Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade-off: attention-based methods offer expressiveness at O(T^2) complexity, while recurrent architectures suffer from gradient pathologies and dense state storage. Spiking neural networks provide event-driven efficiency but are constrained by sequential propagation, binary information loss, and local aggregation that lacks global context. We propose ChronoSpike, an adaptive spiking graph neural network that integrates learnable LIF neurons with per-channel membrane dynamics, multi-head spatially-attentive aggregation over continuous features, and a lightweight Transformer temporal encoder. This design enables fine-grained local modeling and long-range dependency capture with O(T.d) activation/state memory and an additional O(T^2) per-node attention term that remains small for the horizons evaluated here. ChronoSpike outperforms twelve state-of-the-art baselines on three large benchmarks by 2.0%~Macro-F1 and 2.4%~Micro-F1 on average while achieving 3-10 times faster training than recurrent methods with a constant 105K-parameter budget independent of graph size. We provide theoretical guarantees for membrane potential boundedness, gradient flow stability under contraction factor \r{ho}<1, and BIBO stability; interpretability analyses reveal heterogeneous temporal receptive fields and a learned primacy effect with 83-88% sparsity. Our code is publicly available at: https://github.com/Abrar2652/ChronoSpike
Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（电子与信息工程学院，西安交通大学）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人工智能与机器人研究院，西安交通大学）；State Key Lab of Brain-Machine Intelligence, Zhejiang University（脑机智能国家重点实验室，浙江大学）；School of Computer Science, Dalian University of Technology（计算机科学学院，大连理工大学）；National Key Lab of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（人机混合增强智能国家实验室，西安交通大学）
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机学院）；Zhejiang Lab（之江实验室）
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Institute of Neuroinformatics, University of Zürich and ETH Zürich（神经信息学研究所，苏黎世大学和苏黎世联邦理工学院）；ETH Zürich, Zürich, Switzerland（苏黎世联邦理工学院，苏黎世，瑞士）；AI Center, ETH, Zürich, Switzerland（人工智能中心，苏黎世联邦理工学院，苏黎世，瑞士）；Digital Society Initiative, University of Zurich, Switzerland（数字社会倡议，苏黎世大学，瑞士）
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
Institute of Automation, Chinese Academy of Sciences (CAS)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization
Department of Applied Mathematics and Computer Science (DTU Compute)（应用数学与计算机科学系（DTU计算））；Department of Engineering（工程系）；Department of Computer Engineering（计算机工程系）；Department of Mathematics（数学系）；Department of Computer Systems（计算机系统系）
Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，自动化研究所，中国科学院）；School of Future Technology, University of Chinese Academy of Sciences（未来技术学院，中国科学院大学）；China Electric Power Research Institute Co., Ltd（中国电力科学研究院有限公司）
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences（脑启发认知人工智能实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Beijing Institute of AI Safety and Governance（北京人工智能安全与治理研究院）；Beijing Key Laboratory of Safe Al and Superalignment（北京安全人工智能与超对齐重点实验室）；Center for Long-term AI（长期人工智能中心）
Similarity-based context aware continual learning for spiking neural networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；Center for Long-term Artificial Intelligence（长期人工智能中心）
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
organization= Department of Computer Science \& Engineering, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands（鲁布·大学多纳中心，尼姆egen，荷兰）；Cajal Neuroscience Center, Spanish National Research Council, Madrid, Spain（西班牙国家科研 council Cajal 神经科学中心，马德里，西班牙）
Meta-learning Spiking Neural Networks with Surrogate Gradient Descent
Peter Grünberg Institute – Neuromorphic Software Ecosystems（彼得·格伦贝格研究所——神经形态软件生态系统）；Forschungszentrum Jülich（于利希研究中心）；RWTH Aachen（亚琛工业大学）；UC Irvine（加州大学欧文分校）
Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Institute of Neuroinformatics, University of Zurich（神经信息学研究所，苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Digital Society Initiative, University of Zurich（数字社会倡议，苏黎世大学）；Department of Geography（地理系）
