title: Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers.
abstract: Cochlear implants, also called bionic ears, are implanted neural prostheses that can restore lost human hearing function by direct electrical stimulation of auditory nerve fibers. Previously, an information-theoretic framework for numerically estimating the optimal number of electrodes in cochlear implants has been devised. This approach relies on a model of stochastic action potential generation and a discrete memoryless channel model of the interface between the array of electrodes and the auditory nerve fibers. Using these models, the stochastic information transfer from cochlear implant electrodes to auditory nerve fibers is estimated from the mutual information between channel inputs (the locations of electrodes) and channel outputs (the set of electrode-activated nerve fibers). Here we describe a revised model of the channel output in the framework that avoids the side effects caused by an "ambiguity state" in the original model and also makes fewer assumptions about perceptual processing in the brain. A detailed comparison of how different assumptions on fibers and current spread modes impact on the information transfer in the original model and in the revised model is presented. We also mathematically derive an upper bound on the mutual information in the revised model, which becomes tighter as the number of electrodes increases. We found that the revised model leads to a significantly larger maximum mutual information and corresponding number of electrodes compared with the original model and conclude that the assumptions made in this part of the modeling framework are crucial to the model's overall utility.
authors.affiliation: Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, University of South Australia, SA 5095, Australia.
authors.forename: Xiao
authors.initials: X
authors.lastname: Gao
authors.affiliation: Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, University of South Australia, SA 5095, Australia and NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering and the Centre for Neural Engineering, University of Melbourne, VIC 3010, Australia.
authors.forename: David B
authors.initials: DB
authors.lastname: Grayden
authors.affiliation: Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, University of South Australia, SA 5095, Australia.
authors.forename: Mark D
authors.initials: MD
authors.lastname: McDonnell
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D003054:Cochlear Implants; D003056:Cochlear Nerve / Q000502:physiology*; D007257:Information Theory; D008959:Models, Neurological; D013269:Stochastic Processes