Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

ABSTRACT: Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. We employed the Q-learning model to accurately reproduce the licking responses of mice in a Go/No-go auditory-discrimination task. This method enabled the calculation of reinforcement learning variables, such as reward, predicted reward, and reward-prediction errors in each learning trial. Through tensor component analysis of two-photon Ca2+ imaging data from more than 6,000 Purkinje cells, we found that climbing fiber inputs of the two distinct components, which were specifically a

SUBMITTER: Hoang H 

PROVIDER: S-EPMC11957396 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature