文献阅读笔记：脉冲神经网络最新文献合集-IX
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| 序号 | 英文标题 | 作者及机构 | 中文翻译 | 出处 | 链接 |
|---|---|---|---|---|---|
| 1 | Frame-Unit Operating Neuron Circuits for Hardware Recurrent Spiking Neural Networks | Yeonwoo Kim1; Bosung Jeon1; Jonghyuk Park1; Woo Young Choi1（1Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, South Korea） | 用于硬件递归脉冲神经网络的帧单元操作神经元电路 | IEEE Transactions on Electron Devices, 2025, Vol. 72, No. 4, pp. 1795–1801 | 链接 |
| 2 | A Charge-Domain Design of Ferroelectric Tunneling Junction Synapse for Spiking Neural Networks | Xiaobao Zhu1; Ning Feng1; Jiajun Qiu1; Xianyu Wang1; Min Zeng2; Yanqing Wu2; Lining Zhang1（1Guangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China; 2School of Integrated Circuits, Peking University, Beijing, China） | 用于脉冲神经网络的铁电隧道结突触电荷域设计 | IEEE Transactions on Electron Devices, 2025, Vol. 72, No. 4, pp. 1730–1737 | 链接 |
| 3 | Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage | Yongqiang Zhang1; Haijie Pang2; Jinlong Ma2; Guilei Ma3; Xiaoming Zhang2; Menghua Man3（1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; 2School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; 3Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China） | 网络连接损伤下脉冲神经网络的抗干扰性能研究 | Brain Sciences, 2025, Vol. 15, No. 3, p. 217 | 链接 |
| 4 | Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks | Yana Garipova1; Shogo Yonekura1; Yasuo Kuniyoshi1（1Laboratory for Intelligent Systems and Informatics, University of Tokyo, Tokyo 113-0033, Japan） | 噪声和动态突触作为脉冲神经网络的优化工具 | Entropy (Basel, Switzerland), 2025, Vol. 27, No. 3, p. 219 | 链接 |
| 5 | RRAM-Based Spiking Neural Network With Target-Modulated Spike-Timing-Dependent Plasticity | Kalkidan Deme Muleta1; Bai-Sun Kong1（1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea） | 基于RRAM的目标调制脉冲时间依赖可塑性脉冲神经网络 | IEEE Transactions on Biomedical Circuits and Systems, 2025, Vol. 19, No. 2, pp. 385–392 | 链接 |
| 6 | Biomimetic Spiking Neural Network Based on Monolayer 2-D Synapse With Short-Term Plasticity for Auditory Brainstem Processing | Jieun Kim1; Peng Zhou2,3; Unbok Wi4; Bomin Joo1,5; Donguk Choi2,6; Myeong-Lok Seol7; Sravya Pulavarthi2,8; Linfeng Sun9; Heejun Yang10; Woo Jong Yu1; Jin-Woo Han7,5; Sung-Mo Kang11; Bai-Sun Kong12（1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; 2Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA; 3LuxiTech, Shenzhen, China; 4Department of Artificial Intelligence, Sungkyunkwan University, Suwon, South Korea; 5Samsung Electronics, Hwaseong, South Korea; 6IBM Research, Albany, NY, USA; 7Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA, USA; 8MathWorks, Natick, MA, USA; 9School of Physics, Beijing Institute of Technology, Beijing, China; 10Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, South Korea; 11Baskin School of Engineering, University of California, Santa Cruz, CA, USA; 12Department of Electrical and Computer Engineering and Department of Artificial Intelligence, Sungkyunkwan University, Suwon, South Korea） | 基于单层二维突触短时可塑性的仿生脉冲神经网络用于听觉脑干处理 | IEEE Transactions on Cognitive and Developmental Systems, 2025, Vol. 17, No. 2, pp. 247–258 | 链接 |
| 7 | General and stable emulation of finite state machines with spiking neural networks | Ziyang Sun; Zhong Zheng; Binying Zhang; Hanle Zheng; Zikai Wang; Hao Guo*; Lei Deng* | 脉冲神经网络对有限状态机的通用稳定仿真 | Neuromorphic Computing and Engineering, 2025, Vol. 5, No. 1, p. 014016 | 链接 |
| 8 | Tiny dLIF: a dendritic spiking neural network enabling a time-domain energy-efficient seizure detection system | Luis Fernando Herbozo Contreras*; Leping Yu; Zhaojing Huang; Ziyao Zhang; Armin Nikpour; Omid Kavehei | Tiny dLIF：一种树突脉冲神经网络实现时域节能癫痫检测系统 | Neuromorphic Computing and Engineering, 2025, Vol. 5, No. 1, p. 014015 | 链接 |
| 9 | Assisting Training of Deep Spiking Neural Networks With Parameter Initialization | Jianhao Ding1; Jiyuan Zhang1; Tiejun Huang1; Jian K. Liu2; Zhaofei Yu3（1School of Computer Science and the State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, China; 2School of Computer Science, University of Birmingham, Birmingham, U.K.; 3Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China） | 参数初始化辅助深度脉冲神经网络训练 | IEEE Transactions on Neural Networks and Learning Systems, 2025, pp. 1–14 | 链接 |
