脉冲神经网络最新文献合集-1104

序号英文标题作者及机构中文翻译出处链接
1High-Fidelity Real-Time Simulation of Power Electronics Converters via FPGA-Accelerated Dynamic Connectionist Neural NetworkHaowen Weng1;Zixiang Liao1;Yinbin Chen1;Can Wang1(1School of Robotics and Advanced Manufacturing, Harbin Institute of Technology, Shenzhen, China)基于FPGA加速动态连接主义神经网络的电力电子变换器高保真实时仿真IEEE Transactions on Power Electronics 2025 P1-13链接
2Using Synapse Saturation in Spiking Neural Networks for Wakeup Receivers in Internet of Things NetworksGuillaume Marthe1;Claire Goursaud1;Laurent Clavier2(1CITI, EA3720 Univ, INSA Lyon, Inria, Villeurbanne, France 2IMT Nord Europe, CNRS, UMR 8520 - IEMN, Université de Lille, Lille, France)突触饱和在物联网网络唤醒接收器脉冲神经网络中的应用IEEE Internet of Things Journal 2025 P1链接
3SA-YOLO: Spike-Driven Attention for Energy-Efficient UAV-Based Small Object DetectionYiyu Shen1;Hu Liu1;Keke Zha1;Xu Liu1;Yanan Ding1(1Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China)SA-YOLO:基于脉冲驱动注意力的节能型无人机小目标检测IEEE Internet of Things Journal 2025 P1链接
4Tri-Memristor Hyperchaotic Ring Neural Network With Hidden Firings: Dynamic Analysis, Hardware Implementation, and Application to Image EncryptionJie Wang1;Sen Zhang2;Yuanjin Zheng3;Yongxin Li1;Chunbiao Li2;Yichen Wang1;Xin Ding2(1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China 2School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, China 3School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore)具有隐藏放电的三忆阻器超混沌环形神经网络:动态分析、硬件实现及图像加密应用IEEE Internet of Things Journal 2025 P1链接
5An Enhanced Neural Communication Model for IoNT Based on the Oscillatory Characteristics of Membrane PotentialHuiyu Luo1,2;Li Huang1;Hao Jiang3,4,5;Yi Huang1;Lin Lin1(1College of Electronics and Information Engineering, Tongji University, Shanghai, China 2Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China 3School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China 4National Mobile Communications Research Laboratory, Southeast University, Nanjing, China 5School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China)基于膜电位振荡特性的IoNT增强型神经通信模型IEEE Internet of Things Journal 2025 P1链接
6Reconfigurable neuromorphic networks enabled by robust organic memristors with tunable plasticityXingyu Chen;Jiahao Gu;Ziyan Zhang;Jiashu Chen;Wei Jiang;Bin Ge;Sheng Li;Quan Xu;Jianhua Qiu*;Huafei Guo*;Sai Jiang*基于可调塑性鲁棒有机忆阻器的可重构类脑网络Journal of Physics D: Applied Physics 2025 Vol.58 No.41 P415108链接
7Adaptable microplastic classification using similarity learning on FTIR spectra collected from FTIR focal plane array imagingJustin A Smolen 1;Gavin E Moore 1;Nicholas D Perez 2;Karen L Wooley 1(1Department of Chemistry, Laboratory for Synthetic-Biologic Interactions, Texas A&M University, College Station, TX 77843. 2Department of Geology and Geophysics, Texas A&M University, College Station, TX 77843.)基于FTIR焦平面阵列成像光谱相似性学习的自适应微塑料分类Proceedings of the National Academy of Sciences of the United States of America 2025 Vol.122 No.42 e2509745122链接
8Calibrated mixup for imbalanced regression on tabular dataNathaniel Kang1;Kibok Lee1;Jongho Im1(1Department of Statistics and Data Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea)表格数据不平衡回归的校准混合方法Pattern Recognition 2025 P112610链接
