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Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.
PLOS ONE ( IF 2.9 ) Pub Date : 2021-09-20 , DOI: 10.1371/journal.pone.0257568
Xiao Gao 1, 2 , David Grayden 1 , Mark McDonnell 3
Affiliation  

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.

中文翻译:

在人工耳蜗电极辨别模型中统一信息理论和机器学习。

尽管几十年来人工耳蜗的发展和成功,但报告的语音感知的受试者间差异很大。这表明人工耳蜗用户依赖因素限制了个人层面的言语感知。临床研究已经证明了电极的数量、位置和插入深度对语音识别能力的重要性。然而,这些并不能解释所有受试者间的可变性,并且这些因素在多大程度上影响语音识别能力尚未得到研究。在本文中,将信息论方法和机器学习技术结合在一个模型中,以研究限制人工耳蜗电极辨别的关键因素的程度。该框架使用神经网络分类器来预测针对听觉神经的给定模拟激活模式刺激哪个电极,然后估计实际刺激电极和预测电极之间的互信息。我们还研究了参数的选择如何以及在多大程度上影响模型的性能。该框架的优点包括 i) 使用信息理论量化电极辨别能力,ii) 它提供了一个灵活的框架,可用于研究限制人工耳蜗使用者性能的关键因素,以及 iii) 它为未来提供了见解其他类型神经假体的建模研究。我们还研究了参数的选择如何以及在多大程度上影响模型的性能。该框架的优点包括 i) 使用信息理论量化电极辨别能力,ii) 它提供了一个灵活的框架,可用于研究限制人工耳蜗使用者性能的关键因素,以及 iii) 它为未来提供了见解其他类型神经假体的建模研究。我们还研究了参数的选择如何以及在多大程度上影响模型的性能。该框架的优点包括 i) 使用信息理论量化电极辨别能力,ii) 它提供了一个灵活的框架,可用于研究限制人工耳蜗使用者性能的关键因素,以及 iii) 它为未来提供了见解其他类型神经假体的建模研究。
更新日期:2021-09-20
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