Implementation of a Model of the Cortex Basal Ganglia Loop
This is an incremental contribution for researchers in computational neuroscience or brain-inspired AI, as it provides a component for larger brain models without demonstrating new capabilities.
The authors tackled the problem of modeling the cortex-basal ganglia-thalamus loop for action selection by implementing a simple model based on reinforcement learning, but no concrete results or numbers are reported.
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.