Sign inGet started
Paper · arXiv

Winfree Oscillatory Neural Network

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 ...

AuthorsJiawen Dai, Yue Song
Primary categorycs.LG
Published20 May 2026
Last updated at source20 May 2026
cs.LGcs.AIcs.CV
Embed this entry
<iframe src="https://silytics.si/embed/item/d4d9d4b2-3f2f-42bc-a4f8-1a334e539d1a" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.