| 10 | Synapses mediate the effects of different types of stress on working memory: a brain-inspired spiking neural network study | Chengcheng Du1,2,3; Yinqian Sun1,2,3; Jihang Wang1,2,3; Qian Zhang1,3,4; Yi Zeng1,3,4,5（1Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 2School of Future Technology, University of Chinese Academy of Sciences, Beijing, China; 3Center for Long-term Artificial Intelligence, Beijing, China; 4School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; 5Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China） | 突触介导不同类型压力对工作记忆的影响：脑启发脉冲神经网络研究 | Frontiers in Cellular Neuroscience, 2025, Vol. 19, p. 1534839 | 链接 |
| 11 | A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection | K. Anup Kumar1; C. Vanmathi1（1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India） | 用于增强皮肤癌检测的混合并行卷积脉冲神经网络 | Scientific Reports, 2025, Vol. 15 | 链接 |
| 12 | Spiking Neural Networks for People Counting based on FMCW radar | Alberto Martin-Martin1,2; Marta Verona-Almeida1; Rubén Padial-Allué2; Borja Saez1; Javier Mendez1; Encarnación Castillo2; Luis Parrilla2（1Eesy Innovation, Leipziger Str. 16, Unterhaching, Germany; 2Department of Electronics and Computer Technology, Faculty of Sciences, University of Granada, Granada, Andalucia, Spain） | 基于FMCW雷达的脉冲神经网络人数统计 | IEEE Access, 2025, p. 1 | 链接 |
| 13 | SAR-TinySNN: A lightweight spiking neural network for SAR target recognition | Junyu Wang1; Hao Sun1; Yuli Sun1; Tao Tang1; Lin Lei1; Kefeng Ji1（1State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System and the College of Electronic Science and Technology, National University of Defense Technology, Changsha, China） | SAR-TinySNN：一种用于SAR目标识别的轻量级脉冲神经网络 | IEEE Geoscience and Remote Sensing Letters, 2025, p. 1 | 链接 |
| 14 | Identification of Depression Patients Using LIF Spiking Neural Network Model From the Pattern of EEG Signals | Rekha Sahu1; Prasant Kumar Pattnaik2; Kalaiarasi Sonai Muthu Anbananthen3; Saravanan Muthaiyah4（1Department of Computer Science and Engineering, Silicon University, Bhubaneswar, Odisha, India; 2School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India; 3Faculty of Information Science and Technology, Multimedia University, Malacca, Malaysia; 4School of Business and Technology, IMU University, Kuala Lumpur, Malaysia） | 基于EEG信号模式的LIF脉冲神经网络模型识别抑郁症患者 | IEEE Access, 2025, Vol. 13, pp. 55156–55168 | 链接 |
| 15 | Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance | Senlin Fang1,2; Haoran Ding3; Yangjun Liu4,5; Jiashu Liu2; Yupo Zhang2; Yilin Li2; Hoiio Kong1; Zhengkun Yi1,2,6（1City University of Macau, Macau, China; 2Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; 3City University of Hong Kong, Hong Kong, China; 4University of Macau, Macau, China; 5Shenzhen Institute of Advanced Technology, Shenzhen, China; 6Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, China） | Evetac与稀疏概率脉冲神经网络结合：提升卡扣识别效率与性能 | IEEE Robotics and Automation Letters, 2025, pp. 1–8 | 链接 |
| 16 | Denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset | Pinze Ren1; Yitian Wang2; Zisheng Wang3; Dandan Peng4; Chenyu Liu5; Te Han6（1Department of Chemical Engineering, Tsinghua University, China; 2School of Computer Science, University of California San Diego, United States; 3Department of Industrial Engineering, Tsinghua University, China; 4Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, China; 5Mechanical and Electrical Engineering, Northwestern Polytechnical University, China; 6Center for Energy and Environmental Policy Research, Beijing Institute of Technology, China） | 基于去噪自编码器多层感知器脉冲神经网络的工业级异烟酸产率预测 | Advanced Engineering Informatics, 2025, Vol. 65, Part C, p. 103273 | 链接 |
| 17 | Evaluation of the anti-disturbance capability of fMRI-based spiking neural network based on speech recognition | Lei Guo1,2; Chongming Li1,2; Youxi Wu3; Menghua Man4（1Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; 2State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300131, China; 3School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; 4Shijiazhuang Campus, Army Engineering University of PLA, Hebei 050000, China） | 基于语音识别的fMRI脉冲神经网络抗干扰能力评估 | Applied Soft Computing, 2025, Vol. 175, p. 113069 | 链接 |
