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Information transfer at multiple scales.

Max Lungarella1, Alex Pitti, Yasuo Kuniyoshi

  • 1ERATO Asada Synergistic Intelligence Project, JST, The University of Tokyo, 113-8656 Tokyo, Japan. lunga@ifi.uzh.ch

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 1, 2008
PubMed
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This study introduces a new wavelet-based method to measure information flow across different time scales in complex systems. The technique effectively maps interactions and dependencies within and between systems.

Area of Science:

  • Complex Systems Science
  • Information Theory
  • Network Analysis

Background:

  • Mapping interactions in complex systems is crucial for understanding dependencies.
  • Information flow occurs across multiple spatial and temporal scales.
  • Existing methods may not fully capture multi-scale dynamics.

Purpose of the Study:

  • To develop a novel method for measuring directional information transfer.
  • To analyze information flow across multiple time scales.
  • To validate the proposed method on diverse datasets.

Main Methods:

  • Wavelet-based extension of transfer entropy.
  • Analysis of directional information flow.
  • Application to artificial, physiological, and robotic time series data.

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Main Results:

  • The proposed wavelet-based transfer entropy effectively measures multi-scale information transfer.
  • Demonstrated effectiveness on artificial networks, physiological recordings, and simulated robot data.
  • Identified directional dependencies at various temporal resolutions.

Conclusions:

  • The wavelet-based transfer entropy is a powerful tool for analyzing complex system interactions.
  • The method provides insights into multi-scale information dynamics.
  • Potential for broader applications in systems science and engineering.