文献阅读笔记：脉冲神经网络最新文献合集-IX

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, Moffe