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Statistical complexity is maximized in a small-world brain

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  • Teck Liang Tan
  • Siew Ann Cheong

Abstract

In this paper, we study a network of Izhikevich neurons to explore what it means for a brain to be at the edge of chaos. To do so, we first constructed the phase diagram of a single Izhikevich excitatory neuron, and identified a small region of the parameter space where we find a large number of phase boundaries to serve as our edge of chaos. We then couple the outputs of these neurons directly to the parameters of other neurons, so that the neuron dynamics can drive transitions from one phase to another on an artificial energy landscape. Finally, we measure the statistical complexity of the parameter time series, while the network is tuned from a regular network to a random network using the Watts-Strogatz rewiring algorithm. We find that the statistical complexity of the parameter dynamics is maximized when the neuron network is most small-world-like. Our results suggest that the small-world architecture of neuron connections in brains is not accidental, but may be related to the information processing that they do.

Suggested Citation

  • Teck Liang Tan & Siew Ann Cheong, 2017. "Statistical complexity is maximized in a small-world brain," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0183918
    DOI: 10.1371/journal.pone.0183918
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    References listed on IDEAS

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    1. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
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