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Measuring the Complexity of Consciousness.

Xerxes D Arsiwalla1,2,3, Paul Verschure1,2,3,4

  • 1Institute for Bioengineering of Catalunya, Barcelona, Spain.

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Summary
This summary is machine-generated.

Quantifying consciousness computationally is challenging for realistic brain networks. This article reviews complexity measures for consciousness, focusing on clinical applications and overcoming computational hurdles.

Keywords:
clinical scalescomplexity measurescomputational neuroscienceconsciousness in the clinicinformation theory

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Information Theory

Background:

  • Understanding consciousness requires robust computational measures of global brain states.
  • Information-theoretic complexity, like integrated information, is a promising approach for quantifying conscious awareness.
  • Clinical disorders of consciousness necessitate reliable quantitative diagnostic and therapeutic tools.

Purpose of the Study:

  • To provide a comprehensive lookup of computational measures for consciousness.
  • To emphasize the clinical applicability of these measures.
  • To address challenges in applying complexity measures to realistic brain networks.

Main Methods:

  • Review of principle-based complexity measures.
  • Inclusion of empirical measures tested on patients.
  • Analysis of computational costs and network dimensions.

Main Results:

  • Existing complexity measures face computational challenges with large-scale brain networks.
  • Clinical applicability of consciousness measures is crucial for diagnosis and therapy.
  • Potential resolutions for applying complexity measures to realistic networks are discussed.

Conclusions:

  • Formalizing computational measures for consciousness is vital for neuroscience and clinical practice.
  • Addressing computational limitations is key to advancing the application of complexity measures.
  • This work serves as a guide for researchers and clinicians investigating consciousness.