Neural Networks and Parkinson's Disease

Abstract

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.