Comparison of supervised learning methods for spike time coding in spiking neural networks

• Autorzy: Andrzej Kasiński , Filip Ponulak
• Języki publikacji: EN

Abstrakty

EN

In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order to answer this question, we discuss various approaches to the learning task considered. We shortly describe the particular learning algorithms and report the results of experiments. Finally, we discuss the properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.

Słowa kluczowe

EN

spiking neural networks; time coding; supervised learning; temporal sequences of spikes

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2006
• Tom: 16
• Numer: 1
• Strony: 101-113

Daty

wydano

2006

otrzymano

2005-10-17

poprawiono

2006-03-02

Twórcy

autor

Andrzej Kasiński

• Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3a, 60-965 Poznań, Poland

autor

Filip Ponulak

• Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3a, 60-965 Poznań, Poland

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