SpikeCD: a parameter-insensitive spiking neural network with clustering degeneracy strategy

Abstract

A clustering degeneracy algorithm, called SpikeCD, with spiking RBF neurons for classification is proposed in this paper. Unlike traditional spiking RBF networks where their performance severely relies on the time-costing process of parameter optimization, SpikeCD uses a clustering degeneracy strategy to adjust the number and centers of spiking RBF neurons, which is insensitive to parameters. A supervised learning is followed to improve network’s classification ability. Its performance is demonstrated on several benchmark datasets from the UCI Machine Learning Repository and image datasets. The results show SpikeCD can achieve good classification accuracy with simple structure. Moreover, the variation of parameters has a little effect on it. We hope this algorithm can be a new inspiration for improving the robustness of evolving spiking neural networks and other machine learning methods.