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- Dynamic Neural Manifolds For Flexible Closed Loop Control On NeuromorphicIn biological circuits, sequential neural activity evolves along dynamic, low-dimensional manifolds to enable flexible behavior. Spiking network models link aspects of this sequential activity to feat. Based on arXiv:2607.07373.Votes: 0GitHub stars: 3
- Deep Reinforcement Learning With Spiking Qlearning**arXiv ID:** 2201.09754 **Authors:** Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian **Published:** 2022-01-21T16:42:11Z **Abstract:** With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (RL). There are only a few existing SNN-based RL methods at present. Mos...Votes: 0GitHub stars: 3
- Constraints On Hebbian And Stdp Learned Weights Of A Spiking Neuron**arXiv ID:** 2012.07664 **Authors:** Dominique Chu, Huy Le Nguyen **Published:** 2020-12-14T16:09:12Z **Abstract:** We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbian learning, we find that the normalised weights equal the promotion probabilities of weights up to correction terms that depend on the learning rate and are usually small. A similar relation can be d...Votes: 0GitHub stars: 3
- Boost Eventdriven Tactile Learning With Location Spiking Neurons**arXiv ID:** 2210.04277 **Authors:** Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt **Published:** 2022-10-09T14:49:27Z **Abstract:** Tactile sensing is essential for a variety of daily tasks. And recent advances in event-driven tactile sensors and Spiking Neural Networks (SNNs) spur the research in related fields. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representation abilities of existing spiking neuron...Votes: 0GitHub stars: 3
- Benchmarking Fairness In Spiking Neural Networks Data Bias Spurious Features And Hardware Effects**arXiv ID:** 2605.27407 **Authors:** Hudi He, Fukun Wang, Zhe Wang, Xinyi Wang, Shuhan Ye, Jiarui Liu, Qing Qing, Ziqi Xu, Xikun Zhang, Renqiang Luo **Published:** 2026-05-08T02:29:00Z **Abstract:** Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remain limited by superficial dataset diversity and idealized hardware assumptions. This work introduces the first systematic fairness benchmark for SNN...Votes: 0GitHub stars: 3
- Bayesian Continual Learning Via Spiking Neural Networks**arXiv ID:** 2208.13723 **Authors:** Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone **Published:** 2022-08-29T17:11:14Z **Abstract:** Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based computing paradigm of biological brains. I...Votes: 0GitHub stars: 3
- Backpropagationfree Spiking Neural Networks With The Forwardforward Algorithm**arXiv ID:** 2502.20411 **Authors:** Mohammadnavid Ghader, Saeed Reza Kheradpisheh, Bahar Farahani, Mahmood Fazlali **Published:** 2025-02-19T12:44:26Z **Abstract:** Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility. ...Votes: 0GitHub stars: 3
- Averageovertime Spiking Neural Networks For Uncertainty Estimation In Regression**arXiv ID:** 2412.00278 **Authors:** Tao Sun, Sander Bohté **Published:** 2024-11-29T23:13:52Z **Abstract:** Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that adapt the Average-Over-Time Spiking Neural Network (AOT-SNN) framewor...Votes: 0GitHub stars: 3
- Attacking The Spike On The Transferability And Security Of Spiking Neural Networks To Adversarial Examples**arXiv ID:** 2209.03358 **Authors:** Nuo Xu, Kaleel Mahmood, Haowen Fang, Ethan Rathbun, Caiwen Ding, Wujie Wen **Published:** 2022-09-07T17:05:48Z **Abstract:** Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep learning approaches, the study of SNN robustness to adversarial examples remains relatively underdeveloped. In this work, we advance the adversarial attack ...Votes: 0GitHub stars: 3
