Coding Method Based on Fuzzy C-Means Clustering for Spiking Neural Network With Triangular Spike Response Function

Although spiking neural network (SNN) has the advantages of strong brain-likeness and low energy consumption due to the use of discrete spikes for information representation and transmission, its performance still needs to be improved. This article improves SNN in terms of the coding process and the spike response function by invoking fuzzy sets. In terms of coding, a new fuzzy C-means coding (FCMC) method is proposed, which breaks the limitation of uniformly distributed receptive fields of existing coding methods and automatically determines suitable receptive fields that reflect the density distribution of the input data for encoding through the fuzzy C-means clustering. In terms of spike response function, triangular fuzzy numbers instead of the commonly used alpha-type function are used as the spike response function. Different from other functions of fixed shape, width parameters of the proposed function are learnt in the iterative way like weights of synapses do. Experimental results obtained on seven benchmark datasets and two real-world datasets with eleven approaches demonstrate that SNN with triangular spike response functions (abbreviated as T-SNN) combining FCMC can achieve improved performance in terms of accuracy, F-measure, AUC, required epochs, running time, and stability.