@article{ZemanovaZhouKurths2006,
  author    = {Zemanova, Lucia and Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Structural and functional clusters of complex brain networks},
  series = {Physica, D, Nonlinear phenomena},
  volume    = {224},
  journal   = {Physica, D, Nonlinear phenomena},
  number    = {1-2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-2789},
  doi       = {10.1016/j.physd.2006.09.008},
  pages     = {202 -- 212},
  year      = {2006},
  abstract  = {Recent research using the complex network approach has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. It is of importance to understand the implications of such complex network structures in the functional organization of the brain activities. Here we study this problem from the viewpoint of dynamical complex networks. We investigate synchronization dynamics on the corticocortical network of the cat by modeling each node (cortical area) of the network with a sub-network of interacting excitable neurons. We find that the network displays clustered synchronization behavior, and the dynamical clusters coincide with the topological community structures observed in the anatomical network. Our results provide insights into the relationship between the global organization and the functional specialization of the brain cortex.},
  language  = {en}
}
@article{ZhouZemanovaZamoraetal.2006,
  author    = {Zhou, Changsong and Zemanova, Lucia and Zamora, Gorka and Hilgetag, Claus C. and Kurths, J{\"u}rgen},
  title     = {Hierarchical organization unveiled by functional connectivity in complex brain networks},
  series = {Physical review letters},
  volume    = {97},
  journal   = {Physical review letters},
  publisher = {American Physical Society},
  address   = {College Park},
  issn      = {0031-9007},
  doi       = {10.1103/PhysRevLett.97.238103},
  pages     = {4},
  year      = {2006},
  abstract  = {How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks.},
  language  = {en}
}
@article{ZhouKurths2006,
  author    = {Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Hierarchical synchronization in complex networks with heterogeneous degrees},
  issn      = {1054-1500},
  doi       = {10.1063/1.2150381},
  year      = {2006},
  abstract  = {We study synchronization behavior in networks of coupled chaotic oscillators with heterogeneous connection degrees. Our focus is on regimes away from the complete synchronization state, when the coupling is not strong enough, when the oscillators are under the influence of noise or when the oscillators are nonidentical. We have found a hierarchical organization of the synchronization behavior with respect to the collective dynamics of the network. Oscillators with more connections (hubs) are synchronized more closely by the collective dynamics and constitute the dynamical core of the network. The numerical observation of this hierarchical synchronization is supported with an analysis based on a mean field approximation and the master stability function. (C) 2006 American Institute of Physics},
  language  = {en}
}
@article{ZhouMotterKurths2006,
  author    = {Zhou, Changsong and Motter, Adilson E. and Kurths, J{\"u}rgen},
  title     = {Universality in the synchronization of weighted random networks},
  doi       = {10.1103/Physrevlett.96.034101},
  year      = {2006},
  abstract  = {Realistic networks display not only a complex topological structure, but also a heterogeneous distribution of weights in the connection strengths. Here we study synchronization in weighted complex networks and show that the synchronizability of random networks with a large minimum degree is determined by two leading parameters: the mean degree and the heterogeneity of the distribution of node's intensity, where the intensity of a node, defined as the total strength of input connections, is a natural combination of topology and weights. Our results provide a possibility for the control of synchronization in complex networks by the manipulation of a few parameters},
  language  = {en}
}
@article{ZhouKurths2006,
  author    = {Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Dynamical weights and enhanced synchronization in adaptive complex networks},
  doi       = {10.1103/Physrevlett.96.164102},
  year      = {2006},
  abstract  = {Dynamical organization of connection weights is studied in scale-free networks of chaotic oscillators, where the coupling strength of a node from its neighbors develops adaptively according to the local synchronization property between the node and its neighbors. We find that when complete synchronization is achieved, the coupling strength becomes weighted and correlated with the topology due to a hierarchical transition to synchronization in heterogeneous networks. Importantly, such an adaptive process enhances significantly the synchronizability of the networks, which could have meaningful implications in the manipulation of dynamical networks},
