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Null models for comparing information decomposition across complex systems.

Alberto Liardi1,2,3, Fernando E Rosas3,4,5,6, Robin L Carhart-Harris7

  • 1Department of Computing, Imperial College London, London, United Kingdom.

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Summary
This summary is machine-generated.

Null Models for Information Theory (NuMIT) offers a novel, non-linear normalization method for complex systems. This technique enables robust cross-dataset comparisons and significance testing in information theory analyses.

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Area of Science:

  • Information Theory
  • Complex Systems Analysis
  • Computational Neuroscience

Background:

  • Information theory's universality allows application to diverse complex systems.
  • Standard normalization methods for information-theoretic measures have limitations, hindering cross-dataset comparisons.
  • Partial Information Decomposition (PID) provides a framework for analyzing information sharing in complex systems.

Purpose of the Study:

  • To introduce Null Models for Information Theory (NuMIT), a novel null model-based non-linear normalization procedure.
  • To overcome limitations of standard entropy-based normalization techniques.
  • To enable robust cross-dataset comparisons and significance testing for information-theoretic measures, particularly within PID analyses.

Main Methods:

  • Developed NuMIT, a null model-based non-linear normalization procedure.
  • Implemented practical versions of NuMIT for systems with varying statistics.
  • Validated the method using synthetic models and human neuroimaging data.

Main Results:

  • NuMIT demonstrates improved performance over standard entropy-based normalization.
  • The method provides a reliable approach for characterizing complex systems.
  • NuMIT facilitates meaningful cross-dataset comparisons and significance testing for PID.

Conclusions:

  • NuMIT is a robust and reliable tool for normalizing information-theoretic measures in complex systems.
  • The technique enhances the comparability of data across different studies and datasets.
  • NuMIT offers a significant advancement for Partial Information Decomposition analyses.