  We present a computational model that highlights the role of basal ganglia
(BG) in generating simple reaching movements. The model is cast within the
reinforcement learning (RL) framework with the correspondence between RL
components and neuroanatomy as follows: dopamine signal of substantia nigra
pars compacta as the Temporal Difference error, striatum as the substrate for
the Critic, and the motor cortex as the Actor. A key feature of this
neurobiological interpretation is our hypothesis that the indirect pathway is
the Explorer. Chaotic activity, originating from the indirect pathway part of
the model, drives the wandering, exploratory movements of the arm. Thus the
direct pathway subserves exploitation while the indirect pathway subserves
exploration. The motor cortex becomes more and more independent of the
corrective influence of BG, as training progresses. Reaching trajectories show
diminishing variability with training. Reaching movements associated with
Parkinson's disease (PD) are simulated by (a) reducing dopamine and (b)
degrading the complexity of indirect pathway dynamics by switching it from
chaotic to periodic behavior. Under the simulated PD conditions, the arm
exhibits PD motor symptoms like tremor, bradykinesia and undershoot. The model
echoes the notion that PD is a dynamical disease.
