Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization

TL;DR

This paper introduces a brain-inspired framework called HoloGraph that models neural oscillations to improve graph neural networks, addressing over-smoothing and enhancing reasoning capabilities.

Contribution

It proposes a novel synchronization-based principle for graph neural networks, inspired by neural rhythms, to enhance their performance and robustness.

Findings

HoloGraph mitigates over-smoothing in GNNs

It models oscillatory synchronization for better reasoning

Demonstrates improved performance on graph tasks

Abstract

Neural coupling in both neuroscience and artificial intelligence emerges as dynamic oscillatory patterns that encode abstract concepts. To this end, we hypothesize that a deeper understanding of the neural mechanisms governing brain rhythms can inspire next-generation design principles for machine learning algorithms, leading to improved efficiency and robustness. Building on this idea, we first model evolving brain rhythms through the interference of spontaneously synchronized neural oscillations, termed HoloBrain. The success of modeling brain rhythms using an artificial dynamical system of coupled oscillations motivates a "first principle" for brain-inspired machine intelligence based on a shared synchronization mechanism, termed HoloGraph. This principle enables graph neural networks to move beyond conventional heat diffusion paradigms toward modeling oscillatory synchronization. Our HoloGraph framework not only effectively mitigates the over-smoothing problem in graph neural networks but also demonstrates strong potential for reasoning and solving challenging problems on graphs.