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      Griffiths phase on hierarchical modular networks with small-world edges

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      Physical Review. E
      American Physical Society

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          Abstract

          The Griffiths phase has been proposed to induce a stretched critical regime that facilitates self-organizing of brain networks for optimal function. This phase stems from the intrinsic structural heterogeneity of brain networks, i.e., the hierarchical modular structure. In this work, the Griffiths phase is studied in modified hierarchical networks with small-world connections based on the 3-regular Hanoi network. Through extensive simulations, the hierarchical level-dependent inter-module wiring probabilities are identified to determine the emergence of the Griffiths phase. Numerical results and the complementary spectral analysis of the relevant networks can be helpful for a deeper understanding of the essential structural characteristics of finite-dimensional networks to support the Griffiths phase.

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          Most cited references35

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          Nonanalytic Behavior Above the Critical Point in a Random Ising Ferromagnet

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            Synchronization in small-world systems

            , (2001)
            We quantify the dynamical implications of the small-world phenomenon. We consider the generic synchronization of oscillator networks of arbitrary topology, and link the linear stability of the synchronous state to an algebraic condition of the Laplacian of the graph. We show numerically that the addition of random shortcuts produces improved network synchronizability. Further, we use a perturbation analysis to place the synchronization threshold in relation to the boundaries of the small-world region. Our results also show that small-worlds synchronize as efficiently as random graphs and hypercubes, and more so than standard constructive graphs.
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              The criticality hypothesis: how local cortical networks might optimize information processing.

              John Beggs (2008)
              Early theoretical and simulation work independently undertaken by Packard, Langton and Kauffman suggested that adaptability and computational power would be optimized in systems at the 'edge of chaos', at a critical point in a phase transition between total randomness and boring order. This provocative hypothesis has received much attention, but biological experiments supporting it have been relatively few. Here, we review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point. Simulation studies capture the main features of these data and suggest that criticality may allow cortical networks to optimize information processing. These simulations lead to predictions that could be tested in the near future, possibly providing further experimental evidence for the criticality hypothesis.
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                Author and article information

                Journal
                Phys Rev E
                Phys Rev E
                PRE
                PLEEE8
                Physical Review. E
                American Physical Society
                2470-0045
                2470-0053
                March 2017
                6 March 2017
                6 March 2017
                : 95
                : 3
                : 032306
                Affiliations
                Department of Physics, Emory University, Atlanta , Georgia 30322, USA
                Author notes
                [*]

                shanshan.li@emory.edu

                Article
                10.1103/PhysRevE.95.032306
                7217519
                28415342
                3d073e8f-c882-4c04-9d73-d41e8f6a3637
                ©2017 American Physical Society

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 26 August 2016
                : 30 December 2016
                Page count
                Pages: 9
                Funding
                Funded by: National Science Foundation http://dx.doi.org/10.13039/100000001 NSF http://sws.geonames.org/6252001/ http://sws.geonames.org/6254928/
                Award ID: DMR-1207431
                Categories
                Articles
                Networks and Complex Systems

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