  It is well-known that the robustness of artificial neural networks (ANNs) is
important for their wide ranges of applications. In this paper, we focus on the
robustness of the classification ability of a spiking neural network which
receives perturbed inputs. Actually, the perturbation is allowed to be
arbitrary styles. However, Gaussian perturbation and other regular ones have
been rarely investigated. For classification problems, the closer to the
desired point, the more perturbed points there are in the input space. In
addition, the perturbation may be periodic. Based on these facts, we only
consider sinusoidal and Gaussian perturbations in this paper. With the
SpikeProp algorithm, we perform extensive experiments on the classical XOR
problem and other three benchmark datasets. The numerical results show that
there is not significant reduction in the classification ability of the network
if the input signals are subject to sinusoidal and Gaussian perturbations.

Machine Learning;Machine Learning