- Arxiv 2609 08070v1 A Gradient Based Yet Spike Timing Dependent Soluti**arXiv ID:** 2609.08070v1 **Authors:** Xiangnan Zhang, Jingxin Liu, Ranqi Lu, Jingyu Liu, Qunxi Dong, Fuze Tian, Lixian Zhu, Bin Hu, Björn W. Schuller **URL:** http://arxiv.org/abs/2609.08070v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 30439v1 Event Driven Language Models With Sparse Neural Ac**arXiv ID:** 2608.30439v1 **Authors:** Simon Richter, Ruhai Lin, Jason Yik, Taylor Kergan, Rui-Jie Zhu, Farshad Moradi, Jason Eshraghian **URL:** http://arxiv.org/abs/2608.30439v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 25871v1 Cedar Controlled And Event Driven Demand Forecasti**arXiv ID:** 2608.25871v1 **Authors:** Junjie Meng, Ranxu Zhang, Zi-an Zhang, Shujun Liu, Xiaoning Qi, Xiaozhou Xu, Yanyong Zhang, Hui Xiong, Chao Wang **URL:** http://arxiv.org/abs/2608.25871v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 25738v1 Memark Membrane Space Watermarking For Spiking Neu**arXiv ID:** 2608.25738v1 **Authors:** Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta **URL:** http://arxiv.org/abs/2608.25738v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 25536v1 Syn2logic End To End Neuromorphic Design Automatio**arXiv ID:** 2608.25536v1 **Authors:** Artur Podobas **URL:** http://arxiv.org/abs/2608.25536v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 24073v1 Orbitalif An Efficient Spiking Federated Learning**arXiv ID:** 2608.24073v1 **Authors:** Bohan Zhang, Chenyu Xu, Yijie Mao, Yuanming Shi **URL:** http://arxiv.org/abs/2608.24073v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 22729v1 Spiking Neural Networks For Continuous Control Neu**arXiv ID:** 2608.22729v1 **Authors:** Jessica Hunter, Md Maruf Hossain Shuvo, Krishna Roy **URL:** http://arxiv.org/abs/2608.22729v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 18341v1 Low Power Neuromorphic Acoustic Anomaly Detection**arXiv ID:** 2608.18341v1 **Authors:** Steven C. Nesbit, Victor M. Vergara, Michael A. Felix, Evan T. Kain, Luis R. García Carrillo, Gerd J. Kunde, Andrew T. Sornborger **URL:** http://arxiv.org/abs/2608.18341v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 13221 Tsugo Probing Search Efficiency In Llm Reasoning VTsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems (arXiv: 2608.13221)Votes: 0GitHub stars: 3
- Arxiv 2608 11865v1 Lapis Laplacian Spiking Attention Via First Spike**arXiv ID:** 2608.11865v1 **Authors:** Kaiwen Tang, Jiaqi Zheng, Zixuan Zhu, Yiqun Wang, Zhanglu Yan, Weng-Fai Wong **URL:** http://arxiv.org/abs/2608.11865v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06346v1 Trajdebug Tracing Error Lifecycle To Identify CritTRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories (arXiv: 2608.06346v1)Votes: 0GitHub stars: 3
- Adaptive Spiking Neuron DesignFunctional design framework for adaptive spiking neurons enabling high-performance vision and language modeling. Activation: adaptive spiking, adaptive snn, spiking neuron design, functional spiking, 自适应脉冲神经元.Votes: 0GitHub stars: 3
- Towards Optimal Vpu Compiler Cost Modeling By Using Neural Networks To Infer Hardware Performances**arXiv ID:** 2205.04586 **Authors:** Ian Frederick Vigogne Goodbody Hunter, Alessandro Palla, Sebastian Eusebiu Nagy, Richard Richmond, Kyle McAdoo **Published:** 2022-05-09T22:48:39Z **Abstract:** Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depending on their configuration - How work is shared between distributed targets, the subdivision o...Votes: 0GitHub stars: 3
- Page Prototypebased Modellevel Explanations For Graph Neural Networks**arXiv ID:** 2210.17159 **Authors:** Yong-Min Shin, Sun-Woo Kim, Won-Yong Shin **Published:** 2022-10-31T09:10:06Z **Abstract:** Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored...Votes: 0GitHub stars: 3
- Github Repo ManagementClone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.Votes: 0GitHub stars: 3