Experimental Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Networks
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.
Unable to display preview. Download preview PDF.
- 3.Bohte, S., Kok, J., Potr’e, H.L.: SpikeProp: Backpropagation for Networks of Spiking Neurons. In: European Symposium on Artificial Neural Networks, ESANN, pp. 775–777 (2000)Google Scholar
- 4.Barber, D.: Learning in spiking neural assemblies. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15, pp. 149–156. MIT Press, Cambridge (2003)Google Scholar
- 5.Ponulak, F.: ReSuMe - new supervised learning method for the Spiking Neural Networks. Technical Report, Institute of Control and Information Engineering, Poznan University of Technology (2005), Available at: http://d1.cie.put.poznan.pl/~fp/
- 8.Natschlaeger, T., Markram, H., Maass, W.: Computer models and analysis tools for neural microcircuits. In: Koetter, R. (ed.) Neuroscience Databases. A Practical Guide, ch. 9, pp. 123–138. Kluwer Academic Publishers, Boston (2003)Google Scholar