Cochlear implants are electronic devices intended for restoring hearing to the profoundly deaf. Unfortunately the degree of restored hearing varies greatly between subjects. To investigate some of the mechanisms that determine this variability, mathematical models of the auditory system are used. The level of detail that these models incorporate varies greatly. The present study describes the development of a method to create high detail, subject specific cochlea models. μ-CT scans and photomicrographs were used to obtain the morphology and histology of a specific guinea pig cochlea. A 3D model was constructed from this data and the finite element method was used to determine the potential distribution inside the cochlea. The potential distribution was calculated for different stimulus protocols applied to different modelled electrodes. A neuron model was then used to obtain neural excitation profiles. The modelled excitation profiles were compared to data from literature and it was found that this model is valid and can be used as a tool in electric hearing research. The model output was also compared to brainstem response data from the specific subject to assess the degree to which this model can predict brain stem data from a specific subject. Possible improvements to the model were also discussed.
Dissertation (MEng)--University of Pretoria, 2011.
Since the emergence of systematic science it has been recognized that a natural phenomenon can be described
by different models that vary in their complexity and their ability to capture the details of the features
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