A theoretical analysis of continuous firing condition for pulse-coupled neural networks with its applications

Abstract

The pulse-coupled neural network (PCNN) has become a popular biology-inspired model because of its remarkable performance, such as image fusion, segmentation, and recognition. Although the PCNN is an unsupervised neural network model, its parameters are often manually adjusted, which leads to the shortcomings of being time-consuming, verbose, and with bad consistency. Researchers have conducted work on the PCNN's neurodynamic analysis and parameter settings. However, most of these works were proposed for specific application areas, and there are no widely recognized and accepted methods or theories for PCNN parameter setting at present. Thus, how to reduce the difficulty of parameter setting based on neurodynamic analysis is a very important research area for applying PCNN. In this work, we proposed a general formulae of a continuous firing condition based on the dynamic analysis of PCNN neurons to avoid constructing an invalid model and to learn the neurons' firing characteristics. The results obtained from the proposed formulae were used to explore the usability of the PCNN parameter setting theory. We first utilized the pre-existing theory of the PCNN firing period to examine the rationality of our theory, and then we also introduce an image fusion method using our theory and whale optimization algorithm to verify our method and theory, besides, numerical tests and experiments on common images were also performed to verify our theory. Experimental results show that our method and theory is effective.