Synchronization Of Oscillators Research Articles

Synchronization of phase oscillators with chemical coupling: Removal of oscillators and external feedback control

In many applications of biological interest spatially localized phase oscillators present a non-local coupling characterized by the diffusion of a mediating chemical. The latter is both produced and absorbed by the phase oscillators according to their dynamical behavior. We investigate numerically phase and frequency synchronization in a model for chemical coupling of uniform phase oscillators with natural frequencies randomly distributed according to some statistical distribution. We study the effect of removal of oscillators, caused by lesions, on the synchronization properties of the network. The partial or complete suppression of synchronization is also investigated through the influence of a time-delayed feedback external signal.

Patterns and stability of coupled multi-stable nonlinear oscillators

Nonlinear isolated and coupled oscillators are extensively studied as prototypical nonlinear dynamics models. Much attention has been devoted to oscillator synchronization or the lack thereof. Here, we study the synchronization and stability of coupled driven-damped Helmholtz–Duffing oscillators in bi-stability regimes. We find that despite the fact that the system parameters and the driving force are identical, the stability of the two states to spatially non-uniform perturbations is very different. Moreover, the final stable states, resulting from these spatial perturbations, are not solely dictated by the wavelength of the perturbing mode and take different spatial configurations in terms of the coupled oscillator phases.

Synchronization of Bloch oscillations by gate voltage modulation

We propose to synchronize Bloch oscillations in a double phase-slip junction by modulating the gate voltage rather than the bias voltage. We show this is advantageous and the relatively small a.c. modulation of the gate voltage gives rise to the pronounced plateaux of quantized current of the width of the order of Coulomb blockade threshold. We theoretically investigate the setup distinguishing three regimes of the strong, weak, and intermediate coupling defined by the ratio of the gate capacitance $C$ and the effective capacitance of the phase-slip junctions. An important feature of the intermediate coupling regime is the occurrence of the fractional plateaux of the quantized current. We investigate the finite temperature effects finding an empirical scaling for the smoothing of integer plateaux.

Invitro cell models merging circadian rhythms and brain waves for personalized neuromedicine.

New evidence is emerging about the dynamics of interaction between circadian rhythms and brain waves, whose coordination occurs through the entrainment process. The so-called "oscillopathies" or dysfunctions in synchronization of neuronal oscillation in key brain networks lead to the onset of neurodegenerative diseases. A typical example of alteration is insomnia, a risk factor for the oscillopathies, increasingly widespread worldwide. Recently, synchronization of circadian rhythms in cell cultures has allowed an improvement in the physiological relevance of responses to stimuli. Furthermore, brain organoids and neurons cultured in microfluidic systems are the latest frontiers for invitro reproduction of rhythmic electrical signals. In this review, the combination of these invitro experimental approaches is proposed as suitable for a more direct investigation on the common mechanisms and neurophysiological substrates underlying brain waves and circadian oscillations, and useful to evaluate the effects of "oscillotherapeutic" drugs for personalized neuromedicine.

Direction of Arrival Estimation and Phase-Correction for Noncoherent Subarrays: A Convex Optimization Approach

Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization, and radar. In this article, we consider a challenging multisource DOA estimation task, where the receiving antenna array is composed of noncoherent subarrays, i.e., subarrays that observe different unknown phase shifts at every snapshot (e.g., due to waiving the demanding synchronization of local oscillators across the entire array). We formulate this problem as the reconstruction of joint sparse and low-rank matrices and solve the problem’s convex relaxation. To scale the optimization complexity with the number of snapshots better than general-purpose solvers, we design an optimization scheme, based on integrating the alternating direction method of multipliers and the accelerated proximal gradient techniques, which exploits the structure of the problem. While the DOAs can be estimated from the solution of the aforementioned convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the subarrays’ phase shifts. This is done using another, computationally light, convex relaxation that is practically tight. Using the estimated phase shifts, “phase-corrected” observations are created and a final plain (“coherent”) DOA estimation method can be applied. Numerical experiments show the performance advantages of the proposed strategies over existing methods.

Heterogeneity-induced synchronization in delay-coupled electronic oscillators.

We study synchronization in networks of delay-coupled electronic oscillators, so-called phase-locked loops (PLLs). Using a phase-model description, we study the collective dynamics of mutually coupled PLLs and report the phenomenon of heterogeneity-induced synchronization. This phenomenon refers to the observation that heterogeneity in the system's parameters can induce synchronization by stabilizing the states which are unstable without such heterogeneity. In systems where component heterogeneity can be tuned and controlled, we show how the complex collective self-organized dynamics can be guided towards synchronized states with specific operational frequencies and phase relations. This is of importance for the technical applicability of self-organized dynamics. In electrical engineering, for example, where components can be strongly heterogeneous, our theoretical framework can inform the design process for networks of spatially distributed PLLs. The results presented here are also useful in understanding the collective dynamics in ensembles of phase oscillators with time-delayed interactions, inertia, and heterogeneity.

Oscillatory Activities in Multiple Frequency Bands in Patients with Schizophrenia During Motion Perception.

