Complete and phase synchronization in a heterogeneous small-world neuronal network

Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh--Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony.     

空间关联白噪声影响下小世界神经元网络系统的同步动力学

以电耦合的Terman-Wang小世界神经元网络系统为研究对象, 研究了空间关联白噪声影响下神经元网络系统的同步动力学. 首先将动力学平均场近似理论扩展到受空间关联白噪声影响下的小世界网络系统中, 将描述网络系统动力学演化的2N维随机微分方程简化为11个确定性的矩微分方程. 其次, 基于动力学平均场近似理论所推导的矩方程, 讨论了空间关联噪声、网络结构参数对神经元网络系统同步动力学的关键影响, 发现较大的噪声空间关联系数、耦合强度及节点平均度均对神经元网络系统同步放电具有积极作用. 进一步地, 利用计算机仿真数值模拟原神经元网络系统的同步动力学, 并与基于动力学平均场近似理论所得到的结果进行比较, 发现二者具有较好的一致性.     

小世界网络与无标度网络的社区结构研究

模块性(modularity)是度量网络社区结构(community structure)的主要参数.探讨了Watts和Strogatz的小世界网络(简称W-S模型)以及Barabàsi 等的B-A无标度网络(简称B-A模型)两类典型复杂网络模块性特点.结果显示，网络模块性受到网络连接稀疏的影响，W-S模型具有显著的社区结构，而B-A模型的社区结构特征不明显.因此，应用中应该分别讨论网络的小世界现象和无标度特性.社区结构不同于小世界现象和无标度特性，并可以利用模块性区别网络类型，因此网络复杂性指标应该包括 关键词： 模块性 社区结构 小世界网络 无标度网络     

Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses

We study the dependence of synchronization transitions in small-world networks of bursting neurons with hybrid electrical–chemical synapses on the information transmission delay, the probability of electrical synapses, and the rewiring probability. It is shown that, irrespective of the probability of electrical synapses, the information transmission delay can always induce synchronization transitions in small-world neuronal networks, i.e., regions of synchronization and nonsynchronization appear intermittently as the delay increases. In particular, all these transitions to burst synchronization occur approximately at integer multiples of the bursting period of individual neurons. In addition, for larger probability of electrical synapses, the intermittent synchronization transition is more profound, due to the stronger synchronization ability of electrical synapses compared with chemical ones. More importantly, chemical and electrical synapses can perform complementary roles in the synchronization of hybrid small-world neuronal networks: the larger the electrical synapse strength is, the smaller the chemical synapse strength needed to achieve burst synchronization. Furthermore, the small-world topology has a significant effect on the synchronization transition in hybrid neuronal networks. It is found that increasing the rewiring probability can always enhance the synchronization of neuronal activity. The results obtained are instructive for understanding the synchronous behavior of neural systems.     

An effective method to improve the robustness of small-world networks under attack

In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network.Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario.     

States and transitions in mixed networks

A network is named as mixed network if it is composed of N nodes, the dynamics of some nodes are periodic, while the others are chaotic. The mixed network with all-to-all coupling and its correspond- ing networks after the nonlinearity gap-condition pruning are investigated. Several synchronization states are demonstrated in both systems, and a first-order phase transition is proposed. The mixture of dynamics implies any kind of synchronous dynamics for the whole network, and the inixed networks may be controlled by the nonlinearity gap-condition pruning.     

Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution(W SD) have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order(i.e., the number of nodes) and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.     

