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Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings

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  • Daniele Marinazzo
  • Guorong Wu
  • Mario Pellicoro
  • Leonardo Angelini
  • Sebastiano Stramaglia

Abstract

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.

Suggested Citation

  • Daniele Marinazzo & Guorong Wu & Mario Pellicoro & Leonardo Angelini & Sebastiano Stramaglia, 2012. "Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
  • Handle: RePEc:plo:pone00:0045026
    DOI: 10.1371/journal.pone.0045026
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    References listed on IDEAS

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    1. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Marco Alberto Javarone & Daniele Marinazzo, 2018. "Dilution of Ferromagnets via a Random Graph-Based Strategy," Complexity, Hindawi, vol. 2018, pages 1-11, April.
    2. Jasleen Gundh & Awaneesh Singh & R K Brojen Singh, 2015. "Ordering Dynamics in Neuron Activity Pattern Model: An Insight to Brain Functionality," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.

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