Realtime Simulation of a Cerebellar Spiking Network Model Towards Neuroprosthesis

Neuroprosthesis aims to supersede a damaged or degenerated brain caused by accidents or aging by an artificial brain that simulates and thereby restores the impaired brain functions. To replace the real brain, the artificial brain has to simulate the same functions of the real brain, and the simulation has to be conducted in realtime. We have built a large-scale spiking network model of the cerebellum that is composed of more than 100,000 neuron units and acts as a versatile supervised learning machine for spatiotemporal information. We implement it on a graphics processing unit (GPU) to conduct the numerical simulation in realtime owing to the parallel computing capability of GPUs. We propose to use the present model towards neuroprosthesis.