小世界生物神经网络的相干共振研究

研究了无外界周期信号时Hodgkin-Huxley模型小世界生物神经网络的非线性响应.数值模拟结果显示：当噪声强度取某一有限值时,峰序列有序度可以达到最大,即产生相干共振现象.同时发现： 随着网络规模N的变化,相干共振系数cv的极小值不是一个,而是多个.这表明相干共振可发生在神经元集群数目特定的不同规模的网络中. 关键词： 相干共振 有序度 小世界网络 生物神经网络     

LiAl分子基态、激发态势能曲线和振动能级

利用单双激发多参考组态相互作用方法获得了LiAl分子基态X¹∑⁺及七个激发态a³∏, A¹∏, b³∑⁺, c³∑⁺, B¹∏, C¹∑⁺, d³∏的势能曲线, 通过势能曲线得到各态的平衡核间距R_e, 进而求得绝热激发能和垂直激发能.计算结果表明:c³∑⁺ 电子态是一个不稳定的排斥态, A¹∏态是一个较弱的束缚态, 其余6个电子态均为束缚态; b³∑⁺与 c³∑⁺态之间存在预解离现象; 8个电子态分别解离到两个通道, 即Li(²S)+Al(²P⁰)与Li(²P⁰)+Al(²P⁰). 接着将势能曲线拟合到Murrel-Sorbie解析势能函数形式, 据此获得各态的光谱数据:基态X¹∑⁺的平衡键长为0.2863 nm, 谐振频率为316 cm^-1, 解离能D_e为1.03 eV, 激发态a³∏, A¹∏, b³∑⁺, c³∑⁺, B¹∏, C¹∑⁺, d³∏的垂直激发能依次为0.27, 0.83, 1.18, 1.14, 1.62, 1.81, 2.00 eV; 解离能依次为1.03, 0.82, 0.26, 排斥态, 1.54, 1.10, 0.93 eV, 相应谐振频率 ω_e为339, 237, 394, 排斥态, 429, 192, 178 cm^-1. 通过求解核运动的薛定谔方程找到了J=0时 LiAl分子7个束缚电子态的振动能级和转动惯量. 关键词： LiAl 光谱常数 势能曲线 振动能级     

高阶Adams公式的系数

当k的值不大时(例如k≤4),诸系数γiγi^*,β_kρ,β_kρ^*易于手工计算。但对大的k值(例如k≥5),手工计算已非易事。利用著名的计算机代数软件:REDUCE^[a],我们可以很容易地计算出诸系数γi,γi^*,β_kρ,β_kρ^*。     

镁基合金自由枝晶生长的相场模拟研究

利用定量相场模型, 以Mg-0.5 wt.%Al合金为例模拟了基面((0001)面)内镁基合金的等温自由枝晶生长过程. 通过研究该合金体系数值模拟的收敛性, 获得了最优化值耦合参数λ = 5.5及网格宽度Δx/W₀ = 0.4, 并在该参数下系统研究了各向异性强度和过饱和度对枝晶尖端生长速度、尖端曲率半径、Péclet数及稳定性常数σ^* 的影响. 结果表明, 由微观可解性理论得到的稳定性系数σ^* 与ε₆ 拟合值σ^*≅ ε₆ ^1.81905, 更接近理想值σ ^* (ε₆) ≅ε₆ ^1.75. 此外, 当过饱和度Ω < 0.6时, 稳定性系数σ ^* 不随ε₆ 的变化而变化, 而当Ω > 0.6时, 稳定性系数σ ^* 随着ε₆ 的增加而减小. 这反映了枝晶的生长由扩散控制向动力学控制的转变. 随着过饱和度的增加, 枝晶形貌由雪花状枝晶向圆状枝晶转变.     

层流圆管潜射流生成蘑菇形涡结构特性数值研究

基于不可压Navier-Stokes方程, 采用计算流体力学方法, 数值模拟与分析了层流圆管潜射流在密度均匀黏性流体中的演化机理及其表现特征, 定量研究了蘑菇形涡结构无量纲射流长度L^*、螺旋型涡环半径R^*及其包络外形长度d^*等几 何特征参数随无量纲时间t^*的变化规律. 数值结果表明, 蘑菇形涡结构的形成与演化过程可分为三个不同的阶段: 启动阶段、发展阶段和衰退阶段. 在启动阶段, L^*和d^*随t^* 线性变化, 而R^*则近似为一个常数; 在发展阶段, 蘑菇形涡结构的演化具有自相似性, L^*, R^*和d^*与t^*1/2均为同一正比关系, 而且雷诺数和无量纲射流时间不影响该正比关系; 在衰退阶段, L^* 和R^* 正比于t^*1/5, 而d^*则近似为一个常数. 此外, 还对蘑菇形涡结构二次回流点、 动量源作用中心及其几何中心的速度变化规律、垂向涡量分布特征和 涡量-流函数关系进行了分析. 关键词： 圆管潜射流 蘑菇形涡结构 演化机理     

