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Graph neural networks for integrated information and major complex estimation.

Tadaaki Hosaka1

  • 1School of Science and Technology, Meiji University, Kanagawa, Japan.

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Graph neural networks (GNNs) estimate integrated information and major complexes in complex systems. This approach offers a practical method for analyzing large systems, inspired by brain configurations.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Complex systems theory

Background:

  • Integrated Information Theory (IIT) 3.0 quantifies consciousness but is computationally intensive for large systems.
  • Calculating integrated information and identifying the major complex are prohibitive for systems beyond a few nodes.
  • Existing methods struggle with the hierarchical complexity inherent in IIT 3.0.

Purpose of the Study:

  • To develop a Graph Neural Network (GNN) model for estimating system-level integrated information and major complexes within IIT 3.0.
  • To overcome the computational limitations of exact calculations for large and complex systems.
  • To provide a scalable framework for analyzing consciousness-related properties in intricate networks.

Main Methods:

  • Proposed a GNN model incorporating transformer convolutions and multi-head attention mechanisms.
  • Evaluated the model using exact solutions for systems with 5, 6, and 7 nodes.
  • Conducted non-extrapolative and extrapolative training/testing experiments to assess model generalization.
  • Examined scaling behavior across different graph topologies (tree-like, fully connected, loop-containing).
  • Qualitatively analyzed a 100-node split-brain-like system.

Main Results:

  • The GNN model provides approximate estimates for integrated information and major complex size in larger systems.
  • Approximate estimates qualitatively preserve patterns observed in smaller systems.
  • Demonstrated emergent 'local integration' in weakly coupled subsystems, transitioning to 'global integration' with increased connectivity.
  • Observed a single subsystem forming a major complex at low connectivity, expanding to a larger system complex as connectivity increases.

Conclusions:

  • The GNN-based framework offers a practical approach for the qualitative analysis of integrated information and major complexes in large-scale systems.
  • The model successfully captures the transition from local to global integration based on system connectivity.
  • Findings suggest GNNs are a viable tool for exploring IIT 3.0 in complex, brain-inspired architectures.