  language  = {en}
}
@article{ZamoraLopezZhouKurths2009,
  author    = {Zamora-Lopez, Gorka and Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Graph analysis of cortical networks reveals complex anatomical communication substrate},
  issn      = {1054-1500},
  doi       = {10.1063/1.3089559},
  year      = {2009},
  abstract  = {Sensory information entering the nervous system follows independent paths of processing such that specific features are individually detected. However, sensory perception, awareness, and cognition emerge from the combination of information. Here we have analyzed the corticocortical network of the cat, looking for the anatomical substrate which permits the simultaneous segregation and integration of information in the brain. We find that cortical communications are mainly governed by three topological factors of the underlying network: (i) a large density of connections, (ii) segregation of cortical areas into clusters, and (iii) the presence of highly connected hubs aiding the multisensory processing and integration. Statistical analysis of the shortest paths reveals that, while information is highly accessible to all cortical areas, the complexity of cortical information processing may arise from the rich and intricate alternative paths in which areas can influence each other.},
  language  = {en}
}
@article{WuZhouXiaoetal.2010,
  author    = {Wu, Ye Wu and Zhou, Changsong and Xiao, Jinghua and Kurths, J{\"u}rgen and Schellnhuber, Hans Joachim},
  title     = {Evidence for a bimodal distribution in human communication},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.1013140107},
  year      = {2010},
  abstract  = {Interacting human activities underlie the patterns of many social, technological, and economic phenomena. Here we present clear empirical evidence from Short Message correspondence that observed human actions are the result of the interplay of three basic ingredients: Poisson initiation of tasks and decision making for task execution in individual humans as well as interaction among individuals. This interplay leads to new types of interevent time distribution, neither completely Poisson nor power-law, but a bimodal combination of them. We show that the events can be separated into independent bursts which are generated by frequent mutual interactions in short times following random initiations of communications in longer times by the individuals. We introduce a minimal model of two interacting priority queues incorporating the three basic ingredients which fits well the distributions using the parameters extracted from the empirical data. The model can also embrace a range of realistic social interacting systems such as e-mail and letter communications when taking the time scale of processing into account. Our findings provide insight into various human activities both at the individual and network level. Our analysis and modeling of bimodal activity in human communication from the viewpoint of the interplay between processes of different time scales is likely to shed light on bimodal phenomena in other complex systems, such as interevent times in earthquakes, rainfall, forest fire, and economic systems, etc.},
  language  = {en}
}
@article{BaptistaZhouKurths2006,
  author    = {Baptista, Murilo da Silva and Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Information transmission in phase synchronous chaotic arrays},
  issn      = {0256-307X},
  doi       = {10.1088/0256-307X/23/3/010},
  year      = {2006},
  abstract  = {We show many versatile phase synchronous configurations that emerge in an array of coupled chaotic elements due to the presence of a periodic stimulus. Then, we explain the relevance of these configurations to the understanding of how information about such a. stimulus is transmitted from one side to the other in this array. The stimulus actively creates the ways to be transmitted, by making the chaotic elements to phase synchronize},
  language  = {en}
}
@article{ChenShangZhouetal.2009,
  author    = {Chen, Maoyin and Shang, Yun and Zhou, Changsong and Wu, Ye and Kurths, J{\"u}rgen},
  title     = {Enhanced synchronizability in scale-free networks},
  issn      = {1054-1500},
  doi       = {10.1063/1.3062864},
  year      = {2009},
  abstract  = {We introduce a modified dynamical optimization coupling scheme to enhance the synchronizability in the scale- free networks as well as to keep uniform and converging intensities during the transition to synchronization. Further, the size of networks that can be synchronizable exceeds by several orders of magnitude the size of unweighted networks.},
  language  = {en}
}
@article{ZhouKurthsHu2001,
  author    = {Zhou, Changsong and Kurths, J{\"u}rgen and Hu, B.},
  title     = {Array-enhanced coherence resonance: Nontrivial effects of heterogeneity and spatial independence of noise},
  year      = {2001},
  language  = {en}
}