Imitation learning with spiking neural networks and real-world devices

This article is about a new approach in robotic learning systems. It provides a method to use a real-world device that operates in real-time, controlled through a simulated recurrent spiking neural network for robotic experiments. A randomly generated network is used as the main computational unit. Only the weights of the output units of this network are changed during training. It will be shown, that this simple type of a biological realistic spiking neural network

• Type: Journal
• Year: 2006
• Where: EAAI
• Authors: Harald Burgsteiner
• Keywords: EAAI 2006, Neural Microcircuit, Robot Controller, Spiking Neural Network