ReSuMe - New Supervised Learning Method for Spiking Neural Networks

In this report I introduce ReSuMe a new supervised learning method for Spiking Neural Networks. The research on ReSuMe has been primarily motivated by the need of inventing an efficient learning method for control of movement for the physically disabled. However, thorough analysis of the ReSuMe method reveals its suitability not only to the task of movement control, but also to other real-life applications including modeling, identification and control of diverse non-stationary, nonlinear objects. ReSuMe integrates the idea of learning windows, known from the spikebased Hebbian rules, with a novel concept of remote supervision. General overview of the method, the basic definitions, the network architecture and the details of the learning algorithm are presented. The properties of ReSuMe such as locality, computational simplicity and the online processing suitability are discussed. ReSuMe learning abilities are illustrated in a verification experiment.