arXiv cs.NE Neural and Evolutionary Computing

Minsuk Jang, Changick Kim: SPARTA: Advancing Sparse Attention in Spiking Neural Networks via Spike-Timing-Based Prioritization https://arxiv.org/abs/2508.01646 https://arxiv.org/pdf/2508.01646 https://arxiv.org/html/2508.01646

Shiko Kudo: From Taylor Series to Fourier Synthesis: The Periodic Linear Unit https://arxiv.org/abs/2508.01175 https://arxiv.org/pdf/2508.01175 https://arxiv.org/html/2508.01175

Advey Nandan, Cheng-Ting Chou, Amrit Kurakula, Cole Blondin, Kevin Zhu, Vasu Sharma, Sean O'Brien: Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact https://arxiv.org/abs/2508.00903 https://arxiv.org/pdf/2508.00903 https://arxiv.org/html/2508.00903

Sidney Pontes-Filho, Stefano Nichele, Mikkel Lepper{\o}d: Reservoir Computing with Evolved Critical Neural Cellular Automata https://arxiv.org/abs/2508.02218 https://arxiv.org/pdf/2508.02218 https://arxiv.org/html/2508.02218

[2025-08-05 Tue (UTC), 1 new article found for csNE Neural and Evolutionary Computing]

Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Mateja Jamnik, Giuseppe Marra: Foundations of Interpretable Models https://arxiv.org/abs/2508.00545 https://arxiv.org/pdf/2508.00545 https://arxiv.org/html/2508.00545

Ma, Lin, Li, Quan, Zhou, Zhang, Zhong, Jia, Zhu, Meng, Zhou, An: E2ATST: A Temporal-Spatial Optimized Energy-Efficient Architecture for Training Spiking Transformer https://arxiv.org/abs/2508.00475 https://arxiv.org/pdf/2508.00475 https://arxiv.org/html/2508.00475

Barry M. Dillon, Jim Harkin, Aqib Javed: Anomaly detection with spiking neural networks for LHC physics https://arxiv.org/abs/2508.00063 https://arxiv.org/pdf/2508.00063 https://arxiv.org/html/2508.00063

Zeqi Zheng, Zizheng Zhu, Yingchao Yu, Yanchen Huang, Changze Lv, Junfeng Tang, Zhaofei Yu, Yaochu Jin: STF: Shallow-Level Temporal Feedback to Enhance Spiking Transformers https://arxiv.org/abs/2508.00387 https://arxiv.org/pdf/2508.00387 https://arxiv.org/html/2508.00387

Kebin Sun, Tao Jiang, Ran Cheng, Yaochu Jin, Kay Chen Tan: Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning https://arxiv.org/abs/2508.00380 https://arxiv.org/pdf/2508.00380 https://arxiv.org/html/2508.00380

Urba\'nczyk, Urba\'nczyk, Kr\'ol, Rutkowski, Kisiel-Dorohinicki: Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics https://arxiv.org/abs/2508.00229 https://arxiv.org/pdf/2508.00229 https://arxiv.org/html/2508.00229

J. Fernando Hernandez-Garcia, Shibhansh Dohare, Jun Luo, Rich S. Sutton: Reinitializing weights vs units for maintaining plasticity in neural networks https://arxiv.org/abs/2508.00212 https://arxiv.org/pdf/2508.00212 https://arxiv.org/html/2508.00212

[2025-08-04 Mon (UTC), 4 new articles found for csNE Neural and Evolutionary Computing]

Ranxi Lin, Canming Yao, Jiayi Li, Weihang Liu, Xin Lou, Pingqiang Zhou: Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields https://arxiv.org/abs/2507.23033 https://arxiv.org/pdf/2507.23033 https://arxiv.org/html/2507.23033

Farah Baracat, Giacomo Indiveri, Elisa Donati: Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware https://arxiv.org/abs/2507.23474 https://arxiv.org/pdf/2507.23474 https://arxiv.org/html/2507.23474

Bingjia Xiao, Tao Chen, Wenbin Zhang, Xin Qian, Puqing Jiang: Hybrid Particle Swarm Optimization for Fast and Reliable Parameter Extraction in Thermoreflectance https://arxiv.org/abs/2507.22960 https://arxiv.org/pdf/2507.22960 https://arxiv.org/html/2507.22960

Kun Jiang: From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems https://arxiv.org/abs/2507.22916 https://arxiv.org/pdf/2507.22916 https://arxiv.org/html/2507.22916

[2025-08-01 Fri (UTC), 3 new articles found for csNE Neural and Evolutionary Computing]

Maciej Satkiewicz: Tapping into the Black Box: Uncovering Aligned Representations in Pretrained Neural Networks https://arxiv.org/abs/2507.22832 https://arxiv.org/pdf/2507.22832 https://arxiv.org/html/2507.22832

F. Gallavotti, A. Zaccone: Amorphous Solid Model of Vectorial Hopfield Neural Networks https://arxiv.org/abs/2507.22787 https://arxiv.org/pdf/2507.22787 https://arxiv.org/html/2507.22787

Diao, Li, Zeng, Cai, Luo, Yang, Coello: Nearest-Better Network for Visualizing and Analyzing Combinatorial Optimization Problems: A Unified Tool https://arxiv.org/abs/2507.22440 https://arxiv.org/pdf/2507.22440 https://arxiv.org/html/2507.22440

Theviyanthan Krishnamohan, Paul Harvey: OpenRASE: Service Function Chain Emulation https://arxiv.org/abs/2507.22131 https://arxiv.org/pdf/2507.22131 https://arxiv.org/html/2507.22131

Sergii Kavun: Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization https://arxiv.org/abs/2507.22090 https://arxiv.org/pdf/2507.22090 https://arxiv.org/html/2507.22090

Joy Bose: Pendulum Model of Spiking Neurons https://arxiv.org/abs/2507.22146 https://arxiv.org/pdf/2507.22146 https://arxiv.org/html/2507.22146

[2025-07-31 Thu (UTC), 1 new article found for csNE Neural and Evolutionary Computing]

Qingya Li, Shengcai Liu, Wenjie Chen, Juan Zou, Ke Tang, Xin Yao: Knowledge-Guided Memetic Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs https://arxiv.org/abs/2507.21740 https://arxiv.org/pdf/2507.21740 https://arxiv.org/html/2507.21740

Daniel Szelogowski: Hebbian Memory-Augmented Recurrent Networks: Engram Neurons in Deep Learning https://arxiv.org/abs/2507.21474 https://arxiv.org/pdf/2507.21474 https://arxiv.org/html/2507.21474

Alexander Yeung, Peter DelMastro, Arjun Karuvally, Hava Siegelmann, Edward Rietman, Hananel Hazan: Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators https://arxiv.org/abs/2507.21377 https://arxiv.org/pdf/2507.21377 https://arxiv.org/html/2507.21377

[2025-07-30 Wed (UTC), 3 new articles found for csNE Neural and Evolutionary Computing]

Santlal Prajapati, Susmita Sur-Kolay, Soumyadeep Dutta: Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method https://arxiv.org/abs/2507.20998 https://arxiv.org/pdf/2507.20998 https://arxiv.org/html/2507.20998