title: The role of the basal ganglia in exploration in a neural model based on reinforcement learning.
abstract: We present a computational model of basal ganglia as a key player in exploratory behavior. The model describes exploration of a virtual rat in a simulated water pool experiment. The virtual rat is trained using a reward-based or reinforcement learning paradigm which requires units with stochastic behavior for exploration of the system's state space. We model the Subthalamic Nucleus-Globus Pallidus externa (STN-GPe) segment of the basal ganglia as a pair of neuronal layers with oscillatory dynamics, exhibiting a variety of dynamic regimes such as chaos, traveling waves and clustering. Invoking the property of chaotic systems to explore state-space, we suggest that the complex exploratory dynamics of STN-GPe system in conjunction with dopamine-based reward signaling from the Substantia Nigra pars compacta (SNc) present the two key ingredients of a reinforcement learning system.
authors: Program in Neuroscience, Stanford University School of Medicine, California 94305, USA. dsridhar@stanford.edu
authors: D
authors: D
authors: Sridharan
authors: P S
authors: PS
authors: Prashanth
authors: V S
authors: VS
authors: Chakravarthy