Behavior Of Phase Synchronization Research Articles

Phase Synchronization and Dynamic Behavior of a Novel Small Heterogeneous Coupled Network

Studying the firing dynamics and phase synchronization behavior of heterogeneous coupled networks helps us understand the mechanism of human brain activity. In this study, we propose a novel small heterogeneous coupled network in which the 2D Hopfield neural network (HNN) and the 2D Hindmarsh–Rose (HR) neuron are coupled through a locally active memristor. The simulation results show that the network exhibits complex dynamic behavior and is different from the usual phase synchronization. More specifically, the membrane potential of the 2D HR neuron exhibits five stable firing modes as the coupling parameter k1 changes. In addition, it is found that in the local region of k1, the number of spikes in bursting firing increases with the increase in k1. More interestingly, the network gradually changes from synchronous to asynchronous during the increase in the coupling parameter k1 but suddenly becomes synchronous around the coupling parameter k1 = 1.96. As far as we know, this abnormal synchronization behavior is different from the existing findings. This research is inspired by the fact that the episodic synchronous abnormal firing of excitatory neurons in the hippocampus of the brain can lead to diseases such as epilepsy. This helps us further understand the mechanism of brain activity and build bionic systems. Finally, we design the simulation circuit of the network and implement it on an STM32 microcontroller.

Can a Simple Dynamical System Describe the Interplay between Drag and Buoyancy in Terrain-Induced Canopy Flows?

Abstract Under nonneutral stratification and in the presence of topography, the dynamics of turbulent flow within a canopy is not yet completely understood. This has, among other consequences, serious implications for the measurement of surface–atmosphere exchange by means of eddy covariance: for example, the measurement of carbon dioxide fluxes is strongly influenced if drainage flows occur during night, when the flow within the canopy decouples from the flow aloft. An improved physical understanding of the behavior of scalars under canopy turbulence in complex terrain is urgently needed. In the present work, the authors investigate the dynamics of turbulent flow within sloped canopies, focusing on the slope wind and potential temperature. The authors concentrate on the presence of oscillatory behavior in the flow variables in terms of switching of flow regimes by conducting linear stability analysis. The authors revisit and correct the simplified theory that exists in the literature, which is based on the interplay between the drag force and the buoyancy. The authors find that the simplified description of this dynamical system cannot exhibit the observed richness of the dynamics. To augment the simplified dynamical system’s analysis, the authors make use of large-eddy simulation of a three-dimensional hill covered by a homogeneous forest and analyze the phase synchronization behavior of the buoyancy and drag forces in the momentum budget to explore the turbulent dynamics in more detail.

Increased synchronization and decreased neural complexity underlie thalamocortical oscillatory dynamics in mild cognitive impairment

Abnormal patterns of electroencephalographic (EEG) alpha oscillations in preclinical stages of dementia reveal a selective vulnerability of thalamocortical circuits to the cascade of neurodegenerative events heralding Alzheimer's disease (AD). EEG-alpha slowing characterizes both mild cognitive impairment (MCI) and healthy aging, but it remains ambiguous whether different neural mechanisms underlie this oscillatory behavior in normal and pathological senescence. In this study, we show that the strength of phase coupling and the level of phase predictability between thalamocortical and cortico-cortical EEG sources of low alpha frequency are abnormally facilitated in MCI patients when compared to healthy elderly subjects. Additionally, we found a loss of neural complexity intrinsic to both thalamic and cortical generators of lower alpha in MCI patients, which likely influenced the aberrant phase synchronization behavior between EEG-alpha sources in this high risk group of AD. Taken together, these results suggest that different neural mechanisms account for the well known slowing of alpha rhythm present in normal aging and MCI patients. Whether these anomalous neural coding mechanisms of lower alpha generation in MCI patients represent a potential electrophysiological marker of mild AD is a topic for future research.

Dynamic behavior of complete synchronization of coupled chaotic oscillators

The dynamic behavior of phase synchronization in coupled oscillators is investigated using Lyapunov exponents and correlation function. A strongest chaotic phase synchronization——complete synchronization was observed, in which the first two transverse Lyapunov exponents are made equal and the projective structures of oscillators become exactly the same.

Synchronization tomography: a method for three-dimensional localization of phase synchronized neuronal populations in the human brain using magnetoencephalography.

We present a noninvasive technique which allows the anatomical localization of phase synchronized neuronal populations in the human brain with magnetoencephalography. We study phase synchronization between the reconstructed current source density (CSD) of different brain areas as well as between the CSD and muscular activity. We asked four subjects to tap their fingers in synchrony with a rhythmic tone, and to continue tapping at the same rate after the tone was switched off. The phase synchronization behavior of brain areas relevant for movement coordination, inner voice, and time estimation changes drastically when the transition to internal pacing occurs, while their averaged amplitudes remain unchanged. Information of this kind cannot be derived with standard neuroimaging techniques like functional magnetic resonance imaging or positron emission tomography.

Scale-invariant fluctuations of the dynamical synchronization in human brain electrical activity

The dynamical properties of large-scale, long-term phase synchronization behavior in the alpha range of electroencephalographic signals were investigated. We observed dynamical phase synchronization and presented evidence of an underlying spatiotemporal ordering. Fluctuations in the duration of episodes of intermittent synchrony are scale-invariant. Moreover, the exponent used to describe this behavior is stable across different normal subjects. The results provide a new feature of self-organization in human brain activity and constitute a quantitative basis for modeling its dynamics.

Dynamic Behavior of Phase Synchronization of Chaos

The dynamic nature of phase synchronization between chaotic outputs and injected periodic signals is investigated. We find that the collapse of stroboscopic section from global distribution to local one and the loss of homogeneity of stroboscopic sections in chaotic attractors for different phases are the key features of this kind of phase synchronization, meanwhile the overall structure of chaotic attractor is not affected apparently by the phase synchronization.

Phase synchronization in discrete chaotic systems

A simple and instantaneous phase definition is proposed for the study of discrete maps by taking the change of chaotic signal at each iteration time as a vector. With such a definition, an exact phase can be calculated at any iteration time for any scalar signal or two-dimensional vector of interest. As examples, the phase synchronization behavior is discussed for a two-dimensional globally coupled map lattice and a one-way coupled map lattice.