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Basin entropy in Boolean network ensembles.

Peter Krawitz1, Ilya Shmulevich

  • 1Institute for Systems Biology, Seattle, Washington 98103, USA.

Physical Review Letters
|May 16, 2007
PubMed
Summary
This summary is machine-generated.

We introduce basin entropy, a new measure for complex systems

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Area of Science:

  • Complex systems
  • Network theory
  • Information theory

Background:

  • Complex dynamical systems partition state space into basins of attraction.
  • Information processing capacity relates to this state space partitioning.

Purpose of the Study:

  • Introduce basin entropy as a novel network parameter.
  • Quantify the information storage complexity of dynamical systems.

Main Methods:

  • Studied ensembles of random Boolean networks.
  • Analyzed the scaling of basin entropy with system size.

Main Results:

  • Basin entropy scales with system size exclusively in critical regimes.
  • Optimal information partitioning occurs at the boundary between order and disorder.

Conclusions:

  • Basin entropy effectively measures information storage capacity.
  • Criticality in complex systems is crucial for optimal information processing.