A Biologically Plausible Architecture of the Striatum to Solve Context-Dependent Reinforcement Learning Tasks

Abstract

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks.