Synchronization and Bifurcation Analysis in Coupled Networks of Discrete-Time Systems

Synchronization and bifurcation analysis in coupled networks of discrete-time systems are investigated in the present paper. We mainly focus on some special coupling matrix, i.e., the sum of each row equals a nonzero constant u and the network connection is directed. A result that the network can reach a new synchronous state, which is not the asymptotic limit set determined by the node state equation, is derived. It is interesting that the network exhibits bifurcation if we regard the constant u as a bifurcation parameter at the synchronous state. Numerical simulations are given to show the efficiency of our derived conclusions.     

Coexistence of regular and irregular dynamics in complex networks of pulse-coupled oscillators

For general networks of pulse-coupled oscillators, including regular, random, and more complex networks, we develop an exact stability analysis of synchronous states. As opposed to conventional stability analysis, here stability is determined by a multitude of linear operators. We treat this multioperator problem exactly and show that for inhibitory interactions the synchronous state is stable, independent of the parameters and the network connectivity. In randomly connected networks with strong interactions this synchronous state, displaying regular dynamics, coexists with a balanced state exhibiting irregular dynamics. External signals may switch the network between qualitatively distinct states.     

Speed of synchronization in complex networks of neural oscillators: analytic results based on Random Matrix Theory

We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.     

混沌振子的广义旋转数和同步混沌的Hopf分岔

对应于混沌振子的各个Lyapunov指数,在切空间中定义了广义相位和广义旋转数．广义旋转数和Lyapunov指数相结合,可以更完整地描述混沌吸引子的各个运动模式的运动特征,包括伸缩与旋转．用耦合Duffing振子研究了时空混沌系统在同步混沌失稳时发生的分岔行为．结果表明,耦合振子的同步混沌态可以发生一种Hopf分岔,在Hopf分岔后,系统的功率谱中出现了一个特征频率,其值恰好等于分岔前临界横模的广义旋转数． 关键词：     

Complex Ginzburg-Landau equation on networks and its non-uniform dynamics

Dynamics of the complex Ginzburg-Landau equation describing networks of diffusively coupled limit-cycle oscillators near the Hopf bifurcation is reviewed. It is shown that the Benjamin-Feir instability destabilizes the uniformly synchronized state and leads to non-uniform pattern dynamics on general networks. Nonlinear dynamics on several network topologies, i.e., local, nonlocal, global, and random networks, are briefly illustrated by numerical simulations.     

Pattern evolution in non-synchronizable scale-free networks

Pattern formation and evolution in the desynchronizing process of scale-free complex networks are investigated. Depending on how far the system is away from the synchronizable regime, two types of synchronous patterns are identified, namely, the giant-cluster state (GCS) and the scattered-cluster state (SCS). GCS is observed when a system is immediately outside of the synchronizable regime, where the dynamics undergoes a process of on-off intermittency and the patterns are signatured by the existence of a giant synchronous cluster. As the system leaves away from the synchronizable regime, GCS gradually transforms into SCS, accompanied by the continuous dissolving of the giant cluster. Both the two types of patterns are non-stationary, reflected as the timely changed size and content of the clusters. By introducing a new form of synchronization, the temporal phase synchronization, we investigate the dynamical and statistical properties of these non-stationary patterns. An interesting finding is that the unstable nodes of GCS, i.e. nodes that escape from the giant cluster more frequently, are independent of the coupling strength but are sensitive to the bifurcation types. The intermittent behavior of GCS is analyzed by a theory of snapshot attractors, and the theoretical predications fit the numerical observations qualitatively well.     

The instability of chaotic synchronization in coupled Lorenz systems: from the Hopf to the Co-dimension two bifurcation

We investigate the Hopf bifurcation of the synchronous chaos in coupled Lorenz oscillators. We find that the system undergoes a phase transition along the Hopf instability of the synchronous chaos. The phase transition makes the traveling wave component with the phase difference φ(i)-φ(i+1)=2π/N between adjacent sites unstable. The phase transition also plays a role to relate the Hopf bifurcation with the co-dimension two bifurcation of the synchronous chaos.     

