Phenomenological model of auditory nerve population responses to cochlear implant stimulation

Tabibi, Sonia; Boulet, Jason; Dillier, Norbert; Bruce, Ian

doi:10.1016/j.jneumeth.2021.109212

Cited by 7 publications

(6 citation statements)

References 69 publications

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“…Our approach has natural extensions for modeling sensorineural hearing loss and cochlear implants. The healthy auditory peripheral stage in our models could be altered to simulate hair cell loss 101 , cochlear neuropathy 102 , or electrical stimulation from a cochlear implant 103 to reveal their effects on auditory behavior. Optimizing models with different hearing loss etiologies could yield insights into the diverse behavioral outcomes of individuals with hearing loss.…”

Section: Discussionmentioning

confidence: 99%

Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing

Saddler,

McDermott

2024

Preprint

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Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub- millisecond precision, but its behavioral relevance is uncertain. To investigate the role of this temporal coding, we optimized machine learning models to perform real-world hearing tasks with simulated cochlear input. We asked how precise auditory nerve spike timing needed to be to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. Degrading phase locking produced task-dependent effects, revealing how the use of fine-grained temporal information reflects both ecological task demands and neural implementation constraints. The results link neural coding to perception and clarify conditions in which prostheses that fail to restore high-fidelity temporal coding could in principle restore near-normal hearing.

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Section: Discussionmentioning

confidence: 99%

Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing

Saddler,

McDermott

2024

Preprint

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“…The fundamental physiological mechanisms behind the refractory behavior of neurons are described in neurophysiological studies of which first ones date back over a century (Tait, 1910). Once a neuron has generated an action potential, its ion channels remain inactive for a while, preventing the neuron to be excited during the so-called absolute refractory period (ARP).…”

Section: Methodsmentioning

confidence: 99%

“…The threshold-crossing detector in the BLIF model searches for the time instant t 0 at which V(t) first exceeds the stochastic threshold value Θ(t). It should be noted that other models (e.g., Joshi et al, 2017;Tabibi et al, 2021) are using 1/f noise to more closely mimic fluctuations in cell-membrane potentials. A white Gaussian noise distribution was used by Horne et al (2016) as it yielded comparable results for clinically relevant pulse durations, is easier to generate and enabled using the same distribution for simulating the dependency of the jitter and latency on the spiking probability, as explained below.…”

Section: Blif Modelmentioning

confidence: 99%

A Phenomenological Model Reproducing Temporal Response Characteristics of an Electrically Stimulated Auditory Nerve Fiber

Takanen

Seeber

2022

Trends in Hearing

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The ability of cochlear implants (CIs) to restore hearing to profoundly deaf people is based on direct electrical stimulation of the auditory nerve fibers (ANFs). Still, CI users do not achieve as good hearing outcomes as their normal-hearing peers. The development and optimization of CI stimulation strategies to reduce that gap could benefit from computational models that can predict responses evoked by different stimulation patterns, particularly temporal responses for coding of temporal fine structure information. To that end, we present the sequential biphasic leaky integrate-and-fire (S-BLIF) model for the ANF response to various pulse shapes and temporal sequences. The phenomenological S-BLIF model is adapted from the earlier BLIF model that can reproduce neurophysiological single-fiber cat ANF data from single-pulse stimulations. It was extended with elements that simulate refractoriness, facilitation, accommodation and long-term adaptation by affecting the threshold value of the model momentarily after supra- and subthreshold stimulation. Evaluation of the model demonstrated that it can reproduce neurophysiological data from single neuron recordings involving temporal phenomena related to inter-pulse interactions. Specifically, data for refractoriness, facilitation, accommodation and spike-rate adaptation can be reproduced. In addition, the model can account for effects of pulse rate on the synchrony between the pulsatile input and the spike-train output. Consequently, the model offers a versatile tool for testing new coding strategies for, e.g., temporal fine structure using pseudo-monophasic pulses, and for estimating the status of the electrode-neuron interface in the CI user's cochlea.

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“…In contrast to the biophysical approach, phenomenological models do not rely on specific biophysical mechanisms and derive empirical relationships based on neurophysiological and psychophysical observations [141]. Due to the much-reduced parameter space, this approach allows the efficient modelling of complex phenomena that can be adjusted to individual CI patients and has proven effective at predicting and explaining a diverse range of auditory phenomena [139,141,142].…”

Section: Computational Modelsmentioning

confidence: 99%

Models of Cochlea Used in Cochlear Implant Research: A Review

et al. 2023

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As the first clinically translated machine-neural interface, cochlear implants (CI) have demonstrated much success in providing hearing to those with severe to profound hearing loss. Despite their clinical effectiveness, key drawbacks such as hearing damage, partly from insertion forces that arise during implantation, and current spread, which limits focussing ability, prevent wider CI eligibility. In this review, we provide an overview of the anatomical and physical properties of the cochlea as a resource to aid the development of accurate models to improve future CI treatments. We highlight the advancements in the development of various physical, animal, tissue engineering, and computational models of the cochlea and the need for such models, challenges in their use, and a perspective on their future directions.

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Phenomenological model of auditory nerve population responses to cochlear implant stimulation

Cited by 7 publications

References 69 publications

Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing

Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing

A Phenomenological Model Reproducing Temporal Response Characteristics of an Electrically Stimulated Auditory Nerve Fiber

Models of Cochlea Used in Cochlear Implant Research: A Review

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