标题： 探索通过振荡同步全息蓝图在图上连接点的脑启发机器智能 显示英文标题

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

摘要： 神经耦合在神经科学和人工智能中表现为动态振荡模式，这些模式编码抽象概念。 为此，我们假设对支配脑节律的神经机制的更深入理解可以启发下一代机器学习算法的设计原则，从而提高效率和鲁棒性。 基于这一想法，我们首先通过自发同步神经振荡的干涉来建模不断变化的脑节律，称为全脑（HoloBrain）。 使用耦合振荡的人工动力系统对脑节律建模的成功，促使基于共享同步机制的“第一原理”用于脑启发的机器智能，称为全图（HoloGraph）。 这一原理使图神经网络能够超越传统的热扩散范式，转向振荡同步建模。 我们的全图框架不仅有效缓解了图神经网络中的过度平滑问题，还展示了在图上进行推理和解决挑战性问题的强大潜力。 显示英文摘要

摘要： 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.