Winfree 振荡神经网络（WONN）在 ImageNet 等基准上实现可扩展的高参数效率表现

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Abstract:Oscillations and synchronization are widely believed to play a fundamental role in representation and computation. However, existing machine learning approaches based on synchronization dynamics have largely been confined to specialized settings such as object discovery, with limited evidence of scalability to standard vision benchmarks or logic reasoning tasks. We propose the Winfree Oscillatory Neural Network (WONN), a dynamical neural architecture based on generalized Winfree dynamics. WONN evolves representations on the torus $(S^1)^d$ through structured oscillatory interactions, combining phase-based inductive biases with flexible and hierarchical interaction mechanisms instantiated as either fixed trigonometric mappings or learnable neural networks. We evaluate WONN on image recognition and complex reasoning tasks, including CIFAR, ImageNet, Maze-hard, and Sudoku. Across these domains, WONN achieves competitive or superior performance with strong parameter efficiency. In particular, WONN is, to our knowledge, the first synchronization-based oscillatory architecture to scale competitively to ImageNet-1K. Furthermore, on Maze-hard, WONN achieves 80.1% accuracy using only 1% of the parameters of prior state-of-the-art models. These results suggest that structured oscillatory dynamics provide a scalable and parameter-efficient alternative to conventional neural architectures.

Comments:	Project page: this https URL
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2605.20922 [cs.LG]
	(or arXiv:2605.20922v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2605.20922 arXiv-issued DOI via DataCite

Submission history

From: Jiawen Dai [view email]
[v1] Wed, 20 May 2026 09:08:00 UTC (5,745 KB)
[v2] Wed, 11 Nov 2026 10:20:27 UTC (5,745 KB)