9Interpretable multi-objective machine learning with calibrated uncertainty for deployment-oriented prediction of defects and properties in polymer FFFEbenezer Aquisman Asare1,2;Dickson Abdul-Wahab3;Elsie Effah Kaufmann4;Rafeah Wahi5;Zainab Ngaini5(1Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA, USA;2Organic Laboratory Research, Nuclear Chemistry and Environmental Research Centre, National Nuclear Research Institute (NNRI), Ghana Atomic Energy Commission (GAEC), Box LG 80, Legon-Accra, Ghana;3Department of Nuclear Science and Applications, School of Nuclear and Allied Sciences, University of Ghana, Atomic-Kwabenya, Accra, Ghana;4Department of Biomedical Engineering, University of Ghana, Legon-Accra, Ghana;5Department of Chemistry, Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia)面向聚合物FFF缺陷与性能部署预测的带校准不确定性可解释多目标机器学习Measurement 2025 P119350链接
10Intelligent calibration of discrete element microscopic parameters using BP-RIP-Opt algorithm: Application to medium-hard rock simulationsXupeng Sun1;Zizheng Zhang1;Weijian Yu1;Shuaigang Liu1;Zilu Liu1;Shiqiang Xu1(1School of Resources, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)基于BP-RIP-Opt算法的离散元细观参数智能校准——中硬岩模拟应用Powder Technology 2025 P121769链接
11SynapseHD: A unified training framework for bridging spiking neural networks and hyperdimensional computingLingfeng Zhou1;Huiyao Wang1;Bohan Wang1;Jinghai Wang1;Zhiyi Yu1,2;Shanlin Xiao1,2(1The School of Microelectronics Science and Technology, Sun Yat-sen University, ZhuHai, 519082, GuangDong, China;2Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, ZhuHai, 519082, Guangdong, China)SynapseHD:连接脉冲神经网络与超维计算的统一训练框架Neurocomputing 2025 P131757链接
12Dynamic Vision Sensor-Driven Spiking Neural Networks for Low-Power Event-Based Tracking and RecognitionBoyi Feng 1;Rui Zhu 1;Yue Zhu 2;Yan Jin 1;Jiaqi Ju 1(1Shanghai Institute of Technology, Shanghai 201418, China. 2Faculty of Art and Design, Yunnan University, Kunming 650091, China.)基于动态视觉传感器的低功耗事件驱动跟踪与识别脉冲神经网络Sensors (Basel, Switzerland) 2025 Vol.25 No.19 P6048链接
13FortiNIDS: Defending Smart City IoT Infrastructures Against Transferable Adversarial Poisoning in Machine Learning-Based Intrusion Detection SystemsAbdulaziz Alajaji 1(1Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.)FortiNIDS:防御智能城市物联网基础设施中机器学习入侵检测系统的可迁移对抗性投毒攻击Sensors (Basel, Switzerland) 2025 Vol.25 No.19 P6056链接
14Roadmap of Intelligent Photonics (Invited)Bai, Bowen 1; Cao, Liangcai 12; Chen, Hongwei 11; Dong, Jianji 6; Du, Shiyin 10; Fang, Lu 11; Feng, Fu 21; Fu, Tingzhao 9; Gao, Yunhui 12; Guo, Xingxing 16; Hu, Minglie 15; Hu, Yueqiang 7; Huang, Zhengqi 4; Han, Yanan 16; Huo, Dewang 21; Hao, Hao 10; Jiang, Tian 10; Li, Ming 2, 19, 20; Lin, Jie 3; Li, Siteng 18; Li, Liangye 5, 17; Liu, Runmin 15; Meng, Xiangyan 2, 19, 20; Peng, Tao 9; Situ, Guohai 14, 18; Shi, Nuannuan 2, 19, 20; Sun, Qizhen 5; Su, Jinyue 7; Wang, Xingjun 1; Xiang, Shuiying 16; Xu, Danlin 12; Xu, Zhihao 11; Xu, Shibo 6; Yuan, Xiaocong 13, 21; Yang, Qipeng 1; Yao, Yunhua 4; Zhang, Shian 4; Zhou, Tiankuang 11; Zhang, Shixiong 5, 8; Zhang, Ziyang 21(1State Key Laboratory of Advanced Optical Communications System and Networks, School of Electronics, Peking University, Beijing; 100871, China;2State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing; 100083, China;3Center of Ultra-precision Optoelectronic Instrument, Harbin Institute of Technology, Heilongjiang, Harbin; 150001, China;4State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai; 200241, China;5School of Optical and Electronics