A predictive model for artificial mechanical cochlea

Riaz U. Ahmed; Afifa Adiba; Sourav Banerjee

doi:10.1117/12.2084769

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26 March 2015 A predictive model for artificial mechanical cochlea

Riaz U. Ahmed, Afifa Adiba, Sourav Banerjee

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Proceedings Volume 9429, Bioinspiration, Biomimetics, and Bioreplication 2015; 94290K (2015) https://doi.org/10.1117/12.2084769
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2015, San Diego, California, United States

Abstract

To recover the hearing deficiency, cochlea implantation is essential if the inner ear is damaged. Existing implantable cochlea is an electronic device, usually placed outside the ear to receive sound from environment, convert into electric impulses and send to auditory nerve bypassing the damaged cochlea. However, due to growing demand researchers are interested in fabricating artificial mechanical cochlea to overcome the limitations of electronic cochlea. Only a hand full number of research have been published in last couple of years showing fabrication of basilar membrane mimicking the cochlear operations. Basilar membrane plays the most important role in a human cochlea by responding only on sonic frequencies using its varying material properties from basal to apical end. Only few sonic frequencies have been sensed with the proposed models; however no process was discussed on how the frequency selectivity of the models can be improved to sense the entire sonic frequency range. Thus, we argue that a predictive model is the missing-link and is the utmost necessity. Hence, in this study, we intend to develop a multi-scale predictive model for basilar membrane such that sensing potential of the artificial cochlea can be designed and tuned predictively by altering the model parameters.

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Riaz U. Ahmed, Afifa Adiba, and Sourav Banerjee "A predictive model for artificial mechanical cochlea", Proc. SPIE 9429, Bioinspiration, Biomimetics, and Bioreplication 2015, 94290K (26 March 2015); https://doi.org/10.1117/12.2084769

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