M Matsuzaki

The paper examines how motor learning affects cortical network coordination in marmosets using calcium imaging and analysis of activity components.

This study shows that rapid associative spine enlargement is necessary for maintaining cognitive function and stable wakefulness in mice.

The paper shows that climbing fiber inputs in the cerebellum generate reward-prediction errors that guide modular reinforcement learning in mice, integrating with supervised learning mechanisms.

The paper investigates how movement and reward expectancy modulate neural activity in different primate cortical areas during a conditioning task.

ARViS enables precise, fast, and dense viral injection in mouse and marmoset brains with minimal bleeding, facilitating biosensor expression for imaging studies.

The paper analyzes two-photon imaging data from mice during reinforcement learning to show cerebellar climbing fiber responses are organized into four functional components that reduce learning space dimensions.

This study investigates how motor direction representation changes in primate premotor and primary motor cortices during learning, finding that PMdc neurons dynamically adjust their preferred direction while M1 neurons maintain stability.

The paper investigates how frontal cortical neurons represent reward-predicting cues and reward history in mice during a classical conditioning task, finding that deep-layer neurons in dmFrC strongly encode these signals.

This paper demonstrates two-photon calcium imaging up to 1200 m below the cortical surface in mice, revealing higher reward prediction activity in the prelimbic area compared to the hippocampal CA1 region.