用脑波同步机制改进图神经网络，解决过平滑问题
Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization
- 用耦合振荡器模拟脑波动态，构建新型图学习框架
- 在多个数据集上显著缓解过平滑，提升推理能力
- 适合研究脑启发式AI与复杂图结构建模的学者
神经科学与人工智能中的神经耦合均表现为动态振荡模式，编码抽象概念。我们提出假设：深入理解脑节律的神经机制可启发下一代机器学习算法的设计，从而提升效率与鲁棒性。基于此，我们首先通过自发同步神经振荡的干涉，建模动态脑节律，称为HoloBrain。利用人工动力系统模拟脑节律的成功，推动形成基于共享同步机制的脑启发式机器智能“第一性原理”，即HoloGraph。该原理使图神经网络突破传统热扩散范式，转向建模振荡同步。所提出的HoloGraph框架不仅有效缓解了图神经网络中的过平滑问题，还在复杂图任务上展现出强大的推理与求解潜力。
原文摘要 · Abstract (English)
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.
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