A point process framework for modeling electrical stimulation of the auditory nerve

Abstract

Model-based studies of responses of auditory nerve fibers to electrical stimulation can provide insight into the functioning of cochlear implants. Ideally, these studies can identify limitations in sound processing strategies and lead to improved methods for providing sound information to cochlear implant users. To accomplish this, models must accurately describe spiking activity while avoiding excessive complexity that would preclude large-scale simulations of populations of auditory nerve fibers and obscure insight into the mechanisms that influence neural encoding of sound information. In this spirit, we develop a point process model of individual auditory nerve fibers that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the proposed model consists of a cascade of linear and nonlinear stages. We show how each of these stages can be associated with biophysical mechanisms and related to models of neuronal dynamics. Moreover, we derive a semianalytical procedure that uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. Throughout, we compare model predictions to published physiological data of response to high and low pulse rate stimulation. We find that the model, although constructed to fit data from single and paired pulse experiments, can accurately predict responses to unmodulated and modulated pulse train stimuli. We close by performing an ideal observer analysis of simulated spike trains in response to sinusoidally amplitude modulated stimuli and find that carrier pulse rate does not affect modulation detection thresholds.

A Phenomenological Model of the Electrically Stimulated Auditory Nerve Fiber: Temporal and Biphasic Response Properties

“…It is not possible to parameterize the SLIF neuron to reproduce these temporal response properties whilst simultaneously maintaining the input-output and strength-duration functions that have already been fitted to data from cat ANFs. These failings of the SLIF neuron have been noted previously (Hamacher, 2004 ; Fredelake and Hohmann, 2012 ; Goldwyn et al, 2012 ) and are addressed by our extension to the SLIF neuron in Section Temporal Leaky Integrate and Fire Neuron.…”

“…Phenomenological models have previously been used to model the electrically stimulated ANF (e.g., Bruce et al, 1999 ; Hamacher, 2004 ; Carlyon et al, 2005 ; Chen and Zhang, 2007 ; Macherey et al, 2007 ; Cohen, 2009a , b , c , d ; Chen, 2012 ; Goldwyn et al, 2012 ). The required constraints on these models are different to those of other domains.…”

A Model of Electrically Stimulated Auditory Nerve Fiber Responses with Peripheral and Central Sites of Spike Generation

“…Only Goldwyn et al (2012) used the summation threshold time constants to model facilitation, enabling their model to predict temporal integration effects across pulse trains. Nevertheless, their model did not include any effects of accommodation and therefore cannot account for accommodation observed in spike responses at different pulse rates.…”

“…Goldwyn et al 2012). The strength-duration function represents the neural firing response threshold as a function of the pulse duration and can be described using the two parameters rheobase and chronaxie.…”

Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model

“…Channel noise has also been considered an important factor for consideration in how auditory neurons encode information. This has led to the suggestion of utilizing channel noise to reproduce more natural responses in populations of neurons stimulated by cochlear implants [15,[34][35][36]. Channel noise is particularly important in this example as it is likely to be the most significant biophysical source of stochastic noise in the peripheral auditory system following hearing loss caused by the loss of inner hair cells [15].…”