Keisuke Toyama

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 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 paper introduces HA_time, a hyperacuity algorithm for precise spike timing estimation from calcium imaging, achieving up to 20-fold improvement in spike time precision.

This study models how electrical coupling between inferior olive neurons affects firing dimensionality and chaos, supporting the hypothesis that coupling controls learning efficiency in the cerebellum.

This paper introduces two Bayesian algorithms to improve spike detection and timing precision in two-photon recordings using ground-truth electrical signals, outperforming unsupervised methods in both experimental and simulated data.