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Experimental Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Networks

  • Andrzej Kasinski
  • Filip Ponulak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

In this article we consider ReSuMe – a new supervised learning method for the Spiking Neural Networks. We present the results of experiments, which indicate that ReSuMe has the following properties: (1) it can learn temporal sequences of spikes and (2) model object’s I/O properties; (3) it is scalable and (4) computationally simple; (5) it is fast converging; (6) the method is independent on the used neuron models, for this reason it can be implemented in the networks with different neuron models and potentially also to the networks of biological neurons. All these properties make ReSuMe an attractive computational tool for the real-life applications such as modeling, identification and control of non-stationary, nonlinear objects, especially of the biological neural and neuro-muscular systems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Andrzej Kasinski
    • 1
  • Filip Ponulak
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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