Synchronization Behavior Research Articles

Exploring the Dynamics of Canine-Assisted Interactions: A Wearable Approach to Understanding Interspecies Well-Being.

Canine-assisted interactions (CAIs) have been explored to offer therapeutic benefits to human participants in various contexts, from addressing cancer-related fatigue to treating post-traumatic stress disorder. Despite their widespread adoption, there are still unresolved questions regarding the outcomes for both humans and animals involved in these interactions. Previous attempts to address these questions have suffered from core methodological weaknesses, especially due to absence of tools for an efficient objective evaluation and lack of focus on the canine perspective. In this article, we present a first-of-its-kind system and study to collect simultaneous and continuous physiological data from both of the CAI interactants. Motivated by our extensive field reviews and stakeholder feedback, this comprehensive wearable system is composed of custom-designed and commercially available sensor devices. We performed a repeated-measures pilot study, to combine data collected via this system with a novel dyadic behavioral coding method and short- and long-term surveys. We evaluated these multimodal data streams independently, and we further correlated the psychological, physiological, and behavioral metrics to better elucidate the outcomes and dynamics of CAIs. Confirming previous field results, human electrodermal activity is the measure most strongly distinguished between the dyads' non-interaction and interaction periods. Valence, arousal, and the positive affect of the human participant significantly increased during interaction with the canine participant. Also, we observed in our pilot study that (a) the canine heart rate was more dynamic than the human's during interactions, (b) the surveys proved to be the best indicator of the subjects' affective state, and (c) the behavior coding approaches best tracked the bond quality between the interacting dyads. Notably, we found that most of the interaction sessions were characterized by extended neutral periods with some positive and negative peaks, where the bonded pairs might display decreased behavioral synchrony. We also present three new representations of the internal and overall dynamics of CAIs for adoption by the broader field. Lastly, this paper discusses ongoing options for further dyadic analysis, interspecies emotion prediction, integration of contextually relevant environmental data, and standardization of human-animal interaction equipment and analytical approaches. Altogether, this work takes a significant step forward on a promising path to our better understanding of how CAIs improve well-being and how interspecies psychophysiological states can be appropriately measured.

A Chaos Synchronization Diagnostic: Difference Time Series Peak Complexity (DTSPC).

Chaotic systems can exhibit completely different behaviors given only slightly different initial conditions, yet it is possible to synchronize them through appropriate coupling. A wide variety of behaviors-complete chaos, complete synchronization, phase synchronization, etc.-across a variety of systems have been identified but rely on systems' phase space trajectories, which suppress important distinctions between very different behaviors and require access to the differential equations. In this paper, we introduce the Difference Time Series Peak Complexity (DTSPC) algorithm, a technique using entropy as a tool to quantitatively measure synchronization. Specifically, this uses peak pattern complexity created from sampled time series, focusing on the behavior of ringing patterns in the difference time series to distinguish a variety of synchronization behaviors based on the entropic complexity of the populations of various patterns. We present results from the paradigmatic case of coupled Lorenz systems, both identical and non-identical, and across a range of parameters and show that this technique captures the diversity of possible synchronization, including non-monotonicity as a function of parameter as well as complicated boundaries between different regimes. Thus, this peak pattern entropic analysis algorithm reveals and quantifies the complexity of chaos synchronization dynamics, and in particular captures transitional behaviors between different regimes.

From behavioral synchrony to language and beyond.

Decades of research on joint attention, coordinated joint engagement, and social contingency identify caregiver-child interaction in infancy as a foundation for language. These patterns of early behavioral synchrony contribute to the structure and connectivity of the brain in the temporoparietal regions typically associated with language skills. Thus, children attune to their communication partner and subsequently build cognitive skills directly relating to comprehension and production of language, literacy skills, and beyond. This has yielded marked interest in measuring this contingent, synchronous social behavior neurally. Neurological measures of early social interactions between caregiver and child have become a hotbed for research. In this paper, we review that research and suggest that these early neural couplings between adults and children lay the foundation for a broader cognitive system that includes attention, problem solving, and executive function skills. This review describes the role of behavioral synchrony in language development, asks what the relationship is between neural synchrony and language growth, and how neural synchrony may play a role in the development of a broader cognitive system founded in a socially-gated brain. We address the known neural correlates of these processes with an emphasis on work that examines the tight temporal contingency between communicative partners during these rich social interactions, with a focus on EEG and fNIRS and brief survey of MRI and MEG.

