# Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks

arXiv: https://arxiv.org/abs/2502.08644 · HTML: https://mlsanity.com/paper/2502.08644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Pulse-Driven Neural Architecture: Learnable Oscillatory Dynamics for Robust Continuous-Time Sequence Processing](https://mlsanity.com/paper/2603.00153/) (2026)

## Abstract (arXiv)

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 novel 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.