Synchronization Of Networks Research Articles

Fixed/preassigned-time non-chattering synchronization of nonlinear coupled Cohen-Grossberg neural networks via event-triggered control

In this paper, the studies on fixed-time and preassigned-time synchronization for a kind of nonlinear coupled Cohen-Grossberg neural networks (CGNNs) are researched by event-triggered non-chattering control. Firstly, differing from some existed fixed-time synchronization control strategies, a power-law feedback controller without sign function is adopted to achieve non-chattering control effect. To save communication and computational resources, an event-triggered mechanism (ETM) is introduced, which can naturally avoid Zeno behavior. By designing preassigned time parameters as controller parameters, the preassigned-time synchronization of dynamic networks is studied. Then, based on the designed control mechanism, sufficient conditions for fixed-time and preassigned-time synchronization of the nonlinear coupled CGNNs are given. Finally, the validity and generality of the obtained results are verified by numerical simulation.

Projective synchronization of fractional-order quaternion-valued neural networks with Mittag-Leffler kernel

We consider fractional-order quaternion-valued neural networks (FO-QVNNs) with Mittag-Leffler kernel and study projective synchronization. We establish Mittag-Leffler projective synchronization (MLPS) and asymptotic projective synchronization for the FO-QVNNs. Initially, a novel fractional differential inequality for the fractional derivative is established to study the synchronization of fractional-order systems. Subsequently, quaternion-valued feedback controllers are designed. Conditions for MLPS are then formulated using quaternion techniques, the proposed inequality and the properties of fractional calculus. The effectiveness and practicality of the proposed methodologies are displayed through numerical simulations in a concrete case.

Scalable control of cluster synchronization in a chaotic, directed, complex semiconductor laser network

With the rapid growth of information technology, controlling the synchronization of large-scale networks has become more challenging and costly. To address this, we propose a scalable regulation scheme of cluster synchronization in a directed complex semiconductor lasers (SLs) network. The stability of chaotic synchronization for the entire network could be scalably regulated by only modifying the parameters of the driver cluster based on the hierarchical dependency of cluster synchronization in directed network, thus improving the control efficiency and reducing costs. The influence of parameters associated with the driver cluster is discussed systematically, and we numerically and experimentally demonstrate that a complex SLs network could achieve ideal cluster synchronization with the introduction of the proposed scalable regulation scheme. At last, encrypting and decrypting images is realized by leveraging the controlled chaotic cluster synchronization in complex, SLs networks.

Robust Sampled‐Data Synchronization of Memristor Inertial Competitive Neural Networks With Two Delay Components

ABSTRACTThe current study addresses the issue of synchronization in competitive neural networks that are based on memristors and involve an inertial term, parameter uncertainty, and two delay components using sampled‐data control. To achieve synchronization, appropriate Lyapunov‐Krasovskii functionals (LKFs) are constructed, which include double and triple integral terms that capture the information of time delay cross terms. Some sufficient synchronization conditions are derived in terms of linear matrix inequalities (LMIs) using an improved reciprocally convex combination inequality and generalized free weighting matrices inequality. The effectiveness of the proposed findings is highlighted through an illustrative example, validating the robustness and reliability of the developed synchronization approach.

Local Predictors of Explosive Synchronization with Ordinal Methods

We propose using the ordinal pattern transition (OPT) entropy measured at sentinel central nodes as a potential predictor of explosive transitions to synchronization in networks of various dynamical systems with increasing complexity. Our results demonstrate that the OPT entropic measure surpasses traditional early warning signal (EWS) measures and could be valuable to the tools available for predicting critical transitions. In particular, we investigate networks of diffusively coupled phase oscillators and chaotic Rössler systems. As maps, we consider a neural network of Chialvo maps coupled in star and scale-free configurations. Furthermore, we apply this measure to time series data obtained from a network of electronic circuits operating in the chaotic regime.

Finite Time Hybrid Synchronization of Heterogeneous Duplex Complex Networks via Time-varying Intermittent Control

Abstract This paper study the finite time internal synchronization and the external synchronization (hybrid synchronization) for duplex heterogeneous complex networks by time-varying intermittent control. There few study hybrid synchronization of heterogeneous duplex complex networks. Therefore, we research the finite time hybrid synchronization of heterogeneous duplex networks, which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time. To be specific, the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function, the internal synchronization and external synchronization are achieved simultaneously in finite time. Finally, numerical examples are presented to illustrate the validness of theoretical results.

Fixed/preassigned-time synchronization of complex networks under aperiodically intermittent event-triggered control

Fixed/preassigned-time synchronization of complex networks under aperiodically intermittent event-triggered control

Spike propagation by synchronization and vibrational resonance in feedforward Izhikevich neural network

Spike propagation by synchronization and vibrational resonance in feedforward Izhikevich neural network

Finite-Time Synchronization of Fractional-Order Complex-Valued Multi-Layer Network via Adaptive Quantized Control Under Deceptive Attacks

This article investigates the problem of finite-time synchronization of fractional-order complex-valued random multi-layer networks without decomposing them into two real-valued systems. Firstly, by promoting real-valued signum functions, sign functions on the complex-valued domain are introduced. Simultaneously, quantization functions in the complex-valued domain are also introduced, and several related formulas for sign functions and quantization functions in complex-valued domain are established. Under the framework of the given sign function and quantization function, an adaptive quantized control scheme with or without deception attacks is designed. According to the finite-time theorem, Lyapunov function, and graph theory methods, some sufficient criteria for realizing finite-time synchronization in complex-valued fractional-order multi-layer networks have been obtained. Furthermore, the setting time of finite-time synchronization is effectively evaluated. Eventually, the reliability of our results and the practicality of control strategies are verified through numerical examples.

Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters

The finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose complex-valued neural networks into real parts and imaginary parts. This paper addresses the FTCS of coupled memristive neural networks (CMNNs), which are FOCV systems with a time delay. A controller is designed with a complex-valued sign function to achieve FTCS using a non-decomposition approach, which eliminates the need to separate the complex-valued system into its real and imaginary components. By applying fractional-order stability theory, some conditions are derived for FTCS based on the proposed controller. The settling time, related to the system’s initial values, can be computed using the Mittag–Leffler function. We further investigate the optimization of control parameters by formulating an optimization model, which is solved using particle swarm optimization (PSO) to determine the optimal control parameters. Finally, a numerical example and a comparative experiment are both provided to verify the theoretical results and optimization method.

Exponential synchronization of bi-directional associative memory neural networks with delay on arbitrary time domains

Abstract We investigate the exponential synchronization of bi-directional associative memory neural networks with delays on a family of different time domains. By utilizing the theory of time scales, we provide stabilization results that are applicable to continuous-time, discrete-time, and general nonuniform hybrid time domains. Our approach employs a unified matrix-measure theory, a recent alternative to traditional Lyapunov functions, to establish exponential synchronization and design effective feedback laws. Notably, our methodology does not require symmetry or diagonality in the control gain matrix, distinguishing it from prior works. Furthermore, we explore various special cases of the considered systems and provide a detailed discussion highlighting the advantages of our findings over existing results. The effectiveness of our proposed criteria is demonstrated through small-scale and medium-scale simulated numerical examples across different time domains. Additionally, we apply our results to an example from the literature, showcasing the broad applicability and improved performance of our method in comparison to previous approaches.

Hyperbolic Sine Function Control-Based Finite-Time Bipartite Synchronization of Fractional-Order Spatiotemporal Networks and Its Application in Image Encryption

This work is devoted to the hyperbolic sine function (HSF) control-based finite-time bipartite synchronization of fractional-order spatiotemporal networks and its application in image encryption. Initially, the addressed networks adequately take into account the nature of anisotropic diffusion, i.e., the diffusion matrix can be not only non-diagonal but also non-square, without the conservative requirements in plenty of the existing literature. Next, an equation transformation and an inequality estimate for the anisotropic diffusion term are established, which are fundamental for analyzing the diffusion phenomenon in network dynamics. Subsequently, three control laws are devised to offer a detailed discussion for HSF control law’s outstanding performances, including the swifter convergence rate, the tighter bound of the settling time and the suppression of chattering. Following this, by a designed chaotic system with multi-scroll chaotic attractors tested with bifurcation diagrams, Poincaré map, and Turing pattern, several simulations are pvorided to attest the correctness of our developed findings. Finally, a formulated image encryption algorithm, which is evaluated through imperative security tests, reveals the effectiveness and superiority of the obtained results.

Enhanced CB1 receptor function in GABAergic neurons mediates hyperexcitability and impaired sensory-driven synchrony of cortical circuits in Fragile X Syndrome model mice.

Electroencephalographic (EEG) recordings in individuals with Fragile X Syndrome (FXS) and the mouse model of FXS ( Fmr1 KO) display cortical hyperexcitability at rest, as well as deficits in sensory-driven cortical network synchrony. A form of circuit hyperexcitability is observed in ex vivo cortical slices of Fmr1 KO mice as prolonged persistent activity, or Up, states. It is unknown if the circuit mechanisms that cause prolonged Up states contribute to FXS-relevant EEG phenotypes. Here we examined the role of endocannabinoids (eCB) in prolonged Up states in slices and resting and sensory-driven EEG phenotypes in awake Fmr1 KO mice. Bidirectional changes in eCB function are reported in the Fmr1 KO that depend on synapse type (excitatory or inhibitory). We demonstrate that pharmacological or genetic reduction of Cannabinoid Receptor 1 (CB1R) in GABAergic neurons rescues prolonged cortical Up states and deficits in sensory-driven cortical synchrony in Fmr1 KO mice. In support of these findings, recordings from Fmr1 KO cortical Layer (L) 2/3 pyramidal neurons revealed enhanced CB1R-mediated suppression of inhibitory synaptic currents. In contrast, genetic reduction of Cnr1 in glutamatergic neurons did not affect Up state duration, but deletion of Fmr1 in the same neurons was sufficient to cause long Up states. These findings support a model where loss of Fmr1 in glutamatergic neurons leads to enhanced CB1R-mediated suppression of GABAergic synaptic transmission, prolonged cortical circuit activation and reduced sensory-driven circuit synchronization. Results suggest that antagonism of CB1Rs as a therapeutic strategy to correct sensory processing deficits in FXS.

