Computational Efficacy of GPGPU-Accelerated Simulation for Various Neuron Models

To understand the processing mechanism of sensory information in the brain, it is necessary to simulate a huge size of network that is represented by a complicated neuron model imitating actual neurons. However, such a simulation requires a very long computation time, failing to perform computer simulation with a realistic time scale. In order to solve the problem of computation time, we focus on the reduction of computation time by GPGPU, providing an efficient method for simulation of huge number of neurons. In this paper, we develop a computational architecture of GPGPU, by which computation of neurons is performed in parallel. Using this architecture, we show that the GPGPU method significantly reduces the computation time of neural network simulation. We also show that the simulations with single and double float precision give little significant difference in the results, independently of the neuron models used. These results suggest that the GPGPU computation with single float precision could be a most efficient method for simulation of a huge size of neural network.