文献阅读笔记：脉冲神经网络最新文献合集-XVI

序号	英文标题	作者及机构	中文翻译	出处	链接
1	Integrating predictive coding with reservoir computing in spiking neural network model of cultured neurons	Yoshitaka Ishikawa;Takumi Shinkawa;Takuma Sumi;Hideyuki Kato;Hideaki Yamamoto;Yuichi Katori（Future University Hakodate ; Oita University ; Tohoku University ; Oita University ; Tohoku University ; Future University Hakodate International Research Center for Neurointelligence (IRCN)）	培养神经元脉冲神经网络模型中预测编码与储备池计算的融合	Nonlinear Theory and Its Applications, IEICE 2024 Vol.15 No.2 P432-442	链接
2	Deep spiking neural network training method across spiking propagation.	（1广东工业大学计算机学院,广州510006.）	跨脉冲传播的深度脉冲神经网络训练方法	Application Research of Computers / Jisuanji Yingyong Yanjiu 2024 Vol.41 No.7 P2134-2140	链接
3	Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks	Yanjing Li，Xiaowei Wang，Fukun Chen，Bingxu Zhao，Qiang Fu（the Institute of Education Science Research）	基于脉冲神经网络的在线学习行为分析与预测	Journal of Social Computing 2024 第2期 P180-193	链接
4	Exploiting memristive autapse and temporal distillation for training spiking neural networks	Tao Chen1;Shukai Duan1,2,3;Lidan Wang1,2,3,4,5（1College of Artificial Intelligence, Southwest University, Chongqing, 400715, China;2Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Chongqing, 400715, China;3National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing, 400715, China;4Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, Southwest University, Chongqing, 400715, China;5State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing, 400023, China）	利用忆阻自突触和时间蒸馏训练脉冲神经网络	Knowledge-Based Systems 2024 Vol.305 P112627	链接
5	Spike-and-Slab Shrinkage Priors for Structurally Sparse Bayesian Neural Networks	Sanket Jantre;Shrijita Bhattacharya;Tapabrata Maiti（1Michigan State University, East Lansing, MI, USA 2Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA）	结构稀疏贝叶斯神经网络的尖峰-平板收缩先验	IEEE transactions on neural networks and learning systems 2024 P1-13	链接
6	Low-Power FPGA-based Spiking Neural Networks for Real-Time Decoding of Intracortical Neural Activity	Luca Martis1;Gianluca Leone1;Luigi Raffo1;Paolo Meloni1（1Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy）	用于皮层内神经活动实时解码的低功耗FPGA基脉冲神经网络