mlsanityData to 7 Oct 2026

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

arXiv 2602.00057 ↗First posted 20 Jan 2026q-bio.NC cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 76 references that are in the map. First posted on arXiv 20 Jan 2026.

Builds on

25 of 76 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2018Neural Ordinary Differential Equationscited by 3,733
  6. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
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13 more
  1. 2021GRAND: Graph Neural Diffusioncited by 258
  2. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  3. 2020Liquid Time-constant Networkscited by 160
  4. 2022Graph-Coupled Oscillator Networkscited by 123
  5. 2019Continuous Graph Neural Networkscited by 114
  6. 2018SpectralNet: Spectral Clustering using Deep Neural Networkscited by 94
  7. 2020Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networkscited by 57
  8. 2018Geometric Scattering for Graph Data Analysiscited by 49
  9. 2024Artificial Kuramoto Oscillatory Neuronscited by 37
  10. 2023From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approachcited by 15
  11. 2014Controllability of Brain Networkscited by 12
  12. 2023Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionalscited by 7
  13. 2016Brain and Cognitive Reserve: Translation via Network Control Theorycited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Taming Epilepsy: Mean Field Control of Whole-Brain Dynamicscited by 0
  2. 2026From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordinationcited by 0
  3. 2026BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspacescited by 0