Patients with schizophrenia show impairment in binding stimulus features into coherent objects, which are reflected in disturbed oscillatory activities. This study aimed to identify disturbances in multiple oscillatory bands during perceptual organization of motion perception in patients with schizophrenia. EEG was recorded from healthy controls and patients with schizophrenia during continuous presentation of a motion stimulus which induces reversals between two exogenously generated perceptions. This stimulus was used to investigate differences in motion binding processes between healthy controls and patients with schizophrenia. EEG signals were transformed into frequency components by means of the Morlet wavelet transformation in order to analyse inter-trial coherences (ITC) in the delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), and gamma (28-48 Hz) frequency bands during exogenous motion binding. Patients showed decreased delta-ITC in occipital and theta-ITC in central and parietal areas, while no significant differences were found for neither alpha nor gamma-ITCs. The present study provides one of the first insights on the oscillatory synchronizations related with the motion perception in schizophrenia. The ITC differences revealed alterations in the consistency of large-scale integration and transfer functions in patients with schizophrenia.

Neuromodulation in the developing visual cortex after long-term monocular deprivation.

Neural dynamics are altered in the primary visual cortex (V1) during critical period monocular deprivation (MD). Synchronization of neural oscillations is pertinent to physiological functioning of the brain. Previous studies have reported chronic disruption of V1 functional properties such as ocular dominance, spatial acuity, and binocular matching after long-term monocular deprivation (LTMD). However, the possible neuromodulation and neural synchrony has been less explored. Here, we investigated the difference between juvenile and adult experience-dependent plasticity in mice from intracellular calcium signals with fluorescent indicators. We also studied alterations in local field potentials power bands and phase-amplitude coupling (PAC) of specific brain oscillations. Our results showed that LTMD in juveniles causes higher neuromodulatory changes as seen by high-intensity fluorescent signals from the non-deprived eye (NDE). Meanwhile, adult mice showed a greater response from the deprived eye (DE). LTMD in juvenile mice triggered alterations in the power of delta, theta, and gamma oscillations, followed by enhancement of delta-gamma PAC in the NDE. However, LTMD in adult mice caused alterations in the power of delta oscillations and enhancement of delta-gamma PAC in the DE. These markers are intrinsic to cortical neuronal processing during LTMD and apply to a wide range of nested oscillatory markers.

Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects

With the recent developments in renewable energy generation and addition of power electronic devices, power system dynamics have become extremely complex. One of the challenges faced due to this transition is the sub synchronous oscillations caused by the interaction of renewable energy sources and various components of the power grid. Recently reported incidents due to sub synchronous oscillations highlight the need of monitoring and suppression of these harmful oscillations in real time. This paper gives an overview of the phenomena of sub synchronous oscillations and discusses the existing monitoring and damping techniques along with their limitations. Further, it highlights the research trends along this path.

Intergenerational genotypic interactions drive collective behavioural cycles in a social insect

Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely (but more surprisingly), indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).

De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.

Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.

A study of the acoustic effect inside cylindrical bubble produced by underwater electrical discharge

The evolution of a cylindrical gaseous bubble produced by an underwater electrical discharge is considered in the present study. Both the gas flow inside and the water flow around the bubble are theoretically analyzed in a cylindrical coordinate system. By using the potential flow theory and multiple scale expansion method, governing equations of both flows and the bubble surface are formulated. The radial oscillation of the bubble surface is composed of a slow-changing equilibrium part and a fast-changing displacement. The former corresponds to a quiescent water domain and a uniform gas column, and the latter corresponds to acoustic waves in gas and water flows. The axial gas wave can evolve into a stable standing wave if the bubble length is multiples of half a wavelength. The internal acoustic standing wave then causes a synchronous small-amplitude oscillation of the bubble surface when the frequency of the acoustic wave is close to the natural frequency of the bubble surface. An underwater discharge experiment is implemented to validate our theory. Finally, a novel method to estimate the plasma pressure is proposed based on our theory.

The Butterfly Effect in Primary Visual Cortex

Exploring and establishing artificial neural networks with electrophysiological characteristics and high computational efficiency is a popular topic in the field of computer vision. Inspired by the working mechanism of primary visual cortex, pulse-coupled neural network (PCNN) can exhibit the characteristics of synchronous oscillation, refractory period, and exponential decay. However, electrophysiological evidence shows that the neurons exhibit highly complex non-linear dynamics when stimulated by external periodic signals. This chaos phenomenon, also known as the " butterfly effect", cannot be explained by all PCNN models. In this work, we analyze the main obstacle preventing PCNN models from imitating real primary visual cortex. We consider neuronal excitation as a stochastic process. We then propose a novel neural network, called continuous-coupled neural network (CCNN). Theoretical analysis indicates that the dynamic behavior of CCNN is distinct from PCNN. Numerical results show that the CCNN model exhibits periodic behavior under DC stimulus, and exhibits chaotic behavior under AC stimulus, which is consistent with the results of real neurons. Furthermore, the image and video processing mechanisms of the CCNN model are analyzed. Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.

