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Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks

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  • Murilo S Baptista
  • Josué X de Carvalho
  • Mahir S Hussein

Abstract

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Suggested Citation

  • Murilo S Baptista & Josué X de Carvalho & Mahir S Hussein, 2008. "Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-11, October.
    • Handle: RePEc:plo:pone00:0003479
    DOI: 10.1371/journal.pone.0003479
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    References listed on IDEAS

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    1. Perc, Matjaž, 2007. "Effects of small-world connectivity on noise-induced temporal and spatial order in neural media," Chaos, Solitons & Fractals, Elsevier, vol. 31(2), pages 280-291.
    2. V Anne Smith & Jing Yu & Tom V Smulders & Alexander J Hartemink & Erich D Jarvis, 2006. "Computational Inference of Neural Information Flow Networks," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-14, November.
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