Implementation of a Model of the Cortex Basal Ganglia Loop

TL;DR

This paper introduces a simple, implementable model of the cortex-basal ganglia-thalamus loop, highlighting its role in action prediction and reinforcement learning, useful for brain and cognitive architecture modeling.

Contribution

It presents a novel, simplified computational model of the cortex-basal ganglia loop based on action prediction and reinforcement learning principles.

Findings

The model successfully simulates action selection processes.

Implementation demonstrates the loop's role in reinforcement learning.

Potential integration with cortical and cognitive models.

Abstract

This article presents a simple model of the cortex-basal ganglia-thalamus loop, which is thought to serve for action selection and executions, and reports the results of its implementation. The model is based on the hypothesis that the cerebral cortex predicts actions, while the basal ganglia use reinforcement learning to decide whether to perform the actions predicted by the cortex. The implementation is intended to be used as a component of models of the brain consisting of cortical regions or brain-inspired cognitive architectures.