Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.

ABSTRACT: Reward prediction errors (RPEs) quantify the difference between expected and actual rewards, serving to refine future actions. Although reinforcement learning (RL) provides ample theoretical evidence suggesting that the long-term accumulation of these error signals improves learning efficiency, it remains unclear whether the brain uses similar mechanisms. To explore this, we constructed RL-based theoretical models and used multiregional two-photon calcium imaging in the mouse dorsal cortex. We identified a population of neurons whose activity was modulated by varying degrees of RPE accumulation. Consequently, RPE-encoding neurons were sequentially activated within each trial, forming a distributed assembly. RPE representations in mice aligned with theoretical predictions of RL, emerging du

SUBMITTER: Makino H 

PROVIDER: S-EPMC11753378 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature