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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Maximal information transfer and behavior diversity in Random Threshold Networks.

M Andrecut1, D Foster, H Carteret

  • 1Institute for Biocomplexity and Informatics, University of Calgary, Alberta, Canada. mandrecu@ucalgary.com

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 8, 2009
PubMed
Summary
This summary is machine-generated.

Dynamically critical Random Threshold Networks (RTNs) maximize information transfer and diverse behaviors. These critical conditions are vital for coordinated causal systems in physics and biology.

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Area of Science:

  • Complex systems
  • Network science
  • Theoretical neuroscience

Background:

  • Random Threshold Networks (RTNs) model diluted, non-symmetric systems like spin glasses, neural networks, and gene regulatory networks.
  • RTNs represent a general framework for coordinated causal systems.

Purpose of the Study:

  • Investigate conditions for maximal information transfer in RTNs.
  • Determine conditions for maximal behavior diversity in RTNs.
  • Explore the role of these conditions in physical and biological systems.

Main Methods:

  • Analysis of pairwise mutual information within RTNs.
  • Evaluation of correlated behavior diversity in RTNs.
  • Examination of network dynamics near the critical region.

Main Results:

  • Pairwise mutual information is maximized in dynamically critical RTNs.
  • Correlated behavior diversity peaks in slightly chaotic networks, near the critical region.
  • Critical networks exhibit maximal coordinated, diverse dynamical behavior over time and space.

Conclusions:

  • Dynamically critical networks are optimal for information transmission and behavioral diversity.
  • These findings highlight the significance of critical dynamics in biological and physical systems.
  • Maximal information transfer and behavior diversity are achieved in networks poised at the edge of chaos.