Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit

Accumulating evidence shows that the neural network of the cerebral cortex and the basal ganglia is critically involved in reinforcement learning. Recent studies found functional heterogeneity within the cortico-basal ganglia circuit, especially in its ventromedial to dorsolateral axis. Here we review computational issues in reinforcement learning and propose a working hypothesis on how multiple reinforcement learning algorithms are implemented in the cortico-basal ganglia circuit using different representations of states, values, and actions.

► We review computational issues and possible algorithms for decision making. ► We review recent findings on the neural correlates of the variables in those algorithms. ► Then we propose a hypothesis about parallel and hierarchical modules in the striatum.