文献阅读笔记：脉冲神经网络最新文献合集-VIII
2025年脉冲神经网络领域研究涌现多项创新成果。中国团队主导多项研究：电子科大开发自适应阈值指静脉识别系统，国防科大提出RGB-事件融合目标检测方法，东莞理工探索残差机制在动作识别中的应用。国际研究方面，东京大学利用噪声优化网络性能，康奈尔大学研究异构量化正则化作用。应用领域涵盖生物识别（Li Yang）、工业检测（Senlin Fang）、自动驾驶（Miao Jin）等方向，显示脉冲神经网络在计
·
| 序号 | 英文标题 | 作者及机构 | 中文翻译 | 出处 | 链接 |
|---|---|---|---|---|---|
| 1 | Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold | Li Yang, Qiong Yao, Xiang Xu (Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, China) | 基于自适应发放阈值无监督脉冲卷积神经网络的指静脉识别 | Sensors (Basel, Switzerland) 2025 Vol.25 No.7 P2279 | 链接 |
| 2 | Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance | Senlin Fang, Haoran Ding, Yangjun Liu, et al. (Faculty of Data Science, City University of Macau, China; Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, 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 Vol.10 No.6 P5353-5360 | 链接 |
| 3 | Efficient Spiking Neural Network for RGB-Event Fusion-Based Object Detection | Fan, Liangwei, Yang, Jingjun, Wang, Lei, et al. (Natl Univ Def Technol, Coll Intelligence Sci & Technol, China; Acad Mil Sci, Def Innovat Inst, China) | 基于RGB-事件融合的高效脉冲神经网络目标检测 | ELECTRONICS 2025 Vol.14 No.6 | 链接 |
| 4 | Exploring the potential of residual mechanism in spiking neural networks for human action recognition | Jiaqi Chen, Ziliang Ren, Qieshi Zhang, et al. (School of Computer Science and Technology, Dongguan University of Technology, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China) | 残差机制在脉冲神经网络中用于人体动作识别的潜力探索 | Science China Technological Sciences 2025 Vol.68 No.5 | 链接 |
| 5 | Correction: Polaris 23: a high throughput neuromorphic processing element by RISC-V customized instruction extension for spiking neural network (RV-SNN 2.0) and SIMD-style implementation of LIF model with backpropagation STDP | （无作者信息） | 更正：Polaris 23：通过RISC-V定制指令扩展实现的高通量神经形态处理单元（RV-SNN 2.0）及带反向传播STDP的LIF模型SIMD式实现 | The Journal of Supercomputing 2025 Vol.81 No.4 P10 | 链接 |
| 6 | Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks | Yana Garipova, Shogo Yonekura, Yasuo Kuniyoshi (Laboratory for Intelligent Systems and Informatics, University of Tokyo, Japan) | 噪声与动态突触作为脉冲神经网络的优化工具 | Entropy 2025 Vol.27 No.3 P219 | 链接 |
| 7 | Spiking Neural Network Target Detection Method Based on Efficient Deep Feature Extraction | 黄永斌, 李晨, 董文波, 刘顺莲 (湖南工业大学计算机学院, 湖南株洲; 株洲中车时代电气股份有限公司, 湖南株洲; 湖南工业大学理学院, 湖南株洲) | 基于高效深度特征提取的脉冲神经网络目标检测方法 | Computer Science and Application 2025 Vol.15 No.1 P187-198 | 链接 |
| 8 | Heterogeneous quantization regularizes spiking neural network activity | Roy Moyal, Kyrus R Mama, Matthew Einhorn, et al. (Computational Physiology Lab, Department of Psychology, Cornell University, USA; Mehta Family School of Data Science and Artificial Intelligence, IIT Guwahati, India) | 异构量化对脉冲神经网络活动的正则化 | Scientific reports 2025 Vol.15 No.1 P14045 | 链接 |
