holobrain-holograph-oscillatory-gnn

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HoloBrain and HoloGraph framework: modeling brain rhythms through coupled oscillatory synchronization and applying this principle to graph neural networks. Addresses GNN over-smoothing and enables reasoning on graphs through oscillatory dynamics.

hiyenwong By hiyenwong schedule Updated 6/3/2026
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name: holobrain-holograph-oscillatory-gnn description: > HoloBrain and HoloGraph framework: modeling brain rhythms through coupled oscillatory synchronization and applying this principle to graph neural networks. Addresses GNN over-smoothing and enables reasoning on graphs through oscillatory dynamics.

HoloBrain & HoloGraph: Oscillatory Synchronization for Brain Modeling and GNNs

Paper: arXiv:2602.00057 Authors: Tingting Dan, Jiaqi Ding, Guorong Wu Categories: q-bio.NC, cs.LG Year: 2026

Overview

Two-part framework connecting neuroscience and machine learning through oscillatory synchronization:

  1. HoloBrain: Models brain rhythms through interference of spontaneously synchronized neural oscillations
  2. HoloGraph: Applies synchronization principle to GNNs, enabling oscillatory computation beyond heat diffusion

Key Concepts

Neural Oscillatory Synchronization

  • Brain rhythms emerge from synchronization of coupled neural oscillators
  • Phase relationships between oscillators encode abstract concepts
  • Synchronization patterns dynamically reconfigure for different cognitive functions

HoloGraph: Oscillatory GNNs

  • Each node is an oscillator; edges define coupling strength
  • Information propagation through phase synchronization rather than feature diffusion
  • Addresses over-smoothing: oscillatory dynamics maintain distinct phase patterns even after many iterations

Methodology

HoloGraph Implementation

  1. Replace conventional GNN message passing with oscillatory synchronization
  2. Node states as complex numbers (amplitude + phase)
  3. Information encoded in phase relationships
  4. Synchronization dynamics enable iterative refinement
  5. Readout maps final phase patterns to predictions

Advantages over Traditional GNNs

  • No over-smoothing
  • Natural multi-scale representation
  • Biological plausibility
  • Enhanced reasoning capability

Applications

  • Brain rhythm modeling
  • Graph classification
  • Molecular property prediction
  • Knowledge graph reasoning

Key Insights

  1. Shared mechanism: same oscillatory synchronization for brain rhythms and graph computation
  2. Over-smoothing solution through oscillatory dynamics
  3. Phase as richer representation
  4. Biology inspires computation

References

  • Dan, T., Ding, J., & Wu, G. (2026). HoloBrain & HoloGraph. arXiv:2602.00057.
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