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Volume 13
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10.3390/electronics13214297
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Article

Random-Coupled Neural Network

by
Haoran Liu
1,2,
Mingrong Xiang
1,*,
Mingzhe Liu
1,
Peng Li
3,
Xue Zuo
4,
Xin Jiang
1 and
Zhuo Zuo
3
1
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China
2
College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
3
Engineering & Technical College of Chengdu University of Technology, Southwestern Institute of Physics, Leshan 614000, China
4
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(21), 4297; https://doi.org/10.3390/electronics13214297
Submission received: 28 September 2024 / Revised: 21 October 2024 / Accepted: 29 October 2024 / Published: 31 October 2024
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Abstract

Improving the efficiency of current neural networks and modeling them on biological neural systems have become prominent research directions in recent years. The pulse-coupled neural network (PCNN) is widely used to mimic the computational characteristics of the human brain in computer vision and neural network fields. However, PCNN faces limitations such as limited neural connections, high computational costs, and a lack of stochastic properties. This study proposes a random-coupled neural network (RCNN) to address these limitations. RCNN employs a stochastic inactivation process, selectively inactivating neural connections using a random inactivation weight matrix. This method reduces the computational burden and allows for extensive neural connections. RCNN encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, reflecting the information encoding characteristics of biological neural systems. Our experiments applied RCNN to image segmentation and fusion tasks, demonstrating its robustness, efficiency, and high noise resistance. Results indicate that RCNN surpasses traditional methods in performance across these applications.
Keywords: image fusion; image segmentation; neuromorphic computing; pulse-coupled neural network; primary visual cortex image fusion; image segmentation; neuromorphic computing; pulse-coupled neural network; primary visual cortex

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MDPI and ACS Style

Liu, H.; Xiang, M.; Liu, M.; Li, P.; Zuo, X.; Jiang, X.; Zuo, Z. Random-Coupled Neural Network. Electronics 2024, 13, 4297. https://doi.org/10.3390/electronics13214297

AMA Style

Liu H, Xiang M, Liu M, Li P, Zuo X, Jiang X, Zuo Z. Random-Coupled Neural Network. Electronics. 2024; 13(21):4297. https://doi.org/10.3390/electronics13214297

Chicago/Turabian Style

Liu, Haoran, Mingrong Xiang, Mingzhe Liu, Peng Li, Xue Zuo, Xin Jiang, and Zhuo Zuo. 2024. "Random-Coupled Neural Network" Electronics 13, no. 21: 4297. https://doi.org/10.3390/electronics13214297

APA Style

Liu, H., Xiang, M., Liu, M., Li, P., Zuo, X., Jiang, X., & Zuo, Z. (2024). Random-Coupled Neural Network. Electronics, 13(21), 4297. https://doi.org/10.3390/electronics13214297

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