Synchronization induced by directed higher-order interactions

Non-reciprocal interactions play a crucial role in many social and biological complex systems. While directionality has been thoroughly accounted for in networks with pairwise interactions, its effects in systems with higher-order interactions have not yet been explored as deserved. Here, we introduce the concept of M-directed hypergraphs, a general class of directed higher-order structures, which allows to investigate dynamical systems coupled through directed group interactions. As an application we study the synchronization of nonlinear oscillators on 1-directed hypergraphs, finding that directed higher-order interactions can destroy synchronization, but also stabilize otherwise unstable synchronized states.

Synchronization of cooperative and competitive oscillators in dynamical network

Synchronization is a collective behavior that occurs spontaneously, describing the coherence of a collection of dynamic units of a system. The competitive and cooperative behavior of the synchronization process as the simplest synergistic behaviors can promote or inhibit each other, which has been widely investigated for its practical significance in recent years. Here, we report that the synchronization phase transition type can be tuned by adjusting the balance between the two oscillator types and the communication radius of the dynamical network in a population dynamical network with uniformly distributed competitive and cooperative oscillators. Agents of both cooperative and competitive oscillators move and interact only with their neighbors in a system. By numerical simulations, we demonstrate a rich process of synchronous transitions, especially that the transition can be regulated between continuous and explosive by adjusting the balance between the two oscillator types under a specific dynamic network. Moreover, the communication radius of the network and the number of oscillators determine both the density of oscillators in the system, thus changing the connectivity and the synchronization characteristics of the network.

Synchronization of Optomechanical Oscillators in Coupled 1D Optomechanical Crystal Nanobeam Cavities

We proposed a new optomechanical system (OMS) based on parallel suspended one-dimensional optomechanical crystal (1D-OMC) nanobeam cavities for optomechanical synchronization. The optomechanical oscillators (OMOs) were spaced apart by an air-slot gap and coupled through optical radiation fields. The numerical simulation showed that the evolution process of 1D-OMC nanobeam cavities to mechanical synchronization could be divided into three clear stages. The synchronization of two mechanical breathing modes at 5.8846 GHz was achieved by using a single laser source. Finally, we investigated the relationship between the threshold power and detuning of an input laser for self-sustaining and synchronization states. Such chip-based structures hold great potential for large-scale synchronized oscillator networks.

Use of Chaotic Oscillations for Precoding and Synchronization in OFDM

This paper proposes a novel linear precoding method for Orthogonal Frequency Division Multiplexing (OFDM) based on the employment of the chaotic waveforms generated by the fourth-order chaotic oscillator and orthonormalized by the Gram-Schmidt process. The proposed linear precoding method is aimed to increase resilience to the multipath propagation issues and reduce the Peak-to-Average Power Ratio (PAPR) of the transmitted signal. Moreover, the chaotic waveform enables novel timing synchronization methods to be implemented in the receiver. The modelling of baseband Linear Precoded OFDM (LP-OFDM) data transmission system with Rayleigh channel has been performed in Simulink environment to validate the proposed method and to compare the performance to the classic precoding methods, such as Walsh-Hadamard Transform (WHT). Experiments have shown that in a high Signal-to-Noise Ratio (SNR) scenario, the employment of the novel precoding scheme allows reducing Bit Error Ratio (BER) by several dB compared to non-precoded OFDM. The proposed precoding method leads to the reduction of PAPR; however, it is not as efficient as classical precoding schemes, such as WHT. Experimental evidence of synchronization of the chaotic oscillators within 50 samples long time interval is presented.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding.

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

ИССЛЕДОВАНИЕ ФАЗОВЫХ ОШИБОК АНТЕННЫХ УСТРОЙСТВ СИСТЕМЫ СИНХРОНИЗАЦИИ

The requirements for the characteristics of antenna devices on synchronization systems are considered: phase center of antennas, radiation pattern, polarization. An antenna system for a spaced oscillator synchronization system is presented, simulation results are presented for estimating phase errors for various types of antennas. Key words: antennas, synchronization system, phase errors.

Heterogeneous mechanisms for synchronization of networks of resonant neurons under different E/I balance regimes.

Rhythmic synchronization of neuronal firing patterns is a widely present phenomenon in the brain-one that seems to be essential for many cognitive processes. A variety of mechanisms contribute to generation and synchronization of network oscillations, ranging from intrinsic cellular excitability to network mediated effects. However, it is unclear how these mechanisms interact together. Here, using computational modeling of excitatory-inhibitory neural networks, we show that different synchronization mechanisms dominate network dynamics at different levels of excitation and inhibition (i.e. E/I levels) as synaptic strength is systematically varied. Our results show that with low synaptic strength networks are sensitive to external oscillatory drive as a synchronizing mechanism-a hallmark of resonance. In contrast, in a strongly-connected regime, synchronization is driven by network effects via the direct interaction between excitation and inhibition, and spontaneous oscillations and cross-frequency coupling emerge. Unexpectedly, we find that while excitation dominates network synchrony at low excitatory coupling strengths, inhibition dominates at high excitatory coupling strengths. Together, our results provide novel insights into the oscillatory modulation of firing patterns in different excitation/inhibition regimes.