non-linear response properties of auditory nerve fibers including compression, suppression, and increases in tuning bandwidth with sound level.…”

“…Phenomenological models of the auditory nerve (Zilany et al 2014) and band-enhanced modulation tuning in the IC (Mao and Carney 2015) were used to gain insight into the contribution of rate-based modulation tuning to neural coding of formant frequency. Model auditory nerve fibers with characteristic frequencies near 2 kHz showed subtle fluctuations in average discharge rate with increasing formant frequency in quiet (Fig.…”

Competition Between Cortical Ensembles Explains Pitch-Related Dynamics of Auditory Evoked Fields

“…Stimuli presenting fundamental or effective frequencies under f 0 = 150 Hz yielded an overly late predicted N100m. This is due to intrinsic limitations of the peripheral model, that does not present cochlear channels solving frequencies under f = 125 Hz [14]. Stimuli with f 0 > 2000 Hz failed to yield satisfactory perceptual outputs, as a reflection of the limit for phase-locking in the peripheral auditory system [14].…”

“…Subcortical input was simulated using a realistic model of the peripheral auditory system generating realistic auditory nerve spike trains [14] followed by a delayand-multiply processing carried out by chopper neurons in cochlear nucleus and coincidence detector units in the inferior colliculus [6]. The spike trains generated by the peripheral system, represented by the probability of spiking p(t), are phase-locked to the waveform of the stimulus, thus preserving all the periodicities of the sound.…”