NDLI: Comparison of neuronal network models for tinnitus management by sound therapy

Content Provider	IEEE Xplore Digital Library
Author	Nagashino, H. Kinouchi, Y. Danesh, A.A. Pandya, A.S.
Copyright Year	2009
Description	Author affiliation: Department of Biomedical Information Science, Institute of Health Biosciences, The University of Tokushima, Tokushima 770-8509 Japan (Nagashino, H.) \|\| Department of Biological Functions Engineering, Institute of Technology and Science, The University of Tokushima, Tokushima 770-8506 Japan (Kinouchi, Y.) \|\| Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431 USA (Pandya, A.S.) \|\| Department of Communications and Disorders, Florida Atlantic University, Boca Raton, FL 33431 USA (Danesh, A.A.)
Abstract	Tinnitus is a condition in which sounds heard in the ear or head without any external sound. There are many therapeutic approaches for tinnitus and sound therapy is one of the techniques for its treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy from the viewpoint of neural engineering, we have proposed computational models with plasticity and inhibitory feedback using a neural oscillator or model neurons described by simplified Hodgkin-Huxley equations. By hypothesizing that the oscillation and the non-oscillatory state in the models correspond to generation and inhibition of tinnitus, respectively, we found out that the models could explain the fact that the habituated human auditory system temporarily halts perception of tinnitus following sound therapy. However, a simpler model without inhibitory feedback can exhibit the solutions that exist in the former models. In the present paper, outcomes of the neuronal network model, which is incorporated with inhibitory feedback, are compared with the model without inhibitory feedback. It was revealed that the former is superior since it has a larger parameter region in which the effects of sound therapy can be restored due to synaptic plasticity.
Starting Page	1545
Ending Page	1548
File Size	867665
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332402
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological neural networks Medical treatment Neurofeedback Ear Neural engineering Computational modeling Oscillators Neurons Equations Humans
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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