随机中毒对神经元网络时空动力学行为的影响

神经元细胞膜上的离子通道能够被一些有毒的化学物质阻断. 离子通道阻断会降低离子通道的电导率和激活通道数, 影响神经元的放电活动, 进而影响神经网络时空模式的动力学行为. 本文采用具有周期边界的近邻耦合Hodgkin-Huxley神经元网络, 数值研究了钠离子和钾离子通道随机中毒时神经网络时空模式的演化过程. 发现钠离子和钾离子通道随机中毒可以导致螺旋波破裂. 通过分析网络的放电概率, 发现钠离子通道随机中毒降低了神经网络的兴奋性, 且其对中毒的敏感程度与噪声强度有关; 钾离子通道随机中毒增强了神经网络的兴奋性. 与均匀的通道中毒相比, 随机通道中毒的神经网络具有更丰富的动力学行为. 最后, 采用无流边界条件对神经网络进行数值仿真, 得到了类似的结果. 该研究更真实地反映神经系统中毒时整体兴奋性的变化, 从另一个方面揭示离子通道中毒对网络时空行为的影响, 有利于更进一步理解离子通道在网络整体行为中的作用. 关键词： 神经网络 离子通道 随机中毒 时空动力学     

小世界神经网络的二次超谐波随机共振

随机共振现象是非线性系统中普遍存在的自然现象.主要通过研究小世界生物神经网络中的输出信噪比与反映小世界效应的重连概率p、耦合强度c以及输入信号振幅A之间的关系，来揭示小世界生物神经网络的二次超谐波随机共振的一些规律.发现对于Hodgkin-Huxley小世界神经网络，并不是信号越强，信噪比越大，而是输入信号的振幅A存在一个最优值A_O，此时网络信噪比最大.     

螺旋波动力学性质的元胞自动机有向小世界网络研究

以Greenberg-Hastings激发介质元胞自动机模型为基础,研究了有向小世界网络中重新连接概率p对螺旋波动力学行为的影响.对于在规则网络下的稳定螺旋波,施加有向小世界网络后发现:当p值较小时,原本稳定的螺旋波仍保持其稳定性.随着p的增大,先后观察到螺旋波持续漫游、螺旋波断裂以及螺旋波消失等现象.通过监测系统的激发比率,发现以上现象的产生源于介质激发性随p的增大而降低.同时还发现元胞周期的变化也与p有关. 关键词： 元胞自动机 螺旋波 激发介质 有向小世界网络     

HMX不同晶型热膨胀特性及相变的ReaxFF分子动力学模拟

HMX（octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine）晶体不同晶型对温度的响应对于深入认识含能材料在外加载荷下的感度、稳定性、相变等具有重要意义．采用ReaxFF—lg势函数和等温等压分子动力学（NPT-MD）方法,研究了β-,δ-和α-HMX三种固态晶型在T=303—503K内的晶体结构和分子结构．结果表明,ReaxFF—lg势函数能够合理地描述HMX晶体的热膨胀行为,计算得到的晶体结构和热膨胀系数与实验数据比较符合．三种晶型的线膨胀系数表明,β-HMX晶体具有明显的热膨胀各向异性;δ-HMX晶体C轴的热膨胀特性与a,b轴略有不同;α-HMX晶体三个方向的热膨胀特性基本相同．三种晶型的体膨胀系数为8-HMX〉α-HMX〉β-HMX,表明δ-HMX晶体对温度最敏感,这可能是δ-HMX晶体具有更高感度的原因．在T=443K时,β-HMX晶体的晶胞参数突变,且部分分子构型由“椅式”转变为“船式-椅式”．采用两相热力学模型（2PT）得到的亥姆赫兹自由能表明,β—HMX晶体在T=423—443K时发生相变．β-,δ-和α-HMX晶体分别在T=303—423K,T=443—503K和T=363-423K内稳定存在．     

Paraelectric--ferroelectric interface dynamics induced by latent heat transfer and irreversibility of ferroelectric phase transitions

The temperature gradients that arise in the paraelectric--ferroelectric interface dynamics induced by the latent heat transfer are studied from the point of view that a ferroelectric phase transition is a stationary, thermal-electric coupled transport process. The local entropy production is derived for a ferroelectric phase transition system from the Gibbs equation. Three types of regions in the system are described well by using the Onsager relations and the principle of minimum entropy production. The theoretical results coincides with the experimental ones.     