Information, Huazhong University of Science and Technology, Hubei, Wuhan; 430074, China;6Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Hubei, Wuhan; 430074, China;7State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Hunan, Changsha; 410082, China;8School of Intelligent Manufacturing, Hubei University, Hubei, Wuhan; 430062, China;9College of Advanced Interdisciplinary Studies, National University of Defense Technology, Hunan, Changsha; 410073, China;10Institute for Quantum Science and Technology, College of Science, National University of Defense Technology, Hunan, Changsha; 410073, China;11Department of Electronic Engineering, Tsinghua University, Beijing; 100084, China;12State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing; 100084, China;13Nanophotonics Research Centre, Institute of Microscale Optoelectronics, Shenzhen University, Guangdong, Shenzhen; 518060, China;14Shanghai Institute of Laser Technology Co., Ltd., Shanghai; 200233, China;15Ultrafast Laser Laboratory, Key Laboratory of Opto-Electronic Information Technology, Ministry of Education, School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin; 300072, China;16State Key Laboratory of Integrated Service Networks, Xidian University, Shaanxi, Xi’an; 710071, China;17School of Information Mechanics and Sensing Engineering, Xidian University, Shaanxi, Xi’an; 710071, China;18Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai; 201800, China;19Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing; 100190, China;20School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing; 100049, China;21Zhejiang Lab, Research Center for Humanoid Sensing, Zhejiang, Hangzhou; 311100, China)智能光子学路线图(特邀)Laser and Optoelectronics Progress 2025 Vol.62 No.17链接
15基于脉冲神经网络的边端协同目标检测方法研究王又辰,胡馨月,田宗凯,杨雨婷,邹华懿,尤宝鑫,方志,杨艳萍(北京计算机技术及应用研究所)Research on Edge-End Collaborative Target Detection Method Based on Spiking Neural Network网络安全与数据治理 2025 第44卷 第A1期 P247-252链接
16光电融合类脑计算技术(特邀)韩亚楠1,2,项水英1,2,解长健1,张雅慧1,2,郭星星1,2,王涛1,郝跃2(西安电子科技大学空天地一体化综合业务网全国重点实验室;西安电子科技大学微电子学院宽禁带半导体国家工程研究中心)Optoelectronic Fusion Brain-Inspired Computing Technology (Invited)光学学报 2025 第45卷 第17期 P126-137链接
17重构人工智能的数理疆域新高峰——江苏省机器学习与网络安全交叉研究工程中心主任、教授李凡长刘静(无明确机构标注)Reconstructing a New Peak in the Mathematical and Physical Territory of Artificial Intelligence——Li Fanchang, Director and Professor of Jiangsu Engineering Center for Interdisciplinary Research on Machine Learning and Cybersecurity中国科技成果 2025 第16期 P46-47链接
18基于奖励机制的脉冲时间依赖性可塑性的局部背光调光算法韩嘉宁,安健鹏,白岩松,刘智慧(北方自动控制技术研究所)Local Backlight Dimming Algorithm Based on Reward-Mechanism Spike-Timing Dependent Plasticity电子制作 2025 第18期 P62-65链接
19偏斜分析优化SNN-LSTM算法在数控机床主轴误差预测中的应用王舒玮(山西大同大学机电工程学院)Application of Skewness Analysis Optimized SNN-LSTM Algorithm in Spindle Error Prediction of CNC Machine Tools机床与液压 2025 第18期 P68-73链接
20Spiking neural networks with uncertainty model of stochastic sampling for circuit yield enhancementZenan Huang1;Wenrun Xiao1;Haojie Ruan1;Shan He1;Donghui Guo1(1the Department of Microelectronics and Integrated Circuit, Xiamen University, Xiamen, 361005, Fujian, China)基于随机采样不确定性模型的脉冲神经网络用于电路良率提升Engineering Applications of Artificial Intelligence 2026 Vol.163 Part 1 P112523链接
