Liu HLiu MLi DZheng WYin LWang RSong BC2023-11-272024-07-252022-10-112023-11-272024-07-252022-10-11Liu H, Liu M, Li D, Zheng W, Yin L, Wang R. (2022). Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics (Switzerland). 11. 20.https://mro.massey.ac.nz/handle/10179/71075This paper surveys recent advances in pulse-coupled neural networks (PCNNs) and their applications in image processing. The PCNN is a neurology-inspired neural network model that aims to imitate the information analysis process of the biological cortex. In recent years, many PCNN-derived models have been developed. Research aims with respect to these models can be divided into three categories: (1) to reduce the number of manual parameters, (2) to achieve better real cortex imitation performance, and (3) to combine them with other methodologies. We provide a comprehensive and schematic review of these novel PCNN-derived models. Moreover, the PCNN has been widely used in the image processing field due to its outstanding information extraction ability. We review the recent applications of PCNN-derived models in image processing, providing a general framework for the state of the art and a better understanding of PCNNs with applications in image processing. In conclusion, PCNN models are developing rapidly, and it is projected that more applications of these novel emerging models will be seen in future.(c) 2022 The Author/sCC BYhttps://creativecommons.org/licenses/by/4.0/pulse-coupled neural networkquasi-continuous modelheterogeneous PCNNimage processingJournal article10.3390/electronics112032642079-9292journal-article3264