A closed-loop or recurrent neural network was taught to generate outpu
t discharges to reproduce the prototypical activations in agonist and
antagonist muscles which produce the displacement of a limb about a si
ngle joint. By introducing a generalized decrease in the excitability
of the pre-output layer in the network, the network made the displacem
ent 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-lo
op 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 th
e inability to maintain a repetitive movement.