Multiple phase transitions in the generalized Curie-Weiss model

The generalized Curie-Weiss model is an extension of the classical Curie-Weiss model in which the quadratic interaction function of the mean spin value is replaced by a more general interaction function. It is shown that the generalized Curie-Weiss model can have a sequence of phase transitions at different critical temperatures. Both first-order and second-order phase transitions can occur, and explicit criteria for the two types are given. Three examples of generalized Curie-Weiss models are worked out in detail, including one example with infinitely many phase transitions. A number of results are derived using large-deviation techniques.     

Effects of single particle on shape phase transitions and phase coexistence in odd-even nuclei

A classical analysis of shape phase transitions and phase coexistence in odd-even nuclei has been performed in the framework of the interacting boson-fermion model. The results indicate that the effects of a single particle may influence different types of transitions in different ways. Especially, it is revealed that phase coexistence can clearly emerge in the critical region and thus be taken as a indicator of the shape phase transitions in odd-even nuclei.     

The particle image velocimetry method in the study of the dynamics of phase transitions induced by high pressures in triolein and oleic acid

Particle image velocimetry (PIV) is an optical measurement method capable of providing visualisation of velocity field of particle flow in fluids. After analysis of data acquired in the form of an image sequence, it is possible to retrieve information about flow parameters as mean values of velocity, vorticity, shear and normal strain. This paper presents the results of high pressure experiments using this method applied to triolein and oleic acid samples in their phase transition region. A high pressure optical chamber, He–Ne laser and light-sheet optics together with a digital camera and image acquisition computer allow us to study the motion of particles in high pressure conditions. The set-up was similar to that presented in Özmutlu et al. [Momentum and energy transfer during phase change of water under high hydrostatic pressure, Innov. Food Sci. Emerg. Technol. 7(3) (2006), pp. 161–168] and Kulisiewicz et al. [Visualization of pressure-shift freezing and thawing of concentrated aqueous sucrose solutions, High Press. Res. 27(2) (2007), pp. 291–297]. The analysis of phase transition dynamics in triolein and oleic acid is an extension to the work presented in Tefelski et al. [The investigation of the dynamics of the phase transformation in triolein and oleic acid under pressure, J. Phys.: Conf. Ser. 121(142004) (2008), pp. 1–6]. Oleic acid is a monounsaturated fatty acid and has a bent rod shape. Triolein is a triglyceride and has a “chair”-like shape. It is the base particle of many vegetable oils, especially olive oil. Triolein consists of three chains of oleic acid bound by a glycerol part. Information obtained by the study of phase transitions dynamics is important for food science and food technology processes which involve high pressure treatment. The PIV method shows differences in the solidification process of both substances in time, the existence of inhomogeneities (layers of different densities in the observed flow) and allows us to calculate the parameters of flow using the PIVview2C software from PIVTEC GmbH.     

Hybrid phase transitions of spreading dynamics in multiplex networks

In this paper, we study the spreading dynamics of social behaviors and focus on heterogenous responses of individuals depending on whether they realize the spreading or not. We model the system with a two-layer multiplex network, in which one layer describes the spreading of social behaviors and the other layer describes the diffusion of the awareness about the spreading. We use the susceptible-infected-susceptible (SIS) model to describe the dynamics of an individual if it is unaware of the spreading of the behavior. While when an individual is aware of the spreading of the social behavior its dynamics will follow the threshold model, in which an individual will adopt a behavior only when the fraction of its neighbors who have adopted the behavior is above a certain threshold. We find that such heterogenous reactions can induce intriguing dynamical properties. The dynamics of the whole network may exhibit hybrid phase transitions with the coexistence of continuous phase transition and bi-stable states. Detailed study of how the diffusion of the awareness influences the spreading dynamics of social behavior is provided. The results are supported by theoretical analysis.     

