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.
The work was partially supported by the State Committee for Scientific Research, project 1445/T11/2004/27.
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References
Maass, W., Natschlaeger, T.: Networks of spiking neurons can emulate arbitrary hopfield networks in temporal coding. Network: Computation in Neural Systems 8, 355–372 (1997)
Ruf, B., Schmitt, M.: Learning temporally encoded patterns in networks of spiking neurons. Neural Processing Letters 5, 9–18 (1997)
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)
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)
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/
Gerstner, W., Kistler, W.: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Maass, W., Natschlaeger, T., Markram, H.: Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Computation 11, 2531–2560 (2002)
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)
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Kasinski, A., Ponulak, F. (2005). Experimental Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_24
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DOI: https://doi.org/10.1007/11550822_24
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