Supplement: Active and Nonlinear Models for Cochlear Mechanics

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PROJECT SUMMARY: Fluid flow stimulates the hair bundles (HB) of the inner hair cells (IHC) of the cochlea opening the mechano- electric transducer (MET) channels of the IHCs. The resulting current depolarizes the cell body inducing neurotransmitter release and, ultimately, auditory nerve stimulation. The active machinery of the cochlea, driven by motility of outer hair cells (OHC), both tunes the microfluidic excitation of the IHC HBs and provides for nonlinear compression. However, the relative influence of OHC somatic and HB motility on this final fluidic forcing in the cochlea has yet to be conclusively determined. The specific aims of the parent grant seek to develop mathematical models of these phenomena and rigorously test hypotheses of activity via comparison to existing experiments and work with our collaborators to devise feasible new experiments to test our predictions. This supplement aims to broaden the impact of this work by making a streamlined version of code used in our previous publications available for use and modification by the auditory computation community. We will do this by using open-source software platforms to host our code. This will enable the direct use of the code for simulations under different operating conditions and for modification and improvement of the code. We will publicize this activity through our website, publications, and other presentations. The overarching goal of this research is to develop a complete fluid-mechanical-electrical model that describes the response of the cochlea to external acoustic stimulation. If successful, this model will enhance our understanding of failure mechanisms in the cochlea, answering important questions as to which morphological elements of the cochlea fail and why. Further, this predictive code holds the promise to improve noninvasive diagnosis of auditory function because features of the cochlear response (such as otoacoustic emissions) can be linked to specific pathologies. Finally, having a predictive model over the entire audio spectrum will help us to understand how important classes of signals are processed in the cochlea (such as speech and music) and such understanding can lead to better speech processing algorithms or cochlear implant electrical stimulation approaches.