| 9 | Research on target detection for autonomous driving based on ECS-spiking neural networks | Miao Jin, Xiaohong Wang, Ce Guo, Shufan Yang (School of Artificial Intelligence and Software, LiaoNing Petrochemical University, China; Department of Neurosurgery, The Second Hospital of Shandong University, China) | 基于ECS-脉冲神经网络的自动驾驶目标检测研究 | Scientific reports 2025 Vol.15 No.1 P13725 | 链接 |
| 10 | An Efficient Neural Cell Architecture for Spiking Neural Networks | Kasem Khalil, Ashok Kumar, Magdy Bayoumi (Electrical and Computer Engineering Department, University of Mississippi, USA; The Center for Advanced Computer Studies, University of Louisiana, USA) | 一种高效的脉冲神经网络神经元细胞架构 | IEEE Open Journal of the Computer Society 2025 P1-12 | 链接 |
| 11 | Neuromorphic Computing with Large Scale Spiking Neural Networks | Heng Xue | 大规模脉冲神经网络的神经形态计算 | 2025 | 链接 |
| 12 | Underwater Image Enhancement by Convolutional Spiking Neural Networks | Vidya Sudevan, Fakhreddine Zayer, Rizwana Kausar, et al. | 基于卷积脉冲神经网络的水下图像增强 | 2025 | 链接 |
| 13 | SAR-TinySNN: A Lightweight Spiking Neural Network for SAR Target Recognition | Junyu Wang, Hao Sun, Yuli Sun, et al. (State 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, China) | SAR-TinySNN：一种用于合成孔径雷达目标识别的轻量级脉冲神经网络 | IEEE Geoscience and Remote Sensing Letters 2025 Vol.22 P1-5 | 链接 |
| 14 | An accurate and fast learning approach in the biologically spiking neural network | Soheila Nazari, Masoud Amiri (Faculty of Electrical Engineering, Shahid Beheshti University, Iran; Department of Biomedical Engineering, School of Medicine, Kermanshah University of Medical Sciences, Iran) | 生物脉冲神经网络中的一种精确快速学习方法 | Scientific Reports 2025 Vol.15 | 链接 |
| 15 | SpikeCLIP: A contrastive language-image pretrained spiking neural network | Changze Lv, Tianlong Li, Wenhao Liu, et al. (School of Computer Science, Fudan University, China; University College London, UK) | SpikeCLIP：一种对比语言-图像预训练脉冲神经网络 | Neural Networks 2025 P107475 | 链接 |
| 16 | iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism | Qian Zhou, Jie Meng, Hao Luo (Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, China; Department of Physics, School of Science, Tianjin University, China) | iPro-CSAF：基于卷积脉冲神经网络和脉冲注意力机制的启动子识别 | PeerJ Computer Science 2025 Vol.11 e2761 | 链接 |
| 17 | Input-Triggered Hardware Trojan Attack on Spiking Neural Networks | Spyridon Raptis, Paul Kling, Ioannis Kaskampas, et al. | 针对脉冲神经网络的输入触发硬件木马攻击 | 2025 | 链接 |
| 18 | A 71.2- ? 0 ? 2 ? 0 ? 4 ?0?2?0?4 ?0?2?0?4W Speech Recognition Accelerator with Recurrent Spiking Neural Network | Chih-Chyau Yang, Tian-Sheuan Chang | 一种71.2-μW的递归脉冲神经网络语音识别加速器 | 2025 | 链接 |
| 19 | Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation | Aung Htet, Alejandro Rodriguez Jimenez, Sarah Hamburg, et al. | 用于感知与计算的脉冲神经网络持续状态的稳态控制 | 2025 | 链接 |
| 20 | A Digital Machine Learning Algorithm Simulating Spiking Neural Network CoLaNET | Mikhail Kiselev | 一种模拟脉冲神经网络CoLaNET的数字机器学习算法 | 2025 | 链接 |
| 21 | LightSNN: Lightweight Architecture Search for Sparse and Accurate Spiking Neural Networks | Yesmine Abdennadher, Giovanni Perin, Riccardo Mazzieri, et al. | LightSNN：用于稀疏且准确脉冲神经网络的轻量级架构搜索 | 2025 | 链接 |
