文献阅读笔记：脉冲神经网络最新文献合集-XIV

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序号	英文标题	作者及机构	中文翻译	出处	链接
1	A two-stage strategy for brain-inspired unsupervised learning in spiking neural networks	Zhen Cao1;Chuanfeng Ma1;Biao Hou1;Xiaoyu Chen1;Leida Li1;Hao Zhu1;Dou Quan1;Licheng Jiao1（1School of Artificial Intelligence, Xidian University, Xi’an, 710126, Shaanxi, China）	脉冲神经网络中受脑启发的无监督学习两阶段策略	Neurocomputing 2025 Vol.611 P128655	链接
2	Spiking Neural Networks for event-based action recognition: A new task to understand their advantage	Alex Vicente-Sola1;Davide L. Manna1;Paul Kirkland1;Gaetano Di Caterina1;Trevor J. Bihl2（1Neuromorphic Sensor Signal Processing Lab, University of Strathclyde, Glasgow, UK;2Air Force Research Laboratory, Wright Patterson AFB, OH, USA）	用于基于事件的动作识别的脉冲神经网络：一项理解其优势的新任务	Neurocomputing 2025 Vol.611 P128657	链接
3	Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks	Yi Yang1;Richard M. Voyles1;Haiyan H. Zhang1;Robert A. Nawrocki1（1School of Engineering Technology, Purdue University, West Lafayette, United States）	用于多层脉冲神经网络的分数阶脉冲时序依赖梯度下降	Neurocomputing 2025 Vol.611 P128662	链接
4	A novel label-aware global graph construction method and spiking-coded graph neural network for intelligent process fault diagnosis	Dazi Li1;Yurui Zhu1;Zhihuan Song2;Hamid Reza Karimi3（1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China;2State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China;3Department of Mechanical Engineering, Politecnico di Milano, Milano 20156, Italy）	用于智能过程故障诊断的新型标签感知全局图构建方法与脉冲编码图神经网络	Neurocomputing 2025 Vol.611 P128707	链接
5	Constructing lightweight and efficient spiking neural networks for EEG-based motor imagery classification	Xiaojian Liao1;Guang Li2;You Wang2;Lining Sun1;Hongmiao Zhang1（1The Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electric Engineering, Soochow University, Suzhou 215000, Jiangsu, China;2The State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China）	构建用于基于脑电图的运动想象分类的轻量高效脉冲神经网络	Biomedical Signal Processing and Control 2025 Vol.100 Part B P107000	链接
6	Jixie Gongcheng Xuebao/Journal of Mechanical Engineering	Wang, Juna;Yang, Yiqinga;Liu, Jinzhaob;Shen, Changqinga;Huang, Weiguoa;Zhu, Zhongkuia（aSchool of Rail Transportation, Soochow University, Suzhou, 215131, China;bInfrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing, 100081, China）	基于小波核编码脉冲卷积神经网络的可解释智能诊断	Jixie Gongcheng Xuebao/Journal of Mechanical Engineering 2024 Vol.60 No.12 P41-50	链接
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