Multilayer structure-induced collective dynamics in uncoupled memristive Rulkov neurons: Impact of field coupling and intralayer connections

In this study, we explore a multilayered structure in which the coupled oscillators of one layer serve as a shared medium for an uncoupled population of neurons. The layers function based on the memristive Rulkov map, and interlayer connections are established through magnetic flux variables, referred to as field coupling. We adopt both hybrid (electrical and chemical) and exclusively chemical couplings for intralayer connectivity. The study highlights the pivotal role of the reversal potential in the dynamics of chemical coupling, while the firing threshold and sigmoid slope play lesser roles. Synchrony analysis reveals distinct synchronization behaviors between the layers. Notably, although the coupled layer can achieve phase synchrony, it fails to induce comparable synchrony in the uncoupled layer. Our findings also highlight the emergence of distinct collective dynamics in the uncoupled network, influenced by the coherence level of the flux variables in the coupled layer. Specifically, incoherent, two-clustered, and globally synchronized oscillations of flux variables in the coupled layer lead to chimera states, two-cluster synchronization, and complete synchronization in the uncoupled neurons, respectively.

Static pinning synchronization control of self-triggered coupling dynamical networks

In this paper, a new static pinning intermittent control based on resource awareness triggering is proposed. A multi-layer control technique is used to synchronize the coupled neural network. First, a hierarchical network structure including pinned and interaction layers is induced using each pinning strategy. Second, using the ideas of average aperiodic intermittent control (AIC) rate method and constructing an auxiliary function, a new lemma is proposed for the pinning intermittent synchronization of coupled networks, where the upper/lower bound restrictions on each control width for AIC are relaxed. Third, to obtain the desired synchronization behavior, a self-triggering mechanism (STM) is proposed to execute the AIC of the pinned and interaction layers. Moreover, the proposed STM is effective for the actuation of the static pinning impulsive control. The static pinning method modifies the single pinning and switching pinning impulsive control. Finally, the proposed results are applied to Chua’s circuits, oscillators and small-world networks. Experimental results show the performance of the proposed STM which reduces 34.58% the number of control updates compared to a periodically intermittent event-triggered scheme. Further, for large-scale coupled networks, the N-dimensional Laplacian matrix LV can be decomposed into sp×sp and N−sp×N−sp dimensions by hierarchical method, thus reducing the complexity of calculation.

ChimpanSEE, ChimpanDO: Grooming and play contagion in chimpanzees.

Behavioural contagion-the onset of a species-typical behaviour soon after witnessing it in a conspecific-forms the foundation of behavioural synchrony and cohesive group living in social animals. Although past research has mostly focused on negative emotions or neutral contexts, the sharing of positive emotions in particular may be key for social affiliation. We investigated the contagion of two socially affiliative interactive behaviours, grooming and play, in chimpanzees. We collected naturalistic observations of N = 41 sanctuary-living chimpanzees at Chimfunshi Wildlife Orphanage, conducting focal follows of individuals following observations of a grooming or play bout, compared with matched controls. We then tested whether the presence and latency of behavioural contagion was influenced by age, sex, rank, and social closeness. Our results offer evidence for the presence of grooming and play contagion in sanctuary-living chimpanzees. Grooming contagion appeared to be influenced by social closeness, whilst play contagion was more pronounced in younger individuals. These findings emphasise that contagion is not restricted to negatively valenced or self-directed behaviours, and that the predictors of contagious behaviour are highly specific to the behaviour and species in question. Examining the factors that influence this foundational social process contributes to theories of affective state matching and is key for understanding social bonding and group dynamics.

The slow clock is the master

AbstractWe investigate the synchronization and stability of mutually coupled oscillators through small impacts and explore both master–master and master–slave synchrony. The synchronization behaviour between two oscillators, one operating at a frequency $$ \omega $$ ω and the other at a frequency near a multiple of the first one, $$ n \omega $$ n ω where $$ n $$ n is an integer greater than 1, is investigated. It is observed that such systems tend to exhibit a consistent pattern of master–slave relationship. This phenomenon is geometrically elucidated by considering the interaction dynamics: while the multiple bursts of the faster oscillator tend to cancel each other out upon impacting the slower oscillator, the single burst of the slower oscillator on the faster has a secular effect. We propose that this synchronization pattern, arising from perturbative impacts, is prevalent across various physical systems. Further experimental validation of these findings could contribute to a deeper understanding of synchronization phenomena and their implications in natural and technological domains.

Hyperscanning: from inter-brain coupling to causality.

