Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

Brain Functional Network Based on Small-Worldness and Minimum Spanning Tree for Depression Analysis

Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization.

基于Small-World网络的非结构化DHT算法

目前,非结构化的P2P路由算法面临着搜索效率低下的严峻问题,这严重影响了非结构算法的应用领域.提出一种基于关键字聚类的分布式哈希表算法,主要思路是将环状关键字空间分成上下两层,下层(AUT层)负责关键字管理,上层(HUB层)负责节点路由.每个节点用一个随机数值作为它的聚类中心,从过往的路由消息中本地节点将抽取文件关键字和节点聚类中心,以聚类原则将这些数据记录到本地路由表中.除了改进非结构化算法的数据组织无序性,另一个目标是提高搜索效率.于是,上述算法的增强算法利用了small-world理论,在HUB层中加入远距离节点的聚类中心,将确定性聚类转化为概率性聚类,故能保证路由长度为O(log2N).

一种基于Small-World和相似度的文本聚类算法

在介绍Small-World基本概念的基础上,利用人类语言中存的Small-World现象,提出了一种基于Small-World的文档关键字提取算法,给出一种相似度聚类算法的描述。

用Small-World设计无组织P2P系统的路由算法

由于peer-to-peer系统在文件共享方面有着巨大的应用前景,peer-to-peer搜索问题已成为目前学术界重点的研究问题之一.对于缺乏缓存机制的无组织P2P系统,已有的分布式路由算法缺乏全局导航能力,属于无序搜索.为此,提出一种key clustering算法,将路由空间分为HUB和AUT两层,从全局角度进行有序搜索.为提高key clustering算法的可扩展性,借鉴Small-world领域的研究成果,在路由表中以一定概率插入连接远距离节点的快捷连接,以缩短平均路径长度.初步仿真实验表明,引入快捷连接的key clustering算法具有良好的搜索能力和扩展性.

Sliding mode synchronization between uncertain Watts-Strogatz small-world spatiotemporal networks

Based on the topological characteristics of small-world networks,a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties.To qualify the effects of uncertain parameters in the response networks,some effective recognition rates are designed so as to achieve a steady value in the extremely fast simulation time period.Meanwhile,the Fisher-Kolmogorov and Burgers spatiotemporal chaotic systems are selected as the network nodes for constructing a drive and a response network,respectively.The simulation results confirm that the developed sliding mode could realize the effective synchronization problem between the spatiotemporal networks,and the outer synchronization is still achieved timely even when the connection probability of the small-world networks changes.

Small-world networks of neuroblastoma cells cultured in three-dimensional polymeric scaffolds featuring multi-scale roughness

Understanding the mechanisms underlying cell-surface interaction is of fundamental importance for the rational design of scaffolds aiming at tissue engineering,tissue repair and neural regeneration applications.Here,we examined patterns of neuroblastoma cells cultured in three-dimensional polymeric scaffolds obtained by two-photon lithography.Because of the intrinsic resolution of the technique,the micrometric cylinders composing the scaffold have a lateral step size of^200 nm,a surface roughness of around 20 nm,and large values of fractal dimension approaching 2.7.We found that cells in the scaffold assemble into separate groups with many elements per group.After cell wiring,we found that resulting networks exhibit high clustering,small path lengths,and small-world characteristics.These values of the topological characteristics of the network can potentially enhance the quality,quantity and density of information transported in the network compared to equivalent random graphs of the same size.This is one of the first direct observations of cells developing into 3D small-world networks in an artificial matrix.

Local Interactions and the Emergence of a Twitter Small-World Network

The small-world phenomenon is found in many self-organising systems. Systems configured in small-world networks spread information more easily than in random or regular lattice-type networks. Whilst it is a known fact that small-world networks have short average path length and high clustering coefficient in self-organising systems, the ego centralities that maintain the cohesiveness of small-world network have not been formally defined. Here we show that instantaneous events such as the release of news items via Twitter, coupled with active community arguments related to the news item form a particular type of small-world network. Analysis of the centralities in the network reveals that community arguments maintain the small-world network whilst ac-tively maintaining the cohesiveness and boundary of the group. The results demonstrate how an active Twitter community unconsciously forms a small-world network whilst interacting locally with a bordering community. Over time, such local interactions brought about the global emergence of the small-world network, connecting media channels with human activities. Understanding the small-world phenomenon in relation to online social or civic movement is important, as evident in the spate of online activists that tipped the power of governments for the better or worst in recent times. The support, or removal of high centrality nodes in such networks has important ramifications in the self-expression of society and civic discourses. The presentation in this article anticipates further exploration of man-made self-organising systems where a larger cluster of adhoc and active community maintains the overall cohesiveness of the network.

Nonlinear Dynamical Behavior in BS Evolution Model Based on Small-World Network Added with Nonlinear Preference

We introduce a modified small-world network adding new links with nonlinearly preferential connectioninstead of adding randomly,then we apply Bak-Sneppen(BS)evolution model on this network.We study severalimportant structural properties of our network such as the distribution of link-degree,the maximum link-degree,and thegth of the shortest path.We further argue several dynamical characteristics of the model such as the important criticalvalue f_c,the f_0 avalanche,and the mutating condition,and find that those characteristics show panticular behaviors.

