Jesse P. Geerts

This paper proposes a computational framework where the cortico-cerebellar network learns low-dimensional action embeddings and the basal ganglia use reinforcement learning to learn value and policies, demonstrating its relevance to motor learning.

This study identifies a movement-based dopamine signal in mice that acts as a value-free teaching signal for learning sound-action associations, working alongside reward prediction error.