Desynchronization by phase slip patterns in networks of pulse-coupled oscillators with delays

Coupling delays may cause drastic changes in the dynamics of oscillatory networks. In the present paper we investigate how coupling delays alter synchronization processes in networks of all-to-all coupled pulse oscillators. We derive an analytic criterion for the stability of synchrony and study the synchronization areas in the space of the delay and coupling strength. Specific attention is paid to the scenario of destabilization on the borders of the synchronization area. We show that in bifurcation points the system possesses homoclinic loops, which give rise to complex long- or quasi-periodic solutions. These newly born solutions are characterized by a synchronous group, from which an oscillator periodically escapes, laps one period, and rejoins. We call such a dynamical regime “phase slip patterns”.     

Effects of Correlation between Network Structure and Dynamics of Oscillators on Synchronization Transition in a Kuramoto Model on Scale-Free Networks

A recent study has found an explosive synchronization in a Kurammoto model on scale-free networks when the natural frequencies of oscillators are equal to their degrees. In this work, we introduce a quantity to characterize the correlation between the structural and the dynamical properties and investigate the impacts of the correlation on the synchronization transition in the Kuramoto model on scale-free networks. We find that the synchronization transition may be either a continuous one or a discontinuous one depending on the correlation and that strong correlation always postpones both the transitions from the incoherent state to a synchronous one and the transition from a synchronous state to the incoherent one. We find that the dependence of the synchronization transition on the correlation is also valid for other types of distributions of natural frequency.     

Desynchronization transitions in nonlinearly coupled phase oscillators

We consider the nonlinear extension of the Kuramoto model of globally coupled phase oscillators where the phase shift in the coupling function depends on the order parameter. A bifurcation analysis of the transition from fully synchronous state to partial synchrony is performed. We demonstrate that for small ensembles it is typically mediated by stable cluster states, that disappear with creation of heteroclinic cycles, while for a larger number of oscillators a direct transition from full synchrony to a periodic or a quasiperiodic regime occurs.     

Analysis of the asynchronous state in networks of strongly coupled oscillators

A general method is presented for the analysis of the asynchronous state in networks of identical, all-to-all coupled, limit-cycle oscillators of arbitrary dimension and with arbitrarily strong coupling. It is shown that, with strong coupling, this state can be destabilized in directions orthogonal to the limit cycle, which may change the units' behavior qualitatively. An example, involving integrate and fire neurons with spike adaptation, exhibits a bifurcation to a synchronized bursting state for strong feedback coupling. The analysis can account for transitions that cannot be studied in the commonly used phase-coupled approximation.     

Effects of Scale-Free Topological Properties on Dynamical Synchronization and Control in Coupled Map Lattices

In the paper,we study effects of scale-free (SF) topology on dynamical synchronization and control in coupled map lattices (CML).Our strategy is to apply three feedback control methods,including constant feedback and two types of time-delayed feedback,to a small fraction of network nodes to reach desired synchronous state.Two controlled bifurcation diagrams verses feedback strength are obtained respectively.It is found that the value of critical feedback strength γc for the first time-delayed feedback control is increased linearly as ε is increased linearly.The CML with SF loses synchronization and intermittency occurs if γ,＞γc.Numerical examples are presented to demonstrate all results.     

Epidemic dynamics on an adaptive network

Many real-world networks are characterized by adaptive changes in their topology depending on the state of their nodes. Here we study epidemic dynamics on an adaptive network, where the susceptibles are able to avoid contact with the infected by rewiring their network connections. This gives rise to assortative degree correlation, oscillations, hysteresis, and first order transitions. We propose a low-dimensional model to describe the system and present a full local bifurcation analysis. Our results indicate that the interplay between dynamics and topology can have important consequences for the spreading of infectious diseases and related applications.     

一类惯性神经网络的分岔与控制

本文讨论了一类三阶惯性神经网络的稳定性和分岔问题. 利用灵敏度理论,确定了合适的Hopf 分岔参数. 基于Routh-Hurwitz判据和分岔理论,给出了系统稳定性、发生Hopf分岔以及产生静态分岔的条件. 数值模拟不仅验证了理论分析的正确性,还说明了所设计的单节点时滞反馈控制器不仅能延迟网络分岔的发生,还能改变极限环的振幅. 关键词： 惯性神经网络 稳定性 时滞反馈控制 Hopf分岔     

基于状态反馈控制的无线网络拥塞控制流体流模型的Hopf分岔

针对控制无线网络拥塞控制系统中流体流模型的Hopf分岔的问题,提出一种状态反馈控制器.通过选择通信时延作为分岔参数,验证模型在加入状态反馈控制器后,①增加了分岔参数的临界值,扩大了稳定性区域,使系统的Hopf分岔延迟;②通过选择合适的参数,可以容易地改变分岔周期解的稳定性及其分岔方向.理论分析和数据仿真验证了该方法能够有效地控制系统的Hopf分岔.     

