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A novel perturbation based compression complexity measure for networks.

Mohit Virmani1, Nithin Nagaraj1

  • 1Consciousness Studies Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru, Karnataka, India.

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Summary
This summary is machine-generated.

We introduce a new brain network complexity measure, , bridging Integrated Information Theory (IIT) and Perturbational Complexity Index (PCI). This novel approach offers a computationally tractable and less state-dependent method for assessing brain complexity and consciousness.

Keywords:
Mathematical biosciencesNeuroscience

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Information Theory

Background:

  • Integrated Information Theory (IIT) is a leading framework for consciousness, but faces limitations like computational intractability and state dependence.
  • Perturbational Complexity Index (PCI) is a clinical measure of consciousness, yet its link to integrated information is weak.

Purpose of the Study:

  • To propose a novel complexity measure for brain networks, , that bridges IIT and PCI.
  • To develop a computationally tractable and less state-dependent measure for brain complexity.

Main Methods:

  • Developed a new complexity measure, , using a perturbation-based compression-complexity approach.
  • Founded on lossless data compression principles, computed via perturbation.
  • Ensured is mathematically bounded and has linear scaling for computational efficiency.

Main Results:

  • exhibits negligible current state dependence, unlike IIT's Φ.
  • The measure shows a similar hierarchy to <Φ> in multi-node networks.
  • Demonstrated a rich interplay between network differentiation, integration, and node entropy.

Conclusions:

  • offers a promising heuristic for characterizing brain network complexity.
  • This measure may contribute to developing a quantitative measure of consciousness.
  • Potential applications include estimating brain complexity from neurophysiological data.