弹性矩形板方程在非线性边界条件下整体解的存在唯一性

考虑了在非线性边界条件下的弹性矩形板方程.利用Galerkin方法,首先证明了该方程在非线性边界(a)及初值w⁰∈W,w¹∈W的条件下初边值问题存在唯一整体弱解w(t).其次证明了该方程在非线性边界(b)及初值w⁰∈W₁,w¹∈W₁的条件 关键词： 弹性矩形板方程 非线性边界条件 初边值问题 整体解     

一类特殊单模压缩态的Wigner函数

本文运用IWOP技术推导出Wigner算符的相干态显式,计算出一类特殊单模压缩态 |z〉_f,g=exp[－(|z|²)/2 +(fz+gz^*)a⁺－fga⁺²]|0〉的Wigner函数解析式,通过数值计算可以看到,参数f和g的任一个取值固定时,另一个参数的旋转取值会使得特殊 关键词： IWOP技术 Wigner算符 Wigner函数     

一类高聚类系数的加权无标度网络及其同步能力分析

针对真实世界中大规模网络都具有明显聚类效应的特点, 提出一类具有高聚类系数的加权无标度网络演化模型, 该模型同时考虑了优先连接、三角结构、随机连接和社团结构等四种演化机制. 在模型演化规则中, 以概率p增加单个节点, 以概率1–p增加一个社团. 与以往研究的不同在于新边的建立, 以概率φ在旧节点之间进行三角连接, 以概率1–φ进行随机连接. 仿真分析表明, 所提出的网络度、强度和权值分布都是服从幂律分布的形式, 且具有高聚类系数的特性, 聚类系数的提高与社团结构和随机连接机制有直接的关系. 最后通过数值仿真分析了网络演化机制对同步动态特性的影响, 数值仿真结果表明, 网络的平均聚类系数越小, 网络的同步能力越强. 关键词： 无标度网络 加权网络 聚类系数 同步能力     

幂函数叠加势的径向薛定谔方程的解析解

研究多种正幂势函数与逆幂势函数紧密耦合条件下薛定谔径向方程解析解的求解方法.对势函数为V（r）=α₁r⁸＋α₂r³+α₃r²+β₃r^-1＋β₂r^-3＋β₁r^-4的径向薛定谔方程存在解析解的条件以及精确的解析解进行了研究. 根据量子系统波函数必须满足单值、有界和连续的标准条件,首先求出径向坐标r→∞以及r→0时的渐近解,然后采用非正则奇点邻域附近的波函数级数解法与求得的渐近解相结合,通过幂级数系数比较法得到径向薛定谔方程在势函数系数紧密耦合条件下的一系列定态波函数解析解以及相应的能级结构,并作适当讨论与结论. 关键词： 级数解法 幂势函数 径向波函数 渐近解     

Effects of potential functions on stochastic resonance

In this paper, the effects of a bistable potential function U(x)=-ax²/2+b|x|^2γ/(2γ) on stochastic resonance (SR) is discussed. We investigate the effects of index γ on the performance of the SR system with fixed parameters a and b, and with fixed potential barriers, respectively. To measure the performance of the SR system in the presence of an aperiodic input, the bit error rate is employed, as is commonly used in binary communications. The numerical simulations strongly support the theoretical results. The goal of this investigation is to explore the effects of the shape of potential functions on SR and give a guidance of nonlinear systems in the application of information processing.     

Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

In this paper,we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism.Then we study excitement properties of the model under alternating current (AC) stimulation.The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli,such as refractory period and the brain neural excitement response induced by different intensities of nolse and coupling.The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.     

Topological probability and connection strength induced activity in complex neural networks

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.     