In hyperscanning studies, participants perform a joint task while their brain activation is simultaneously recorded. Evidence of inter-brain coupling is examined, in these studies, as a predictor of behavioral change. While the field of hyperscanning has made significant strides in unraveling the associations between inter-brain coupling and changes in social interactions, drawing causal conclusions between brain and behavior remains challenging. This difficulty arises from factors like the inherently different timescales of behavioral responses and measured cerebral activity, as well as the predominant focus of existing methods on associations rather than causality. Specifically, a question remains as to whether inter-brain coupling between specific brain regions leads to changes in behavioral synchrony, or vice-versa. We propose two novel approaches to addressing this question. The first method involves using dyadic neurofeedback, wherein instances of inter-brain coupling are directly reinforced. Such a system could examine if continuous changes of inter-brain coupling are the result of deliberate mutual attempts to synchronize. The second method employs statistical approaches, including Granger causality and Structural Equation Modeling (SEM). Granger causality assesses the predictive influence of one time series on another, enabling the identification of directional neural interactions that drive behavior. SEM allows for detailed modeling of both direct and indirect effects of inter-brain coupling on behavior. We provide an example of data analysis with the SEM approach, discuss the advantages and limitations of each approach and posit that applying these approaches could provide significant insights into how inter-brain coupling supports crucial processes that occur in social interactions.

The coupling dynamic characteristics and vibration suppression of a double-beam structure with two linear oscillators installed separately

This paper investigates the coupling dynamic characteristics of a double-beam structure with two linear oscillators that generate harmonic concentrated excitation and the vertical elastic support boundary, including the Sommerfeld effect and the synchronization behavior. The governing equations of motion with boundary conditions are developed using Hamilton's principle. The Sommerfeld effect near the resonance region is characterized by transient power balance analysis, and the critical power of the motor is given to allow the system to pass through the resonance region. The theoretical condition for the synchronous behavior of two linear oscillators is derived using the average method, and its stability is also determined. The synchronization characteristics are analyzed and compared with the numerical steady-state response of typical physical parameters. Results show good agreement. Further, on the condition of the linear oscillators exhibiting synchronous behavior, sensitive working regions and parameters are sought to suppress the dynamic loads transmitted from the system to the foundation. The parameter optimization results show that the stable phase difference plays a crucial role in vibration suppression within the appropriate range of sensitive parameters. This study effectively extends the theoretical criteria for synchronous behavior on complex structures and is expected to provide ideas for vibration suppression strategies of multi-driving sources acting on multi-groups of elastic structures.

Collective behaviors of globally coupled fractional oscillators driven by different fluctuating potentials

The synchronization, stability and stochastic resonance (SR) behaviors of a globally coupled fractional oscillators driven by different fluctuating potentials are investigated. The statistical synchronicity between oscillators’ displacements is derived by reasonably grouping variables and using the moment method. Based on this, the output amplitude gain (OAG) and the necessary and sufficient condition for the system stability are obtained. Then we verified the theoretical results through numerical simulation, and mainly characterize the effects of the number of oscillators N, coupling strength ɛ, fractional order α, and noise intensity σ2 on these collective dynamic behaviors. We found that different from the fractional order coupled system with identical noise, the coupling strength ɛ and system size N also play important roles in the evolution of the system. The larger the number of oscillators N or the coupling strength ɛ is, the larger the stability region is. The synchronization speed is mainly controlled by α and has the same shape as the fractional order kernel function. Increasing the coupling strength and number of oscillators can speed up oscillators synchronization. Furthermore, all oscillators show SR phenomenon. The changes of SR with parameters were analyzed from the perspective of the strength of system randomness. This indicated that we can control the SR of the coupled fractional dynamic model by properly adjusting the system parameters within a certain range.

Optimizing the configuration of grid-forming and grid-following control in doubly-fed wind farms considering machine-side dynamics

Abstract With the growing integration of renewable energy, particularly wind power, into power systems, the effectiveness of wind farms in supporting grid stability is increasingly critical. Traditional grid-following (GFL) controls often fall short in weak grids with low short-circuit ratios (SCR), leading to stability issues. In contrast, grid-forming (GFM) controls, which simulate synchronous machine behavior, offer enhanced grid support and stability. This study investigates the optimization of control strategies for doubly-fed induction generator (DFIG) wind farms by comparing four configurations—grid-following DFIGs, grid-side GFM DFIGs, machine-side GFM DFIGs, and doubly grid-forming DFIGs. Through MATLAB/Simulink simulations, the study finds that doubly grid-forming DFIGs provide superior stability and grid support under low SCR conditions. These findings inform optimal configuration practices for wind farms in high-renewable-energy environments.

Firing dynamics and coupling synchronization of memristive EMR-based Chaivlo neuron utilizing equivalent energy approach.

