Topic: cerebellar function

We propose a novel dual-model framework that integrates reinforcement learning (RL) and adaptive control to model cerebellar function in motor learning and adaptation. Our approach distinguishes between the roles of the basal ganglia and cerebellum, addressing limitations of current models in complex motor control tasks. We introduce a decomposition of the value function where the RL component optimizes the mean expected value while the cerebellar module minimizes deviations from optimal latent trajectories. This decomposition approximates a normative information-theoretic objective where the