Let Brain Rhythm Shape Machine Intelligence for Connecting Dots on Graphs
Jiaqi Ding, Tingting Dan, Zhixuan Zhou, Guorong Wu
In both neuroscience and artificial intelligence (AI), it is well-established that neural “coupling” gives rise to dynamically distributed systems. These systems exhibit self-organized spatiotemporal patterns of synchronized neural oscillations, enabling the representation of abstract concepts. By capitalizing on the unprecedented amount of human neuroimaging data, we hypothesize that a deeper theoretical insight into the neural mechanisms governing brain rhythms could inspire the next-generation design principles for machine learning algorithms with greater efficiency and robustness. To that end, we introduce a physics-informed deep learning framework for $\underline{B}rain$ $\underline{R}hythm$ $\underline{I}dentification$ by $\underline{K}uramoto$ and $\underline{C}ontrol$ (coined *BRICK*) to characterize the synchronization of neural oscillations that shapes the dynamics of evolving cognitive states. Given that graph data, like brain networks, is structurally interconnected and behaviorally dynamic, we propose that the success of modeling brain rhythms via an artificial dynamic system of coupled oscillations gives rise to the foundational principle for the development of brain-inspired machine intelligence through the common mechanism of synchronization. By conceptualizing each node as an oscillator coupled with neighbors, we push the limits of conventional graph neural networks from the cliché of graph heat diffusion to a new realm of compressing graph feature representation via oscillatory synchronization. The experiments show that our new graph learning mechanism not only effectively addresses the over-smoothing issue in GNNs but also manifests the potential of reasoning and solving challenging problems on graphs.