| 18 | Physics‐Based Spiking Neural Network as a Surrogate Model for Viscoplastic Material Law in Impulsively Loaded Beams. | Polydoras, Vasileios1; Tandale, Saurabh1; Stoffel, Marcus1（1Institute for General Mechanics, RWTH Aachen University, Aachen North Rhine‐Westphalia, Germany） | 基于物理的脉冲神经网络作为冲击载荷梁粘塑性材料定律的替代模型 | PAMM: Proceedings in Applied Mathematics & Mechanics, 2024, Vol. 24, No. 4, pp. 1–7 | 链接 |
| 19 | Encoding of Input Signals in Terms of Path Complexes in Spiking Neural Networks | V. A. Ilyin1,2,3; Ya. P. Ivina1; M. Yu. Khristichenko1; A. V. Serenko1 & …R. B. Rybka1（1National Research Center “Kurchatov Institute”, Moscow, 123182, Russia; 2National Center for Cognitive Research, ITMO University, St. Petersburg, 197101, Russia; 3Research Computing Center, Lomonosov Moscow State University, Moscow, Russia） | 脉冲神经网络中输入信号的路径复合体编码 | Moscow University Physics Bulletin, 2024, Vol. 79, No. 2, pp. S630–S638 | 链接 |
| 20 | Spiking Neural Network Actor–Critic Reinforcement Learning with Temporal Coding and Reward-Modulated Plasticity | D. S. Vlasov1; R. B. Rybka1; A. V. Serenko1 & …A. G. Sboev1,2（1National Research Centre “Kurchatov Institute” , Moscow, Russia; 2National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia） | 基于时间编码和奖励调制可塑性的脉冲神经网络演员-评论家强化学习 | Moscow University Physics Bulletin, 2024, Vol. 79, No. 2, pp. S944–S952 | 链接 |
| 21 | Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios. | Cavaleri, Matteo; Zandron, Claudio（1Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336/14 Milano 20126, Italy） | 探索脉冲神经网络的多功能性：跨领域应用场景 | International Journal of Neural Systems, 2024, p. 1 | 链接 |
| 22 | A Compressed Spiking Neural Network onto Memcapacitive in-Memory Computing Array | Reon Oshio; Takuya Sugahara; Atsushi Sawada; Mutsumi Kimura; Renyuan Zhang; Yasuhiko Nakashima | 基于忆容内存计算阵列的压缩脉冲神经网络 | IEEE Micro, 2023, pp. 1–9 | 链接 |
| 23 | Neuromorphic Functional Modules of a Spiking Neural Network | E. A. Ryndin; N. V. Andreeva; V. S. Raiimzhonov | 脉冲神经网络的神经形态功能模块 | Nanotechnologies in Russia, 2022, Vol. 17, No. 1Suppl, pp. S80–S90 | 链接 |
| 24 | Modular Modeling of Analog Organic Neuromorphic Circuits: Toward Prototyping of Hardware-Level Spiking Neural Networks | Yi Yang; Mohammad Javad Mirshojaeian Hosseini; Walter Kruger; Robert A. Nawrocki | 模拟有机神经形态电路的模块化建模：迈向硬件级脉冲神经网络原型设计 | IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, pp. 1–14 | 链接 |
| 25 | An Efficient Learning Algorithm for Direct Training Deep Spiking Neural Networks | Xiaolei Zhu; Baixin Zhao; De Ma; Huajin Tang | 直接训练深度脉冲神经网络的高效学习算法 | IEEE Transactions on Cognitive and Developmental Systems, 2021, p. 1 | 链接 |
| 26 | A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks | Jibin Wu; Yansong Chua; Malu Zhang; Guoqi Li; Haizhou Li; Kay Chen Tan | 深度脉冲神经网络的串联学习规则：有效训练与快速推理 | IEEE Transactions on Neural Networks and Learning Systems, 2021, pp. 1–15 | 链接 |
| 27 | Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA | Hajar Asgari; Babak Mazloom-Nezhad Maybodi; Melika Payvand; Mostafa Rahimi Azghadi | 用于FPGA上下文相关学习的低能耗快速脉冲神经网络 | IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, No. 99, p. 1 | 链接 |
| 28 | Deep Spiking Neural Networks with Binary Weights for Object Recognition | Yixuan Wang; Yang Xu; Rui Yan; Huajin Tang | 用于目标识别的二值权重深度脉冲神经网络 | IEEE Transactions on Cognitive and Developmental Systems, 2020, No. 99, p. 1 | 链接 |
| 29 | Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification | Shuiying Xiang; Zhenxing Ren; Ziwei Song; Yahui Zhang; Xingxing Guo; Genquan Han; Yue Hao | 基于垂直腔面发射激光器的全光脉冲神经网络计算基元用于监督学习与模式分类 | IEEE Transactions on Neural Networks and Learning Systems, 2020, pp. 1–12 | 链接 |
| 30 | Efficient Deployment of Spiking Neural Networks on SpiNNaker Neuromorphic Platform | Ioannis Galanis; Iraklis Anagnostopoulos; Chinh Nguyen; Guillermo Bares | SpiNNaker神经形态平台上脉冲神经网络的高效部署 | IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, p. 1 | 链接 |
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