Oscillatory Neural Networks

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Oscillatory Neural Dynamics over Sheaves
Oct 7, 2026Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina +5Graph Neural Networks Graph Representation Learning
FaBiT, University of Bologna · Department of Computer Science, University of Pisa · Department of Computer, Control and Management Engineering, Sapienza University of Rome +1
Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware
Oct 5, 2026Sowjanya Tammali, Wilkie Olin-AmmentorpEquilibrium Propagation On-Device Training
Computer Science, Missouri University of Science and Technology, 500 West 15th Street, Rolla, 65409, MO, USA. · Mathematics & Computer Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, 60439, IL, USA.
Pneumatic neurons for soft robots enable inflate-and-fire networks for rhythmic motion
Sep 14, 2026Dongting Li, Michael Tolley, Nick GravishOscillatory Neural Networks Robot Locomotion
MAE Department, UC San Diego, La Jolla, 92093, US
Neither Adversarial Training Nor Purification: Emergent Adversarial Robustness from Oscillatory Predictive Learning
Sep 8, 2026Mohammed-Yassine Habibi, Klea Ziu, Martin Takáč +1Oscillatory Neural Networks Adversarial Robustness
Ecole polytechnique, IPP, France · Ecole Nationale des Ponts et Chaussées, IPP, France · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE +1
Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks
Jul 17, 2026Filip Sabo, Aida Todri-SanialOscillatory Neural Networks Combinatorial Optimization
NanoComputing Research Lab, Electrical Engineering Department Eindhoven University of Technology Eindhoven, the Netherlands
Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings
Jul 1, 2026Riley Acker, Aman Desai, Garrett Kenyon +1Oscillatory Neural Networks Neuromorphic Computing
1Computing and Artificial Intelligence (CAI) Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, NM 87545, USA · 3Theoretical Division , Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Attention as Frustrated Synchronization
Jun 17, 2026Joshua NunleyOscillatory Neural Networks Autoregressive Language Modeling
Cognitive Science Program Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington
Winfree Oscillatory Neural Network
May 20, 2026Jiawen Dai, Yue SongDynamic Neural Networks Oscillatory Neural Networks
3Shanghai Jiao Tong University · 1Shanghai Qi Zhi Institute · College of AI, Tsinghua University
From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination
May 3, 2026Tingting Dan, Guorong WuOscillatory Neural Networks Spiking Neural Networks
Advanced Computational Medicine Laboratory (ACMLab) · Department of Psychiatry & Department of Computer Science
Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
Apr 9, 2026Mingqing Xiao, Yansen Wang, Dongqi Han +2Oscillatory Neural Networks Vision Transformer
Microsoft Research Asia

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Oscillatory Neural Dynamics over Sheaves

Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina, Alessio Borgi+4

FaBiT, University of Bologna · Department of Computer Science, University of Pisa · Department of Computer, Control and Management Engineering, Sapienza University of Rome+1

Graph Neural Networks Graph Representation Learning Sheaf Neural Networks Oscillatory Neural Networks GNN Expressivity

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Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively influence each other. Sheaf neural networks enrich graph propagation through matrix-valued transport between stalks; still, this expressivity alone does not automatically imply effective long-range communication. We introduce ONDA, a long-range graph learning framework based on operator-valued information waves. Stalk-valued representations evolve through second-order dynamics governed by learned sheaf transport operators, combining wave-like propagation with expressive local geometry. We characterize long-range influence through a stalk-wise sensitivity analysis and show that the cross-influence never vanishes. Across long-range propagation, severe graph bottlenecks, graph transfer, and heterophilic benchmarks, ONDA consistently improves over scalar wave propagation, diffusive sheaf baselines, and state-of-the-art models, demonstrating the benefit of coupling wave dynamics with matrix-valued transport.

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