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论文类型：期刊论文

发表时间：2015-10-01

发表刊物：NONLINEAR DYNAMICS

收录刊物：SCIE、EI、Scopus

卷号：82

期号：1-2

页面范围：723-730

ISSN号：0924-090X

关键字：Robustness; Spiking neural networks; Gaussian perturbation; Classification

摘要：The robustness of an artificial neural network is important for its application. In this paper, we focus on the robustness of the classification ability of spiking neural networks with respect to perturbation of inputs according to the probability distribution. Two typical types of perturbations, sinusoidal and Gaussian perturbations, are considered, which have rarely been investigated for SNNs in the existing literature. In particular, some of the perturbations are allowed to be large, rather than all the perturbations are uniformly small as in the existing literature. Numerical experiments are carried out by using the SpikeProp algorithm on classical XOR problem and other three benchmark datasets. The numerical results show that the classification ability of SNN is robust with respect to sinusoidal and Gaussian perturbations of input signals.