Mapping the small-world properties of brain networks in Chinese to English simultaneous interpreting by using functional near-infrared spectroscopy

The aim of this study is to examine the small-world properties of functional brain networks inChinese to English simultaneous interpreting(SI)using functional near-infrared spectroscopy(INIRS),In particular,the fNIRS neuroimaging combined with complex network analysis wasperformed to extract the features of functional brain networks underling three translationstrategies associated with Chinese to English SI:"transcoding"that takes the"shortcut"linkingtranslation equivalents between Chinese and the English,code-mixing"that basically does notinvolve blingual procesing,and"transphrasingn that takes the long route"involving amonolingual processing of meaning in Chinese and then another monolingual processing ofmeaning in English.Our results demonstrated that the small-world net work topology was able todistinguish well bet ween the transcoding,code-mixing and transphrasing strategies related toChinese to English SI.

Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.

Energy-Efficient Routing Algorithm Based on Small-World Characteristics

Water quality sensor networks are widely used in water resource monitoring.However,due to the fact that the energy of these networks cannot be supplemented in time,it is necessary to study effective routing protocols to extend their lifecycle.To address the problem of limited resources,a routing optimization algorithm based on a small-world network model is proposed.In this paper,a small-world network model is introduced for water quality sensor networks,in which the short average path and large clustering coefficient of the model are used to construct a super link.A short average path can reduce the network’s energy consumption,and a large coefficient can improve its fault-tolerance ability.However,the energy consumption of the relay nodes near the heterogeneous node is too great,and as such the energy threshold and non-uniform clustering are constructed to improve the lifecycle of the network.Simulation results show that,compared with the low-energy adaptive clustering hierarchy routing algorithm and the best sink location clustering heterogeneous network routing algorithm,the proposed improved routing model can effectively enhance the energy-utilization.The lifecycle of the network can be extended and the data transmission amount can be greatly increased.

Small-World在对等网络中的应用研究

论文通过对small-world现象的研究分析,提出了一个构建具有small-world特性的对等网络的解决方案——小世界P2P资源搜索协议,并通过仿真实验证明了协议的有效性和可行性。最后论文对未来的工作做了总结和相关的展望。

Chaos in complex motor networks induced by Newman-Watts small-world connections

We investigate how dynamical behaviours of complex motor networks depend on the Newman-Watts small-world (NWSW) connections.Network elements are described by the permanent magnet synchronous motor (PMSM) with the values of parameters at which each individual PMSM is stable.It is found that with the increase of connection probability p,the motor in networks becomes periodic and falls into chaotic motion as p further increases.These phenomena imply that NWSW connections can induce and enhance chaos in motor networks.The possible mechanism behind the action of NWSW connections is addressed based on stability theory.

Neural Network Predictive Control of Variable-pitch Wind Turbines Based on Small-world Optimization Algorithm

通过将混沌映射用于产生初始节点集和进行算子构造,提出一种新的基于实数编码的混沌小世界优化算法。采用4种算法对多例复杂函数的优化问题进行仿真试验,表明所提算法具有能够有效避免陷入局部极小值、快速搜索到最优值的能力。将上述方法应用于变桨距风电机组启动并网时的转速控制,提出一种基于混沌小世界优化算法的神经网络预测控制策略,其预测模型由基于现场数据的神经网络模型建立。仿真与实际测试结果表明,该系统可以根据风速扰动提前预测电机的转速变化,使控制器超前动作,保证系统输出跟踪参考轨迹的方向稳步改变,确保风电机组平稳并网。

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.

Small-World在确定性Ad hoc网络信任模型中的应用

该文首先介绍了小世界(Small-W orld)现象的起源、发展现状及在现实中的应用。随后介绍了一种基于证书链的Adhoc网络的信任模型。然后根据Ad hoc网络拓扑结构确定性理论,为了在Ad hoc网络中实现“Small-W orld”现象,使得网络中任意节点之间的信息传输中所经历的中间节点尽可能少,从而节约Ad hoc网络中存在的信息传输对节点计算和电力等资源的不必要的消耗。基于上述理论及对现实应用的考虑,该文建立了一个模型,并在NS-2上进行了仿真实验,最后对该模型的安全性方面进行了一些讨论。

Small-worldness of brain networks after brachial plexus injury: a resting-state functional magnetic resonance imaging study

Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury(right handedness,mean age of 27.9±5.4years old)and eight male healthy controls(right handedness,mean age of 28.6±3.2).After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.

Impact of small-world topologies on broadcasting for wireless sensor networks

Broadcasting is an important operation and been widely used in wireless sensor networks (WSNs). These networks are power constrained as nodes operate with limited battery power. Wireless sensor networks are spatial graphs that have much more clustered and much high path-length characteristics. After considering energyeffcient broadcasting in such networks, by combining the small-world characteristic of WSNs and the properties of ant algorithm to quickly identify an optimal path, small-world power-aware broadcast algorithm is introduced and evaluated. Given different densities of network, simulation results show that our algorithm significantly improves life of networks and also reduces communication distances and power consumption.

Bifurcations and chaos control in discrete small-world networks

An impulsive delayed feedback control strategy to control period-doubling bifurcations and chaos is proposed.The control method is then applied to a discrete small-world network model.Qualitative analyses and simulations show that under a generic condition,the bifurcations and the chaos can be delayed or eliminated completely.In addition,the periodic orbits embedded in the chaotic attractor can be stabilized.