Synchronization of Phase Oscillators in Networks with Certain Frequency Sequence

Synchronization of Kuramoto phase oscillators arranged in real complex neural networks is investigated. It is shown that the synchronization greatly depends on the sets of natural frequencies of the involved oscillators. The influence of network connectivity heterogeneity on synchronization depends particularly on the correlation between natural frequencies and node degrees. This finding implies a potential application that inhibiting the effects caused by the changes of network structure can be bManced out nicely by choosing the correlation parameter appropriately.     

Synchronization of Kuramoto model in a high-dimensional linear space

In this Letter, Kuramoto model in a high-dimensional linear space is investigated. Some results on the equilibria and synchronization of the classical Kuramoto model are generalized to the high-dimensional Kuramoto model. It is proved that, if the interconnection graph is connected and all the initial states lie in a half part of the state space, the synchronization can be achieved. Finally, numerical simulations are given to validate the obtained theoretical results.     

Effect of Topology Structures on Synchronization Transition in Coupled Neuron Cells System

In this paper,by the help of evolutionary algorithm and using Hindmarsh–Rose(HR)neuron model,we investigate the efect of topology structures on synchronization transition between diferent states in coupled neuron cells system.First,we build diferent coupling structure with N cells,and found the efect of synchronized transition contact not only closely with the topology of the system,but also with whether there exist the ring structures in the system.In particular,both the size and the number of rings have greater efects on such transition behavior.Secondly,we introduce synchronization error to qualitative analyze the efect of the topology structure.Furthermore,by fitting the simulation results,we find that with the increment of the neurons number,there always exist the optimization structures which have the minimum number of connecting edges in the coupling systems.Above results show that the topology structures have a very crucial role on synchronization transition in coupled neuron system.Biological system may gradually acquire such efcient topology structures through the long-term evolution,thus the systems’information process may be optimized by this scheme.     

Effects of Coupling Distance on Synchronization and Coherence in Chaotic Neural Networks

Effects of coupling distance on synchronization and coherence of chaotic neurons in complex networks are numerically investigated. We find that it is not beneficial to neurons synchronization if confining the coupling distance of random edges to a limit dmax, but help to improve their coherence. Moreover, there is an optimal value of dmax at which the coherence is maximum.     

The Influence of Network Properties on the Synchronization of Kuramoto Oscillators Quantified by a Bayesian Regression Analysis

The influence of the network structure on the emergence of collective dynamical behavior is an important topic of research that has not been fully understood yet. In the current work, it is shown how statistical regression analysis can be considered to address this issue. The regression model proposed suggests that the average shortest path length is the network property most influencing the degree of synchronization of Kuramoto oscillators. Moreover, this model revealed to be very accurate, being the predicted and measured values of synchronization highly correlated. Therefore, the regression modeling allows predicting the values of the dynamic variable in terms of network structure.     

Clustering and Synchronization in an Array of Repulsively Coupled Phase Oscillators

Clustering and synchronization in an array of repulsively coupled phase oscillators are numerically investigated. It is found that oscillators are divided into several clusters according to the symmetry in the structure.Synchronization occurs between oscillators in each cluster, while those oscillators belonging to different clusters remain asynchronous. Such synchronization may collapse for all clusters when the dynamics of only one oscillator is altered properly. The synchronous state may return back after a short period of transient process. This is determined by the strength of the oscillator altered. Its application in the communication of one-to-several is suggested.     

Dynamical Phase Transition in a Neural Network Model with Noise: An Exact Solution

The dynamical organization in the presence of noise of a Boolean neural network with random connections is analyzed. For low levels of noise, the system reaches a stationary state in which the majority of its elements acquire the same value. It is shown that, under very general conditions, there exists a critical value _c of the noise, below which the network remains organized and above which it behaves randomly. The existence and nature of the phase transition are computed analytically, showing that the critical exponent is 1/2. The dependence of _c on the parameters of the network is obtained. These results are then compared with two numerical realizations of the network.     

Effects of Spike Frequency Adaptation on Synchronization Transitions in Electrically Coupled Neuronal Networks with Scale-Free Connectivity

Effects of spike frequency adaptation （SFA ） on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode： compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong- degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations.     

Effects of Reduced Frequency on Network Configuration and Synchronization Transition

Synchronization of networked phase oscillators depends essentially on the correlation between the topological structure of the graph and the dynamical property of the elements.We propose the concept of 'reduced frequency',a measure which can quantify natural frequencies of each pair of oscillators.Then we introduce an evolving network whose linking rules are controlled by its own dynamical property.The simulation results indicate that when the linking probability positively correlates with the reduced frequency,the network undergoes a first-order phase transition.Meanwhile,we discuss the circumstance under which an explosive synchronization can be ignited.The numerical results show that the peculiar butterfly shape correlation between frequencies and degrees of the nodes contributes to an explosive synchronization transition.     

