Literature DB >> 12906539

Heterogeneity in oscillator networks: are smaller worlds easier to synchronize?

Takashi Nishikawa1, Adilson E Motter, Ying-Cheng Lai, Frank C Hoppensteadt.   

Abstract

Small-world and scale-free networks are known to be more easily synchronized than regular lattices, which is usually attributed to the smaller network distance between oscillators. Surprisingly, we find that networks with a homogeneous distribution of connectivity are more synchronizable than heterogeneous ones, even though the average network distance is larger. We present numerical computations and analytical estimates on synchronizability of the network in terms of its heterogeneity parameters. Our results suggest that some degree of homogeneity is expected in naturally evolved structures, such as neural networks, where synchronizability is desirable.

Year:  2003        PMID: 12906539     DOI: 10.1103/PhysRevLett.91.014101

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  46 in total

1.  Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions.

Authors:  Takashi Nishikawa; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2010-05-20       Impact factor: 11.205

2.  Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

Authors:  Michael J Leone; Brandon N Schurter; Benjamin Letson; Victoria Booth; Michal Zochowski; Christian G Fink
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-03-30

3.  Synchronous neural activity in scale-free network models versus random network models.

Authors:  Geoffrey Grinstein; Ralph Linsker
Journal:  Proc Natl Acad Sci U S A       Date:  2005-07-05       Impact factor: 11.205

4.  Dynamics and effective topology underlying synchronization in networks of cortical neurons.

Authors:  Danny Eytan; Shimon Marom
Journal:  J Neurosci       Date:  2006-08-16       Impact factor: 6.167

5.  Sick and edgy: walk-counting as a metric of epidemic spreading on networks.

Authors:  Dennis C Wylie; Wayne M Getz
Journal:  J R Soc Interface       Date:  2008-12-16       Impact factor: 4.118

6.  Controllability of complex networks.

Authors:  Yang-Yu Liu; Jean-Jacques Slotine; Albert-László Barabási
Journal:  Nature       Date:  2011-05-12       Impact factor: 49.962

7.  Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity.

Authors:  Sang-Yoon Kim; Woochang Lim
Journal:  Cogn Neurodyn       Date:  2018-09-11       Impact factor: 5.082

8.  Fundamental limitations of network reconstruction from temporal data.

Authors:  Marco Tulio Angulo; Jaime A Moreno; Gabor Lippner; Albert-László Barabási; Yang-Yu Liu
Journal:  J R Soc Interface       Date:  2017-02       Impact factor: 4.118

Review 9.  The structure and dynamics of multilayer networks.

Authors:  S Boccaletti; G Bianconi; R Criado; C I Del Genio; J Gómez-Gardeñes; M Romance; I Sendiña-Nadal; Z Wang; M Zanin
Journal:  Phys Rep       Date:  2014-07-10       Impact factor: 25.600

10.  Data assimilation for heterogeneous networks: the consensus set.

Authors:  Timothy D Sauer; Steven J Schiff
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-05-13
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