NeurosciencePNAS NexusPeer-reviewed
A theory of cerebellar learning as a spike-based reinforcement learning in continuous time and space
Rin Kuriyama, Hideyuki Yoshimura, Tadashi Yamazaki
This paper develops a cerebellar spiking network as a reinforcement learning model in continuous time and space, demonstrating its ability to solve RL tasks and challenge the classical view of cerebellar learning.
2 citations