Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importanc...

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Autores principales:	Xiao Gao, David Grayden, Mark McDonnell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/577ae60e14804fa9b01fc7590228d3b2
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id	oai:doaj.org-article:577ae60e14804fa9b01fc7590228d3b2
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spelling	oai:doaj.org-article:577ae60e14804fa9b01fc7590228d3b22021-12-02T20:14:28ZUnifying information theory and machine learning in a model of electrode discrimination in cochlear implants.1932-620310.1371/journal.pone.0257568https://doaj.org/article/577ae60e14804fa9b01fc7590228d3b22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257568https://doaj.org/toc/1932-6203Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.Xiao GaoDavid GraydenMark McDonnellPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257568 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiao Gao David Grayden Mark McDonnell Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
description	Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.
format	article
author	Xiao Gao David Grayden Mark McDonnell
author_facet	Xiao Gao David Grayden Mark McDonnell
author_sort	Xiao Gao
title	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
title_short	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
title_full	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
title_fullStr	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
title_full_unstemmed	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
title_sort	unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/577ae60e14804fa9b01fc7590228d3b2
work_keys_str_mv	AT xiaogao unifyinginformationtheoryandmachinelearninginamodelofelectrodediscriminationincochlearimplants AT davidgrayden unifyinginformationtheoryandmachinelearninginamodelofelectrodediscriminationincochlearimplants AT markmcdonnell unifyinginformationtheoryandmachinelearninginamodelofelectrodediscriminationincochlearimplants
_version_	1718374644965179392

Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.

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