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.

　大脳基底核は、神経生理学、神経解剖学、臨床神経学ばかりでなく、数理工学 からも注目を集めており、ホットな分野の一つです。様々な数理モデルが提唱さ れ、それによって大脳基底核の機能が明らかになりつつあります。今回のセミ ナーでは、Chakravarthy博士自身が提案した数理モデルによって、パーキンソン 病の病態を説明しようという試みについて紹介してもらいます。是非、御来聴下 さい。