Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Learning Methods of Recurrent Spiking Neural Networks
Transient and Oscillatory Spike Trains
Kukan SELVARATNAMYasuaki KUROETakehiro MORI
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2000 Volume 13 Issue 3 Pages 95-104

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Abstract

In an artificial Spiking Neural Network (SNN) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. Yet, there are no well defined efficient methods for learning due to their rather intricately discontinuous and nonlinear mechanisms. In this paper, we consider a recurrent SNN constructed with integrate-and-fire type spiking neurons. First we propose a learning method such that the SNN possesses desired transient responses (spike-train outputs) by changing the synaptic weights. Further by including periodic state conditions we propose a learning method such that the SNN possesses desired oscillatory responses (limit cycle spike train) by changing both the synaptic weights and the initial conditions. Simulation examples are also provided to verify the efficiency and the applicability of the proposed algorithm.

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