Self-Organized Criticality in a Simple Neuron Model Based on Scale-Free Networks

A simple model for a set of interacting idealized neurons in scale-free networks is introduced. The basic elements of the model are endowed with the main features of a neuron function. We find that our model displays powerlaw behavior of avalanche sizes and generates long-range temporal correlation. More importantly, we find different dynamical behavior for nodes with different connectivity in the scale-free networks.     

Drift Intermittent Synchronization and Controllability in a Simple Model

A simple model of three coupled oscillators as an approximation of main modes behaviors in a spatial extended system is proposed. Multi-looping generalized synchronization and drift intermittent lag phase synchronization phenomena are found in this simple model. For a certain of parameters in which chaotic-like intermittent behavior exhibit the amplitudes and phases of three modes are controlled to be synchronized states via coupling them with an external periodic mode.     

F－L模型的状态方程与退禁闭相变图象

用温度场论方法计算了Ｆ－Ｌ模型在有限温度和密度下的状态方程，分析了压强对净重子数密度的等温线．结果表明，在平均场近似下，Ｆ－Ｌ模型所给出的退禁闭相变为一级相变．     

非线性耦合超混沌R(o)ssler系统和网络的同步

研究两个通过非线性函数对称耦合的超混沌Roessler系统的同步问题．通过对超混沌系统的线性项与非线性项的适当分离,构造一个特殊的非线性函数,作为耦合函数,发现在耦合强度α=0．5附近的一小段区域里存在稳定的超混沌同步现象．利用线性系统的稳定性分析准则和条件Lyapunov指数来检验同步状态的稳定性,并进一步研究了由多个超混沌Roessler系统单元通过非线性函数按照完全连接形式组成的网络的混沌同步问题。显示许多耦合单元组成的网络,满足同步稳定性的耦合强度的取值范围可以仅从2个单元组成的网络的参数取值范围估计到。此外发现耦合强度的值与耦合单元数量成反比,数值模拟结果证实所提出方法对超混沌系统和网络的混沌同步是有效的。     

Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

In the presence of Gaussian white noise, we study the properties of voltage-controlled oscillator neuron model and discuss the effects of the additive and multiplicative noise. It is found that the additive noise can accelerate and counterwork the firing of neuron, which depends on the value of central frequency of neuron itself, while multiplicative noise can induce the continuous change or mutation of membrane potential.     

Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

Small world effects in the harmonious unifying hybrid preferential model (HUHPM) networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE model, so-called HUHPM-BA, HUHPM-BBV and HUHPM-TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-weighted models but also for the weighted models.     

On the Dynamics of the Glass Transition on Bethe Lattices

The Glauber dynamics of disordered spin models with multi-spin interactions on sparse random graphs (Bethe lattices) is investigated. Such models undergo a dynamical glass transition upon decreasing the temperature or increasing the degree of constrainedness. Our analysis is based upon a detailed study of large scale rearrangements which control the slow dynamics of the system close to the dynamical transition. Particular attention is devoted to the neighborhood of a zero temperature tricritical point. Both the approach and several key results are conjectured to be valid in a considerably more general context. PACS Numbers:75.50.Lk (Spin glasses), 64.70.Pf (Glass transitions), 89.20.Ff (Computer science     

Hofstadter＇s Butterfly and Phase Transition of Checkerboard Superconducting Network in a Magnetic Field

We study the magnetic effect of the checkerboard superconducting wire network. Based on the de Gennes- Alexader theory, we obtain difference equations for superconducting order parameter in the wire network. Through solving these difference equations, we obtain the eigenvalues, linked to the coherence length, as a function of magnetic field. The diagram of eigenvalues shows a fractal structure, being so-called Hofstadter＇s butterfly. We also calculate and discuss the dependence of the transition temperature of the checkerboard superconducting wire network on the applied magnetic field, which is related to up-edge of the Hofstadter＇s butterfly spectrum.     

链长对苯乙烯基吡啶类液晶化合物的分子结构及相变行为的影响

在氢键诱导液晶化合物中,针对做为质子给体的苯乙烯基吡啶类液晶化合物,通过红外和拉曼光谱研究了链长对分子结构和相变行为的影响。末端链长的改变直接影响到相变次序和相转变温度,同时影响整个分子的排列方式。     

Quantum Effects in a Lattice Model of Anharmonic Vector Oscillators

A model of d-dimensional quantum anharmonic oscillators living on ^v , with a polynomial anharmonicity and a ferroelectric pair interaction is considered. For all v, d , including the cases where such models undergo a structural phase transition, it is proved that the fluctuations of displacements of particles remain normal at all temperatures if the energy of zero-point oscillations of a given particle exceeds a certain value proportional to the energy of its interaction with the rest of oscillators. In particular, this occurs when the smallest distance between the energy levels of the corresponding one-dimensional isolated oscillator is large enough or its reduced mass is small enough. Therefore, in such systems strong zero-point oscillations may suppress abnormal fluctuations of any kind at all temperatures.     

Research on Community Structure in Bus Transport Networks

We abstract the bus transport networks （BTNs） to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm （PKM agglomerative algorithm）, we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs＇, we get obvious community property.