Real-time computation of a large-scaled entorhinal-hippocampal spiking neural network using GPU acceleration

Abstract

This study investigates the real-time computation of a large-scaled spiking neural network using graphic processing units. A randomly coupled network comprising several hundred thousand spiking neurons was computed in real-time. We also developed an entorhinal-hippocampal neural network consisting of approximately 50,000 spiking neurons and implemented a mechanism to form place cells in the hippocampal network through the entorhinal cortex based on the direction of motion and velocity of a mobile robot. In an experiment using a real mobile robot, we confirmed that place cells were formed in the hippocampus while the robot moved through a square open field.