title: Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit.
abstract: The cerebellum plays an essential role in adaptive motor control. Once we are able to build a cerebellar model that runs in realtime, which means that a computer simulation of 1 s in the simulated world completes within 1 s in the real world, the cerebellar model could be used as a realtime adaptive neural controller for physical hardware such as humanoid robots. In this paper, we introduce "Realtime Cerebellum (RC)", a new implementation of our large-scale spiking network model of the cerebellum, which was originally built to study cerebellar mechanisms for simultaneous gain and timing control and acted as a general-purpose supervised learning machine of spatiotemporal information known as reservoir computing, on a graphics processing unit (GPU). Owing to the massive parallel computing capability of a GPU, RC runs in realtime, while reproducing qualitatively the same simulation results of the Pavlovian delay eyeblink conditioning with the previous version. RC is adopted as a realtime adaptive controller of a humanoid robot, which is instructed to learn a proper timing to swing a bat to hit a flying ball online. These results suggest that RC provides a means to apply the computational power of the cerebellum as a versatile supervised learning machine towards engineering applications.
authors: RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. Electronic address: nn12@neuralgorithm.org.
authors: Tadashi
authors: T
authors: Yamazaki
authors: Jun
authors: J
authors: Igarashi
mesh_terms: D000200:Action Potentials; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D003215:Conditioning, Eyelid / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology; D012371:Robotics; D013997:Time Factors
keywords: Cerebellar microcomplex; Graphics processing unit; Realtime simulation; Reservoir computing; Robotics; Spiking network model