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Search Results: 1 - 10 of 158042 matches for " Wang Sheng-Jun "
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Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations
Sheng-Jun Wang,Claus C. Hilgetag,Changsong Zhou
Frontiers in Computational Neuroscience , 2011, DOI: 10.3389/fncom.2011.00030
Abstract: Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing.
Projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks
Cun-Fang Feng,Xin-Jian Xu,Sheng-Jun Wang,Ying-Hai Wang
Physics , 2008, DOI: 10.1063/1.2912720
Abstract: We study projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks. We relax some limitations of previous work, where projective-anticipating and projective-lag synchronization can be achieved only on two coupled chaotic systems. In this paper, we can realize projective-anticipating and projective-lag synchronization on complex dynamical networks composed by a large number of interconnected components. At the same time, although previous work studied projective synchronization on complex dynamical networks, the dynamics of the nodes are coupled partially linear chaotic systems. In this paper, the dynamics of the nodes of the complex networks are time-delayed chaotic systems without the limitation of the partial-linearity. Based on the Lyapunov stability theory, we suggest a generic method to achieve the projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random dynamical networks and find both the existence and sufficient stability conditions. The validity of the proposed method is demonstrated and verified by examining specific examples using Ikeda and Mackey-Glass systems on Erdos-Renyi networks.
Effects of degree distribution in mutual synchronization of neural networks
Sheng-Jun Wang,Xin-Jian Xu,Zhi-Xi Wu,Ying-Hai Wang
Physics , 2007, DOI: 10.1103/PhysRevE.74.041915
Abstract: We study the effects of the degree distribution in mutual synchronization of two-layer neural networks. We carry out three coupling strategies: large-large coupling, random coupling, and small-small coupling. By computer simulations and analytical methods, we find that couplings between nodes with large degree play an important role in the synchronization. For large-large coupling, less couplings are needed for inducing synchronization for both random and scale-free networks. For random coupling, cutting couplings between nodes with large degree is very efficient for preventing neural systems from synchronization, especially when subnetworks are scale-free.
Promote cooperation by localised small-world communication
Zi-Gang Huang,Sheng-Jun Wang,Xin-Jian Xu,Ying-Hai Wang
Physics , 2007, DOI: 10.1209/0295-5075/81/28001
Abstract: The emergence and maintenance of cooperation within sizable groups of unrelated humans offer many challenges for our understanding. We propose that the humans' capacity of communication, such as how many and how far away the fellows can build up mutual communications, may affect the evolution of cooperation. We study this issue by means of the public goods game (PGG) with a two-layered network of contacts. Players obtain payoffs from five-person public goods interactions on a square lattice (the interaction layer). Also, they update strategies after communicating with neighbours in learning layer, where two players build up mutual communication with a power law probability depending on their spatial distance. Our simulation results indicate that the evolution of cooperation is indeed sensitive to how players choose others to communicate with, including the amount as well as the locations. The tendency of localised communication is proved to be a new mechanism to promote cooperation.
Waveform sample method of excitable sensory neuron
Sheng-Jun Wang,Xin-Jian Xu,Ying-Hai Wang
Quantitative Biology , 2006,
Abstract: We present a new interpretation for encoding information of the period of input signals into spike-trains in individual sensory neuronal systems. The spike-train could be described as the waveform sample of the input signal which locks sample points to wave crests with randomness. Based on simulations of the Hodgkin-Huxley (HH) neuron responding to periodic inputs, we demonstrate that the random sampling is a proper encoding method in medium frequency region since power spectra of the reconstructed spike-trains are identical to that of neural signals.
Noise transmission and delay-induced stochastic oscillations in biochemical network motifs

Liu Sheng-Jun,Wang Qi,Liu Bo,Yan Shi-Wei,Fumihiko Sakata,

中国物理 B , 2011,
Abstract:
An Algorithmic Adaptive Enhancement Algorithm of Form Image Based on Image Information Measure
一种基于图像方向信息测度算法的自适应表格图像增强算法

WANG Sheng-jun,LIANG De-qun,
王胜军
,梁德群

中国图象图形学报 , 2006,
Abstract: Image enhancement is an important task of image processing. We propose an algorithmic adaptive image enhancement method based on image information measure in this paper. At the image pixel level, image pixels are classified into smooth pixels and edge pixels by using image information measure, then the smooth pixels are farther classified into smooth pixels and ridge pixels aiming at the feature of form image. Relevant filtering algorithms are designed for different types of pixels, which realizes algorithmic adaption and enhance image primarily. Finally, the multi_level fuzzy enhancement algorithm is imposed to enhance image contrast and achieve a better enhancement effect. Experimental results show that the algorithm can enhance the low contrast image effectively and obtain a very good visual effect.
An efficient quantum light-matter interface with sub-second lifetime
Sheng-Jun Yang,Xu-Jie Wang,Xiao-Hui Bao,Jian-Wei Pan
Physics , 2015,
Abstract: Quantum repeater holds the promise for scalable long-distance quantum communication. Towards a first quantum repeater based on memory-photon entanglement, significant progresses have made in improving performances of the building blocks. Further development is hindered by the difficulty of integrating key capabilities such as long storage time and high memory efficiency into a single system. Here we report an efficient light-matter interface with sub-second lifetime by confining laser-cooled atoms with 3D optical lattice and enhancing the atom-photon coupling with a ring cavity. An initial retrieval efficiency of 76(5)% together with an 1/e lifetime of 0.22(1) s have been achieved simultaneously, which already support sub-Hz entanglement distribution up to 1000 km through quantum repeater. Together with an efficient telecom interface and moderate multiplexing, our result may enable a first quantum repeater system that beats direct transmission in the near future.
Evolutionary prisoner's dilemma game with dynamic preferential selection
Zhi-Xi Wu,Xin-Jian Xu,Zi-Gang Huang,Sheng-Jun Wang,Ying-Hai Wang
Physics , 2005, DOI: 10.1103/PhysRevE.74.021107
Abstract: We study a modified prisoner's dilemma game taking place on two-dimensional disordered square lattices. The players are pure strategists and can either cooperate or defect with their immediate neighbors. In the generations each player update its strategy by following one of the neighboring strategies with a probability dependent on the payoff difference. The neighbor selection obeys a dynamic preferential rule, i.e., the more frequently a neighbor's strategy was adopted by the focal player in the previous rounds, the larger probability it will be chosen to refer to in the subsequent rounds. It is found that cooperation is substantially promoted due to this simple selection mechanism. Corresponding analysis is provided by the investigations of the distribution of players' impact weights, persistence, and as well as correlation function.
Response of degree-correlated scale-free networks to stimuli
Sheng-Jun Wang,An-Cai Wu,Zhi-Xi Wu,Xin-Jian Xu,Ying-Hai Wang
Physics , 2007, DOI: 10.1103/PhysRevE.75.046113
Abstract: The response of degree-correlated scale-free attractor networks to stimuli is studied. We show that degree-correlated scale-free networks are robust to random stimuli as well as the uncorrelated scale-free networks, while assortative (disassortative) scale-free networks are more (less) sensitive to directed stimuli than uncorrelated networks. We find that the degree-correlation of scale-free networks makes the dynamics of attractor systems different from uncorrelated ones. The dynamics of correlated scale-free attractor networks result in the effects of degree correlation on the response to stimuli.
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