Spin Squeezing and Fixed-Point Bifurcation

We study spin squeezing and classical bifurcation in a nonlinear bipartite system. We show that the spin squeezing can be associated with a fixed-point bifurcation in the classical dynamics, namely, it acts as an indicator of the classical bifurcation. For the ground state of a system with coupled giant spins, we find that the spin squeezing achieves its minimum value near the bifurcation point. We also study the dynamics of the spin squeezing, for an initial state corresponding to one of the fixed point, we find that in the stable regime, the spin squeezing exhibits periodic oscillation and always persists except at some fixed times, while in the unstable regime, the periodic oscillation phenomenon disappears and the spin squeezing survives for a short time. Finally, we show that the mean spin squeezing, which is defined to be averaged over time, attains its minimum value near the bifurcation point.     

Spin Squeezing and Fixed-Point Bifurcation

We study spin squeezing and classical bifurcation in a nonlinear bipartite system. We show that the spin squeezing can be associated with a fixed-point bifurcation in the classical dynamics, namely, it acts as an indicator of the classical bifurcation. For the ground state of a system with coupled giant spins, we find that the spin squeezing achieves its minimum value near the bifurcation point. We also study the dynamics of the spin squeezing, for an initial state corresponding to one of the fixed point, we find that in the stable regime, the spin squeezing exhibits periodic oscillation and always persists except at some fixed times, while in the unstable regime, the periodic oscillation phenomenon disappears and the spin squeezing survives for a short time. Finally, we show that the mean spin squeezing, which is defined to be averaged over time, attains its minimum value near the bifurcation point.     

Noise-induced synchronous stochastic oscillations in small scale cultured heart-cell networks

This paper reports that the synchronous integer multiple oscillations of heart-cell networks or clusters are observed in the biology experiment.The behaviour of the integer multiple rhythm is a transition between super-and subthreshold oscillations,the stochastic mechanism of the transition is identified.The similar synchronized oscillations are theoretically reproduced in the stochastic network composed of heterogeneous cells whose behaviours are chosen as excitable or oscillatory states near a Hopf bifurcation point.The parameter regions of coupling strength and noise density that the complex oscillatory rhythms can be simulated are identified.The results show that the rhythm results from a simple stochastic alternating process between super-and sub-threshold oscillations.Studies on single heart cells forming these clusters reveal excitable or oscillatory state nearby a Hopf bifurcation point underpinning the stochastic alternation.In discussion,the results are related to some abnormal heartbeat rhythms such as the sinus arrest.     

Noise-induced synchronization in bidirectionally coupled type-I neurons

We present here some studies on noise-induced order and synchronous firing in a system of bidirectionally coupled generic type-I neurons. We find that transitions from unsynchronized to completely synchronized states occur beyond a critical value of noise strength that has a clear functional dependence on neuronal coupling strength and input values. For an inhibitory-excitatory (IE) synaptic coupling, the approach to a partially synchronized state is shown to vary qualitatively depending on whether the input is less or more than a critical value. We find that introduction of noise can cause a delay in the bifurcation of the firing pattern of the excitatory neuron for IE coupling.     

Analysis of critical and post-critical behaviour of non-linear dynamical systems by the normal form method,Part I: Normalization formulae

A method, based on normal form theory, is presented to study the dynamical behaviour of a system in the neighbourhood of a nearly critical equilibrium state associated with a bifurcation condition. Explicit formulae for the normalization procedure are derived. These formulae can be numerically programmed, avoiding usual complicated algebraic calculations and making the method effectively applicable for n-dimensional systems. Rather general bifurcations can be included: e.g., non-linear flutter (Hopf bifurcation), divergence and internal resonance.