21A memristor-based spiking neural network circuit with hardware-optimized unsupervised STDPOuwen Zhang1;Dainan Zhang2;Junjie Wang1;Shuang Liu1;Hao Jiang3;Zhongrui Wang4;Xiaojuan Qi5(1School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China;2National Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China;3Frontier Institute of Chip and System, Fudan University, Shanghai, China;4School of Microelectronics, Southern University of Science and Technology, Shenzhen, Guangdong, China;5Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China)基于忆阻器的硬件优化无监督STDP脉冲神经网络电路Microelectronics Journal 2026 Vol.167 P106916链接
22Aerodynamic optimization for airfoils inspired from Swift’s wing using kriging surrogate modelZhuye Xia1,2,3;Yijuan Gu1,2,3;Luyao Tang1,2;Zhenbo Lu1,2(1School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, Shenzhen, , 518107, China;2School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, , 518107, China;3Authors contributed equally to this work.)基于克里金代理模型的雨燕翼型启发式气动优化Aerospace Science and Technology 2026 Vol.168 Part E P111125链接
23Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extractionJiayi Mao 1;Hanle Zheng 1;Huifeng Yin 1;Hanxiao Fan 1;Lingrui Mei 2;Hao Guo 3;Yao Li 4;Jibin Wu 5;Jing Pei 1;Lei Deng 6(1Center for Brain Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China;2Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;3College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong, 030600, China;4College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong, 030600, China;5Department of Data Science and Artificial Intelligence and Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR, China;6Center for Brain Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China)类脑动态神经网络中的自适应树突可塑性用于增强多时间尺度特征提取Neural networks : the official journal of the International Neural Network Society 2026 Vol.194 P108191链接
24Spatiotemporal patterns in FitzHugh–Nagumo network and its application in image encryptionZhao Yao1;Kehui Sun1;Huihai Wang2(1School of Physics, Central South University, Changsha, 410083, China;2School of Electronic Information, Central South University, Changsha, 410083, China)FitzHugh-Nagumo网络中的时空模式及其图像加密应用Neural Networks 2026 Vol.195 P108204链接
25LAMSNN: Learnable adaptive modulation for artifact suppression in spiking underwater image enhancement networksJinxin Shao1,2;Haosu Zhang1,3;Jianming Miao1,3(1School of Ocean Engineering and Technology, Sun Yat-sen University, Tangqi Road, Xiangzhou District, Zhuhai, 519000, Guangdong, China;2College of Information Technology, Eastern Liaoning University, No. 325 Wenhua Road, Yuanbao District, Dandong, 118000, Liaoning, China;3Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Tangqi Road, Xiangzhou District, Zhuhai, 519000, Guangdong, China)LAMSNN:用于脉冲水下图像增强网络伪影抑制的可学习自适应调制Neural Networks 2026 Vol.195 P108210链接
26Boosting neural Chaos via memcapacitive electromagnetic radiation in a unidirectional ring neural networkFan Shi1;Yinghong Cao1;Xianying Xu1;Santo Banerjee2;Jun Mou1(1School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China;2Department of Mathematical Sciences, Giuseppe Luigi Lagrange, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy)单向环形神经网络中基于_memcapacitive电磁辐射的神经混沌增强Chaos, Solitons & Fractals 2026 Vol.202 Part 1 P117435链接
27Integrated graph neural network framework for threshold-driven topology optimization of transient phase change thermal energy storageRui Zhao1;Yin-Ye Qian1;Bin Ye1;Hui Liang2(1School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China;2School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui 230036, PR China)用于阈值驱动瞬态相变储能拓扑优化的集成图神经网络框架International Journal of Heat and Mass Transfer 2026 Vol.256 Part 1 P128011链接