| 22 | Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition | Siyuan Yang, Shilin Lu, Shizheng Wang, et al. | 基于时间引导脉冲神经网络的事件驱动人体动作识别 | 2025 | 链接 |
| 23 | A Stretchable Leaky Integrate-and-Fire Neuron Enables Uncertainty Handling in Spiking Neural Networks | Mao, Linna, Ke, Yizhen, Su, Hengjie, et al. (Chinese Academy of Medical Sciences, Peking Union Medical College, Institute of Biomedical Engineering, China; University of Houston, USA) | 一种可拉伸的泄漏整合-发放神经元实现脉冲神经网络的不确定性处理 | IEEE Electron Device Letters 2025 | 链接 |
| 24 | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks | Zahra Roozbehi, Ajit Narayanan, Mahsa Mohaghegh, et al. (School of Electrical and Computer Engineering, Auckland University of Technology, New Zealand; Department of Electrical and Computer Engineering, Iran University of Science and Technology, Iran) | 利用物理信号特性在递归脉冲神经网络中增强多声音事件检测与分类 | IEEE Access 2025 P1 | 链接 |
| 25 | A hybrid PKI and spiking neural network approach for enhancing security and energy efficiency in IoMT-based healthcare 5.0 | Dipalee D. Rane Chaudhari, Manisha S. Bhende, Aadam Quraishi, et al. (D. Y. Patil College of Engineering, India; Imam Abdalrhman Bin Faisal University, Saudi Arabia; Korea University of Technology and Education, South Korea) | 一种混合PKI与脉冲神经网络的方法用于提升IoMT医疗5.0的安全性与能效 | SLAS Technology 2025 Vol.32 P100284 | 链接 |
| 26 | A Hybrid PKI and Spiking Neural Network Approach for Enhancing Security and Energy Efficiency in IoMT-Based Healthcare 5.0 | Dipalee D. Rane Chaudhari, Manisha S. Bhende, Aadam Quraishi, et al. (D. Y. Patil College of Engineering, India; Imam Abdalrhman Bin Faisal University, Saudi Arabia; Inje University, South Korea) | 一种混合PKI与脉冲神经网络的方法用于提升IoMT医疗5.0的安全性与能效 | SLAS Technology 2025 P100284 | 链接 |
| 27 | Energy-Efficient Brain-Inspired Self-Attention-Spiking Neural Network Framework for Mix-Type Wafer Defect Recognition | Dandan Peng, Yitian Wang, Xinhe Zhou, et al. (Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, China; School of Computer Science, University of California San Diego, USA) | 用于混合类型晶圆缺陷识别的高能效脑启发自注意力脉冲神经网络框架 | IEEE Sensors Journal 2025 P1 | 链接 |
| 28 | Innovative solutions for plant disease identification: Leveraging DCoS-WOR and spiking neural networks | Piriyadharshini Singaravelu, Ezhilarasi Perumal (Department of Electronics and Communication Engineering, Mohamed Sathak AJ College of Engineering, India; St Joseph’ College of Engineering, India) | 植物病害识别的创新解决方案：利用DCoS-WOR与脉冲神经网络 | Expert Systems with Applications 2025 P127399 | 链接 |
| 29 | Cluster-type conductive path-based selector-less 1R memristor array for spiking neural networks | Ji Eun Kim, Suman Hu, Ju Young Kwon, et al. (Electronic Materials Research Center, Korea Institute of Science and Technology, Republic of Korea; Department of Materials Science and Engineering, Korea University, Republic of Korea) | 用于脉冲神经网络的基于簇型导电路径的无选择器1R忆阻器阵列 | Nano Energy 2025 Vol.140 P110983 | 链接 |
腾讯云面向开发者汇聚海量精品云计算使用和开发经验，营造开放的云计算技术生态圈。
更多推荐
30
0- 0
已为社区贡献7条内容
所有评论(0)