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Neural coupling in both neuroscience and AI emerges dynamic oscillatory patterns that encode abstract concepts. To that end, we hypothesize that a deeper understanding of the neural mechanisms that determine brain rhythms could inspire next-generation design principles for machine learning algorithms, leading to greater efficiency and robustness. Following this notion, we first model the evolving brain rhythm by the interference between spontaneously synchronized neural oscillations (termed HoloBrain). The success of modeling brain rhythms via an artificial dynamic system of coupled oscillations gives rise to the “first principle” for emerging brain-inspired machine intelligence through the common mechanism of synchronization (termed HoloGraph), enabling graph neural networks (GNNs) to move beyond conventional heat diffusion paradigms toward modeling oscillatory synchronization. Our HoloGraph not only effectively addresses the over-smoothing issue in GNNs but also manifests the potential of reasoning and solving challenging problems on graphs. Neural coupling is a challenge in understanding both brain function and advancing machine intelligence. Here, the authors introduce HoloBrain and HoloGraph, a brain-inspired framework that models oscillatory synchronization to overcome limitations of graph neural networks and enable more efficient, robust learning.
Dan et al. (Fri,) studied this question.