Showing papers by "Changsong Zhou published in 2009"

Graph analysis of cortical networks reveals complex anatomical communication substrate.

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.

Effects of social diversity on the emergence of global consensus in opinion dynamics.

Abstract: We propose a variant of the voter model by introducing the social diversity in the evolution process. Each individual is assigned a weight that is proportional to the power of its degree, where the ...

Enhanced synchronizability in scale-free networks.

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.

Synchronization on Effective Networks

Abstract: The study of network synchronization has attracted increasing attention recently. In this paper, we strictly define a class of networks, namely effective networks, which are synchronizable and orientable networks. We can prove that all the effective networks with the same size have the same spectra, and are of the best synchronizability according to the master stability analysis. However, it is found that the synchronization time for different effective networks can be quite different. Further analysis show that the key ingredient affecting the synchronization time is the maximal depth of an effective network: the larger depth results in a longer synchronization time. The secondary factor is the number of links. The more links connecting the nodes in the same layer (horizontal links) will lead to longer synchronization time, while the increasing number of links connecting nodes in neighboring layers (vertical links) will accelerate the synchronization. Our findings provide insights into the roles of horizontal and vertical links in synchronizing process, and suggest that the spectral analysis is helpful yet insufficient for the understanding of network synchronization.

Dynamics in complex systems

Abstract: Over the last decade, we have witnessed the birth of a new movement of interest and research in the study of complex networks. These networks often have irregular structural properties, but also encompass rich dynamics. The interplay between the network topological structure and the associated dynamics attracts a lot of interest. In this research line, we propose a network approach to dealing with complex dynamics, in particular with synchronization dynamics. From the methodological perspective, this approach requires novel ideas from nonlinear sciences, statistical physics and mathematical statistics. Furthermore, we show applications in different disciplines, from earth sciences to brain dynamics. The complex network's approach is an interdisciplinary topic and could be promising for the understanding of complexity from a systems level.

Information encoding in an oscillatory network.

Abstract: Information encoding in a globally coupled network is studied. When the network is in an oscillatory state, the network activities are dominated by the intrinsic oscillatory current and the stimulus is poorly encoded. However, when the amplitude of the input signal is large, the input can still be well read from the population rate and the temporal correlation between spike trains. The underlying reason is that there exists a competition between the intrinsic correlation caused by the oscillatory current and the external correlation caused by the input signal. With small input signal, the rate code performs better than the temporal correlation code. Our results provide insights into the effects of network dynamics on neuronal computations.

Scaling in the global spreading patterns of pandemic Influenza A (H1N1) and the role of control: empirical statistics and modeling

Abstract: Background: The pandemic of influenza A (H1N1) is a serious on-going global public crisis. Understanding its spreading dynamics is of fundamental importance for both public health and scientific researches. Recent studies have focused mainly on evaluation and prediction of on-going spreading, which strongly depends on detailed information about the structure of social contacts, human traveling patterns and biological activity of virus, etc. Methodology/Principal Findings: In this work we analyzed the distributions of confirmed cases of influenza A (H1N1) in different levels and find the Zipf's law and Heaps' law. Similar scaling properties were also observed for severe acute respiratory syndrome (SARS) and bird cases of H5N1. We also found a hierarchical spreading pattern from countries with larger population and GDP to countries with smaller ones. We proposed a model that considers generic control effects on both the local growth and transregional transmission, without the need of the above mentioned detailed information. We studied in detail the impact of control effects and heterogeneity on the spreading dynamics in the model and showed that they are responsible for the scaling and hierarchical spreading properties observed in empirical data. Conclusions/Significance: Our analysis and modeling showed that although strict control measures for interregional travelers could delay the outbreak in the regions without local cases, the focus should be turned to local prevention after the outbreak of local cases. Target control on a few regions with the largest number of active interregional travelers can efficiently prevent the spreading. This work provided not only a deeper understanding of the generic mechanisms underlying the spread of infectious diseases, but also some practical guidelines for decision makers to adopt suitable control strategies.

Effective leadership in competition

Abstract: Among natural biological flocks/swarms or even mass social activities, when the collective behaviors of the followers has been dominated by the moving direction or opinion of one leader group, it seems very difficult for later-coming leaders to reverse the orientation of the mass followers, especially when they are in quantitative minority. This Letter reports a counter-intuitive phenomenon, Following the Later-coming Minority, provided that the late-comers obey a favorable distribution pattern which enables them to spread their influence to as many followers as possible in a given time and to accumulate enough power to govern these followers. We introduce a discriminant index to quantify the whole group's orientation under competing leadership, which helps to design an economic way for the minority later-coming leaders to defeat the dominating majority leaders solely by optimizing their distribution pattern. Our investigation provides new insights into the effective leadership in biological systems, with meaningful implication to social and industrial applications.