Modeling the Time-Varying and Level-Dependent Effects of the Medial Olivocochlear Reflex in Auditory Nerve Responses
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- 作者：Christopher J. Smalt (1)
Michael G. Heinz (2) (3)
Elizabeth A. Strickland (2) - 关键词：time ; varying efferent feedback ; cochlear gain reduction ; outer hair cell ; binaural ; efferent model ; listening in noise ; computational model
- 刊名：JARO - Journal of the Association for Research in Otolaryngology
- 出版年：2014
- 出版时间：April 2014
- 年：2014
- 卷：15
- 期：2
- 页码：159-173
- 全文大小：1,251 KB
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Michael G. Heinz (2) (3)
Elizabeth A. Strickland (2)
1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
2. Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA
3. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA - ISSN：1438-7573
文摘
The medial olivocochlear reflex (MOCR) has been hypothesized to provide benefit for listening in noisy environments. This advantage can be attributed to a feedback mechanism that suppresses auditory nerve (AN) firing in continuous background noise, resulting in increased sensitivity to a tone or speech. MOC neurons synapse on outer hair cells (OHCs), and their activity effectively reduces cochlear gain. The computational model developed in this study implements the time-varying, characteristic frequency (CF) and level-dependent effects of the MOCR within the framework of a well-established model for normal and hearing-impaired AN responses. A second-order linear system was used to model the time-course of the MOCR using physiological data in humans. The stimulus-level-dependent parameters of the efferent pathway were estimated by fitting AN sensitivity derived from responses in decerebrate cats using a tone-in-noise paradigm. The resulting model uses a binaural, time-varying, CF-dependent, level-dependent OHC gain reduction for both ipsilateral and contralateral stimuli that improves detection of a tone in noise, similarly to recorded AN responses. The MOCR may be important for speech recognition in continuous background noise as well as for protection from acoustic trauma. Further study of this model and its efferent feedback loop may improve our understanding of the effects of sensorineural hearing loss in noisy situations, a condition in which hearing aids currently struggle to restore normal speech perception.