Characteristics of phase transitions via intervention in random networks

We present a percolation process in which the classical Erdo¨s–Re′nyi(ER) random evolutionary network is intervened by the product rule(PR) from some moment t0. The parameter t0is continuously tunable over the real interval [0, 1].This model becomes the random network under the Achlioptas process at t0= 0 and the ER network at t0= 1. For the percolation process at t0≤ 1, we introduce a relatively slow-growing point, after which the largest cluster begins growing faster than that in the ER model. A weakly discontinuous transition is generated in the percolation process at t0≤ 0.5.We take the relatively slow-growing point as the lower pseudotransition point and the maximum gap point of the order parameter as the upper pseudotransition point. The critical point can be approximately predicted by each fitting function of the two points about t0. This contributes to understanding the rapid mergence of the large clusters at the critical point.The numerical simulations indicate that the lower pseudotransition point and the upper pseudotransition point are equal in the thermodynamic limit. When t0> 0.5, the percolation processes generate a continuous transition. The scaling analyses of several quantities are presented, including the relatively slow-growing point, the duration of the relatively slow-growing process, as well as the relatively maximum strength between the percolation percolation at t0< 1 and the ER network about different t0. The presented mechanism can be viewed as a two-stage percolation process that has many potential applications in the growth processes of real networks.     

Structural and topological phase transitions on the German Stock Exchange

We find numerical and empirical evidence for dynamical, structural and topological phase transitions on the (German) Frankfurt Stock Exchange (FSE) in the temporal vicinity of the worldwide financial crash. Using the Minimal Spanning Tree (MST) technique, a particularly useful canonical tool of the graph theory, two transitions of the topology of a complex network representing the FSE were found. The first transition is from a hierarchical scale-free MST representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market’s state for the period containing the crash. Subsequently, a transition is observed from this transient, (meta)stable state of the crash to a hierarchical scale-free MST decorated by several star-like trees after the worldwide financial crash. The phase transitions observed are analogous to the ones we obtained earlier for the Warsaw Stock Exchange and more pronounced than those found by Onnela–Chakraborti–Kaski–Kertész for the S&P 500 index in the vicinity of Black Monday (October 19, 1987) and also in the vicinity of January 1, 1998. Our results provide an empirical foundation for the future theory of dynamical, structural and topological phase transitions on financial markets.     

Spiking sychronization regulated by noise in three types of Hodgkin—Huxley neuronal networks

In this paper,we study spiking synchronization in three different types of Hodgkin-Huxley neuronal networks,which are the small-world,regular,and random neuronal networks.All the neurons are subjected to subthreshold stimulus and external noise.It is found that in each of all the neuronal networks there is an optimal strength of noise to induce the maximal spiking synchronization.We further demonstrate that in each of the neuronal networks there is a range of synaptic conductance to induce the effect that an optimal strength of noise maximizes the spiking synchronization.Only when the magnitude of the synaptic conductance is moderate,will the effect be considerable.However,if the synaptic conductance is small or large,the effect vanishes.As the connections between neurons increase,the synaptic conductance to maximize the effect decreases.Therefore,we show quantitatively that the noise-induced maximal synchronization in the Hodgkin-Huxley neuronal network is a general effect,regardless of the specific type of neuronal network.     

From the bulk to clusters: Solid-liquid phase transitions and precursor effects

A review is given of the experiments done in order to understand the initial stages of melting and solidification of a material. We emphasize on new results obtained for ultrafine metal particles by using high sensitivity reflectance and electron microscopy measurements. These experiments show that melting certainly begins through a continuous and reversible transition and proceeds through the usual first-order transition. For ultrafine particles, these two processes have a relative importance which depend on the size, the first process having the major importance for the smallest particles. Undercooled state and subsequent solidification depend also on the size of the particle. For the smallest particles, the maximum supercooling temperature is found to coincide with the final temperature of the continuous transition leading to disappearance of the hysteretic behaviour usually found in solid-liquid transitions for ultrafine particles.