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—also known as a neural microcircuit—is able to imitate robot controllers like that incorporated in Braitenberg vehicles. A more non-linear type controller is imitated in a further experiment. In a different series of experiments that involve temporal memory reported in Burgsteiner et al. [2005. In: Proceedings of the 18th International Conference IEA/AIE. Lecture Notes in Artificial Intelligence. Springer, Berlin, pp. 121–130.] this approach also provided a basis for a movement prediction task. The results suggest that a neural microcircuit with a simple learning rule can be used as a sustainable robot controller for experiments in computational motor control.