Synchronization in a Memristive Duplex Network: The Impact of Intra-layer and Inter-layer Synaptic Pathways

Synchronization in multiplex networks is crucial for modeling functional connectivity in computational neuroscience. This study analyzes synchronization in a memristive Hindmarsh-Rose duplex network, investigating how synaptic pathways affect synchronization. Initially, electrical, inner-linking, and chemical functions are separately applied to both intra- and inter-layer connections. Then, a more realistic approach combines intra-layer electrical and inner-linking functions with inter-layer chemical synapses. Field coupling is also examined as an inter-layer connection with different intra-layer synapses. Synchronization stability is evaluated using the master stability function and synchronization error. Results show that electrical and inner-linking functions support intra- and inter-layer synchronization as coupling strength increases. However, with chemical synapses, neurons achieve synchronization under limited conditions. Applying chemical synapses to inter-layer connections restricts inter-layer synchronization, although intra-layer synchronization remains observable with electrical or inner-linking functions. Field coupling effectively promotes inter-layer synchronization when paired with intra-layer chemical connections but reduces it with intra-layer electrical or inner-linking functions.

Resting State Functional Connectivity of the Default Mode Network During Opioid Use and Cessation in Treatment-Seeking Persons.

Resting-state functional connectivity analyses have been used to examine synchrony in neural networks in substance use disorders (SUDs), with the default mode network (DMN) one of the most studied. Prior research has generally found less DMN synchrony during use and greater synchrony during cessation, although little research has utilized this method with opioid use. This study examined resting brain activity in treatment-seeking persons who use opioids at two points-when using opioids and when opioid-free-to determine whether the DMN exhibits different levels of connectivity during opioid use and cessation and whether differences in connectivity predict subsequent relapse. The sample included 11 participants who met DSM-5 criteria for opioid use disorder and initiated buprenorphine treatment following fMRI scans that were approximately 3 days apart. Results showed greater functional connectivity in the DMN and the rIFG of the salience network (SN) when participants were abstaining than when actively using opioids. These changes in connectivity predicted 76.2% of the variance in withdrawal symptom severity, with the DMN nodes accounting for an additional 30.9%. Findings warrant further longitudinal exploration of the role of DMN connectivity and its interactions with other networks in relation to abstinence and withdrawal status and examination of its utility as a prognostic marker of cessation or relapse.

Fixed-time quantized synchronization of spatiotemporal networks with output-based quantization communication via boundary control

Fixed-time quantized synchronization of spatiotemporal networks with output-based quantization communication via boundary control

Global Mittag-Leffler synchronization of discontinuous memristor-based fractional-order fuzzy inertial neural networks with mixed delays

Global Mittag-Leffler synchronization of discontinuous memristor-based fractional-order fuzzy inertial neural networks with mixed delays

Pinning impulsive control for quasi-projective synchronization of stochastic multi-layer networks

Pinning impulsive control for quasi-projective synchronization of stochastic multi-layer networks

Synchronization stability of epileptic brain network with higher-order interactions.

Generally, epilepsy is considered as abnormally enhanced neuronal excitability and synchronization. So far, previous studies on the synchronization of epileptic brain networks mainly focused on the synchronization strength, but the synchronization stability has not yet been explored as deserved. In this paper, we propose a novel idea to construct a hypergraph brain network (HGBN) based on phase synchronization. Furthermore, we apply the synchronization stability framework of the nonlinear coupled oscillation dynamic model (generalized Kuramoto model) to investigate the HGBNs of epilepsy patients. Specifically, the synchronization stability of the epileptic brain is quantified by calculating the eigenvalue spectrum of the higher-order Laplacian matrix in HGBN. Results show that synchronization stability decreased slightly in the early stages of seizure but increased significantly prior to seizure termination. This indicates that an emergency self-regulation mechanism of the brain may facilitate the termination of seizures. Moreover, the variation in synchronization stability during epileptic seizures may be induced by the topological changes of epileptogenic zones (EZs) in HGBN. Finally, we verify that the higher-order interactions improve the synchronization stability of HGBN. This study proves the validity of the synchronization stability framework with the nonlinear coupled oscillation dynamical model in HGBN, emphasizing the importance of higher-order interactions and the influence of EZs on the termination of epileptic seizures.

Finite-Time Synchronization of Complex-Valued Neural Networks With Infinite Delays: A Criterion and Examples

Finite-Time Synchronization of Complex-Valued Neural Networks With Infinite Delays: A Criterion and Examples