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Neural Networks and Parkinson's Disease

Published online by Cambridge University Press:  18 September 2015

Donald S. Borrett ,
Tet H. Yeap  and
Hon C. Kwan
Donald S. Borrett*
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
Tet H. Yeap
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
Hon C. Kwan
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
*
Department of Physiology, Medical Sciences Building, University of Toronto, Toronto, Ontario, Canada M5S 1A8
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Abstract:

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A closed-loop or recurrent neural network was taught to generate output discharges to reproduce the prototypical activations in agonist and antagonist muscles which produce the displacement of a limb about a single joint. By introducing a generalized decrease in the excitability of the pre-output layer in the network, the network made the displacement more slowly and also showed an inability to maintain a repetitive movement. These concepts can be applied to the human nervous system in the understanding of the physical basis of movement and its disorders. It is suggested that a movement represents the output of a closed-loop network, such as the cortical-basal ganglia-thalamic-cortical motor loop, which iterates repetitively to its end point or attractor. The model provides an explanation of how the state of thalamic inhibition seen in Parkinson's disease physically may produce bradykinesia and the inability to maintain a repetitive movement.

Type
Research Article
Information
Canadian Journal of Neurological Sciences , Volume 20 , Issue 2 , May 1993 , pp. 107 - 113
Copyright
Copyright © Canadian Neurological Sciences Federation 1993

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