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.
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