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  • Open Access

Rhythmic sharing: A bioinspired paradigm for zero-shot adaptive learning in neural networks

Hoony Kang* and Wolfgang Losert†

  • *Contact author: kang.hoony.2@gmail.com
  • †Contact author: wlosert@umd.edu
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Phys. Rev. Research 8, 013267 – Published 10 March, 2026

DOI: https://doi.org/10.1103/fdc2-ljj6

Abstract

The brain rapidly adapts to new contexts and learns from limited data, a coveted characteristic that artificial intelligence (AI) algorithms struggle to mimic. Inspired by the mechanical oscillatory rhythms of neural cells, we developed a learning paradigm utilizing link strength oscillations, where learning is associated with the coordination of these oscillations. Link oscillations can rapidly change coordination, allowing the network to sense and adapt to subtle contextual changes without supervision. The network becomes a generalist AI architecture, capable of predicting dynamics of multiple contexts, including unseen ones. These results make our paradigm a powerful starting point for models of cognition. Because our paradigm is agnostic to specifics of the neural network, our study opens doors for introducing rapid adaptive learning into leading AI models.

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Phys. Rev. Research 8, 013267– Published 10 March, 2026
  • Received 10 September 2025
  • Accepted 20 February 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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