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

TL;DR

This paper introduces a bio-inspired learning paradigm using oscillatory link strengths, enabling neural networks to rapidly adapt to new contexts and unseen data without supervision, mimicking brain-like flexibility.

Contribution

It proposes a novel oscillation-based learning method that enhances neural networks' ability to adapt quickly and generalize across multiple contexts, including unseen ones.

Findings

Networks can adapt to subtle contextual changes rapidly.

The paradigm enables prediction of dynamics in multiple contexts.

It is agnostic to neural network specifics, broadening applicability.

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 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.