28A matrix-assisted surrogate particle swarm optimization algorithm for multi-objective deployment of solar insecticidal lampsWenjie Liu1;Donglin Zhu1;Changjun Zhou1;Shi Cheng2;Lianbo Ma3;Taiyong Li4(1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China;2School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China;3College of Software, Northeastern University, Shenyang, 110819, Liaoning, China;4School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, 611130, China)用于太阳能杀虫灯多目标部署的矩阵辅助代理粒子群优化算法Expert Systems with Applications 2026 Vol.299 Part A P129926链接
29Data-driven modeling for rapid prediction of non-uniform convective heat transfer in BIPV double-skin facadesYixiong Huang1,2;Kang Zhao1,3;Jian Ge1,3,4;Yujie Zhao5(1Department of Architecture, Zhejiang University, Hangzhou, China;2Center for Balance Architecture, Zhejiang University, Hangzhou, China;3Architectural Design & Research Institute of Zhejiang University, Hangzhou, China;4International Research Center for Green Building and Low-Carbon City, International Campus, Zhejiang University, Hangzhou, China;5Hangzhou City University, Hangzhou, China)BIPV双层表皮非均匀对流换热的数据分析快速预测建模Energy and Buildings 2026 Vol.350 P116612链接
30Design of energy-efficient LIF neuron using CMOS compatible gate-all-around floating nanosheet FET for bio-inspired spiking neural networksYashodhan Bhatawdekar1;Syed Mohammad Riyaz1;Lakshmi Amrutha Yechuri1;Sresta Valasa1;Venkata Ramakrishna Kotha1;Sunitha Bhukya1;Shubham Tayal2;Narendar Vadthiya1(1Department of Electronics and Communication Engineering, National Institute of Technology Warangal, 506004, India;2Layout Design, Staff engineer, Synopsys India Pvt. Ltd, Hyderabad 500032, India)基于CMOS兼容全环绕栅浮置纳米片FET的节能型LIF神经元设计用于类脑脉冲神经网络Neurocomputing 2026 Vol.659 P131814链接
### 脉冲神经网络监督学习的文献综述 脉冲神经网络(Spiking Neural Networks, SNN)是一种更接近生物神经系统的工作机制的模型,其信息以脉冲序列的形式存储和传递。由于神经元内部状态变量及误差函数不再满足连续可微的性质,传统的基于误差反向传播(BP)的学习算法无法直接应用于SNN[^1]。 近年来,针对SNN的监督学习算法的研究逐渐增多。文献[19] LIN X, WANG X, ZHANG N等人在《Acta Electronica Sinica》中对SNN的监督学习算法进行了全面的综述[^2]。该文章总结了多种适用于SNN的监督学习方法,并分析了它们的特点和适用场景。 此外,Wang J. 等人在2010年发表的文章中讨论了SNN在线与离线学习的区别,并提出了新的学习策略[^3]。这篇文章不仅回顾了现有的SNN监督学习方法,还探讨了在线学习的优势及其在实际应用中的潜力。 Lobo J.L. 等人于2020年在《Neural Networks》期刊上发表的文章提供了关于SNN监督学习的广泛概述,涵盖了从基本原理到最新进展的内容[^3]。该综述强调了SNN在实时数据处理和在线学习中的优势,并对未来研究方向进行了展望。 以下是一些推荐的文献供进一步研究: - LIN X, WANG X, ZHANG N, et al. Supervised Learning Algorithms for Spiking Neural Networks: A Review[J]. Acta Electronica Sinica, 2015, 3: 024. - Wang J., Belatreche A., Maguire L., McGinnity M. Online versus offline learning for spiking neural networks: A review and new strategies[C]. 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems. - Lobo J.L., DelSer J., Bifet A., Kasabov N. Spiking Neural Networks and online learning: An overview and perspectives[J]. Neural Networks, 2020, 121: 88–100. ```python # 示例代码:简单的SNN模型实现(伪代码) class SpikingNeuron: def __init__(self, threshold): self.threshold = threshold self.membrane_potential = 0 def update(self, input_spike): self.membrane_potential += input_spike if self.membrane_potential >= self.threshold: spike = 1 self.membrane_potential = 0 else: spike = 0 return spike ```
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