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Integrated Information Theory (IIT) quantifies consciousness by measuring integrated information (Φ). A new measure, Φ*, is introduced, satisfying theoretical bounds and applicable to neural data for consciousness research and biological network analysis.

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Area of Science:

  • Neuroscience
  • Information Theory
  • Cognitive Science

Background:

  • Consciousness is linked to the brain's capacity for information integration.
  • Integrated Information Theory (IIT) mathematically quantifies this integration as Φ.
  • Existing practical measures of Φ do not meet theoretical requirements.

Purpose of the Study:

  • To develop a practical and theoretically sound measure of integrated information.
  • To enable empirical evaluation of IIT using neural data.
  • To provide a tool for network analysis in biology.

Main Methods:

  • Derived a novel measure, Φ*, based on mismatched decoding from information theory.
  • Ensured Φ* satisfies lower (0) and upper bounds.
  • Developed an analytical expression for Φ* under Gaussian assumptions.

Main Results:

  • The new measure Φ* is properly bounded, unlike previous practical measures.
  • Φ* is analytically solvable for Gaussian systems, facilitating application to experimental data.
  • Demonstrated Φ* as a valid measure of integrated information.

Conclusions:

  • Φ* offers a practical and theoretically valid method for computing integrated information.
  • This measure can advance empirical research on IIT and consciousness.
  • Φ* serves as a valuable tool for broader biological network analysis.