蛋白质相互作用网络特征的理论再现

无标度性、小世界性、功能模块结构及度负关联性是大量生物网络共同的特征. 为了理解生物网络无标度性、小世界性和度负关联性的形成机制, 研究者已经提出了各种各样基于复制和变异的网络增长模型. 在本文中,我们从生物学的角度通过引入偏爱小复制原则及变异和非均匀的异源二聚作用构建了一个简单的蛋白质相互作用网络演化模型.数值模拟结果表明,该演化模型几乎可以再现现在实测结果所公认的蛋白质相互作用网络的性质：无标度性、小世界性、度负关联性和功能模块结构. 我们的演化模型对理解蛋白质相互作用网络演化过程中的可能机制提供了一定的帮助. 关键词： 蛋白质相互作用网络 偏爱小 非均匀的异源二聚作用 功能模块结构     

Memorizing morph patterns in small-world neuronal network

In this paper, we study the memory representation of morph patterns in an attractor neural network model. Since recent studies indicate that biological neural networks exhibit the so-called small-world effect, we study here how the small-world connection topology affects the dynamics of memory representation of morph patterns. We find that the small-world connection has significant effects on the memory representation dynamics in the network. Based on this finding, we postulate that global (or long-range) synaptic connections are mainly responsible for learning patterns that are significantly different from those already stored. Further numerical simulations show that the model based on this hypothesis has several advantages, for example fast learning and good performance.     

On coordination and continuous hawk-dove games on small-world networks

It is argued that small-world networks are more suitable than ordinary graphs in modelling the diffusion of a concept (e.g. a technology, a disease, a tradition, ...). The coordination game with two strategies is studied on small-world networks, and it is shown that the time needed for a concept to dominate almost all of the network is of order , where N is the number of vertices. This result is different from regular graphs and from a result obtained by Young. The reason for the difference is explained. Continuous hawk-dove game is defined and a corresponding dynamical system is derived. Its steady state and stability are studied. Replicator dynamics for continuous hawk-dove game is derived without the concept of population. The resulting finite difference equation is studied. Finally continuous hawk-dove is simulated on small-world networks using Nash updating rule. The system is 2-cyclic for all the studied range. Received 8 July 2000 and Received in final form 23 July 2000     

Modeling complex networks of nuclear reaction data for probing their discovery processes

Hundreds of thousands of experimental data sets of nuclear reactions have been systematically collected, and their number is still growing rapidly. The data and their correlations compose a complex system, which underpins nuclear science and technology. We model the nuclear reaction data as weighted evolving networks for the purpose of data verification and validation. The networks are employed to study the growing cross-section data of a neutron induced threshold reaction (n,2n) and photoneutron reaction. In the networks, the nodes are the historical data, and the weights of the links are the relative deviation between the data points. It is found that the networks exhibit small-world behavior, and their discovery processes are well described by the Heaps law. What makes the networks novel is the mapping relation between the network properties and the salient features of the database: the Heaps exponent corresponds to the exploration efficiency of the specific data set, the distribution of the edge-weights corresponds to the global uncertainty of the data set, and the mean node weight corresponds to the uncertainty of the individual data point. This new perspective to understand the database will be helpful for nuclear data analysis and compilation.     

Network evolution by nonlinear preferential rewiring of edges

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.     

Transport Properties of Random Walks on Scale-Free/Regular-Lattice Hybrid Networks

We study numerically the mean access times for random walks on hybrid disordered structures formed by embedding scale-free networks into regular lattices, considering different transition rates for steps across lattice bonds (F) and across network shortcuts (f). For fast shortcuts (f/F≫1) and low shortcut densities, traversal time data collapse onto a universal curve, while a crossover behavior that can be related to the percolation threshold of the scale-free network component is identified at higher shortcut densities, in analogy to similar observations reported recently in Newman-Watts small-world networks. Furthermore, we observe that random walk traversal times are larger for networks with a higher degree of inhomogeneity in their shortcut distribution, and we discuss access time distributions as functions of the initial and final node degrees. These findings are relevant, in particular, when considering the optimization of existing information networks by the addition of a small number of fast shortcut connections.     

基于社团结构的城市地铁网络模型研究

本文运用复杂网络理论, 对我国北京、上海、广州和深圳等城市的地铁网络进行了实证研究. 分别研究了地铁网络的度分布、聚类系数和平均路径长度. 研究表明, 该网络具有高的聚类系数和短的平均路径长度, 显示小世界网络的特征, 其度分布并不严格服从幂律分布或指数分布, 而是呈多段的分布, 显示层次网络的特征. 此外, 它还具有重叠的社团结构特征. 基于实证研究的结果, 提出一种基于社团结构的交通网络模型, 并对该模型进行了模拟分析, 模拟结果表明, 该模型的模拟结果与实证研究结果相符. 此外, 该模型还能解释其他类型的复杂网络(如城市公共汽车交通网络)的网络特性. 关键词： 复杂网络 地铁网络 小世界 社团     

A last updating evolution model for online social networks

As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.