Firing dynamics and its energy property of neuron are crucial for exploring the mechanism of intricate information processing within the nervous system. However, the energy analysis of discrete neuron is significantly lacking in comparison to the vast literature and mature theory available on continuous neuron, thereby necessitating a focused effort in this underexplored realm. In this paper, we introduce a Chaivlo neuron map by employing a flux-controlled memristor to simulate electromagnetic radiation (EMR), and a detailed analysis of its firing dynamics is conducted based on an equivalent Hamiltonian energy approach. Our observations reveal that a range of energy-based firing behaviors, such as spike firing, coexistence firing, mixed-mode firing, and chaotic bursting firing, can be induced by EMR and injected current. To delve deeper into the synchronous firing dynamics, we establish a Chaivlo network by electrically coupling two memristive EMR-based Chaivlo neurons. Subsequently, we experimentally evaluate the synchronization behavior of this network by quantifying both the synchronization factor and the average difference of equivalent Hamiltonian energy. Our findings conclusively demonstrate that both EMR and coupling strength positively contribute to the network's synchronization ability.

Fate of vortex-synchronized state in oscillator networks with node defects.

We investigate synchronization behaviors of a Kuramoto oscillator network with a two-dimensional square-lattice configuration. We show that the oscillator network can reach a phase-locking vortex synchronized state in the long time limit starting from random initial oscillator phases sampled according to the von Mises distribution characterized by a zero mean and a finite concentration parameter. We further reveal that the stability of the vortex synchronized state is sensitive to the presence of local node defects, in contrast to the usual knowledge that oscillator networks should exhibit robustness against local perturbations. Moreover, we explore the behaviors of the vortex synchronized state in networks with an additional temporal white noise on the oscillator phases or a spatial noise due to randomly distributed oscillator frequencies. Interestingly, we find that the vortex synchronized state can become immune to local node defects when the variance of spatial noise is above a certain threshold, suggesting a beneficial role of usually unwanted spatial noise in protecting vortex-synchronized networks.

Experimental study on the synchronization mechanism and trigger characteristic density of vertical evacuation in crowds.

Due to simultaneous horizontal and vertical displacement during vertical evacuation, the consequences of stampede congestion accidents can be more severe. Generally, pedestrians trigger a synchronization mechanism at some point during the vertical evacuation process. This synchronization behavior helps prevent stampede congestion and improves evacuation efficiency. This paper designs a well-controlled single-file vertical evacuation experiment. After the experiment, the video footage is imported into the TRACKER system to extract the coordinates of pedestrian step movements, after which the experimental data undergo calculations and visual analysis. The research findings indicate the following: Firstly, when the crowd coordinates trigger the synchronization mechanism, this behavior remains stable as long as pedestrian speed and direction are unchanged; Secondly, the variation in footstep speed over time is not directly related to the footstep synchronization rate of the crowd; Lastly, this study calculated the characteristic density value most likely to trigger the synchronization mechanism during vertical evacuation. This research deepens our understanding of crowd dynamics, reveals the characteristics of pedestrian movement during vertical evacuation, and proposes evacuation guidance strategies based on these features.

Synchronous behavior in directed networks of heterogeneous piecewise linear oscillators

In this paper, we investigate the onset of synchronous behavior on strongly connected directed networks of heterogeneous piecewise linear (PWL) oscillators. Each oscillator is composed of a linear part, which is assumed to be identical for all oscillators, and a commutation function, which is different for each oscillator, creating in this way the heterogeneity in the whole network. Due to this heterogeneity, the oscillators do not achieve perfect or complete synchronization but instead, practical synchronization emerges. The stability properties of the practical synchronous solution in the network are analyzed using standard Lyapunov theory for systems with nonvanishing perturbations and the concept of disagreement vectors. Our results provide an ultimate bound on the norm of the disagreement vector and also a maximal bound on the synchronization error, such that a relationship between the disagreement bound and practical synchronization is established. Additionally, we provide numerical simulations to illustrate the theoretical results.

Synchronization of Chaotic Systems with Huygens-Like Coupling

One of the earliest reports on synchronization of inert systems dates back to the time of the Dutch scientist Christiaan Huygens, who discovered that a pair of pendulum clocks coupled through a wooden bar oscillate in harmony. A remarkable feature in Huygens’ experiment is that different synchronous behaviors may be observed by just changing a parameter in the coupling. Motivated by this, in this paper, we propose a novel synchronization scheme for chaotic oscillators, in which the design of the coupling is inspired in Huygens’ experiment. It is demonstrated that the coupled oscillators may exhibit not only complete synchronization, but also mixed synchronization—some states synchronize in anti-phase whereas other states synchronize in-phase—depending on a single parameter of the coupling. Additionally, the stability of the synchronous solution is investigated by using the master stability function approach and the largest transverse Lyapunov exponent. The Lorenz system is considered as particular application example, and the performance of the proposed synchronization scheme is illustrated with computer simulations and validated by means of experiments using electronic circuits.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant seven-dimensional (7D) neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum, and two-dimensional (2D) dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Prehatch Calls and Coordinated Birth in Turtles.

Hatching synchronisation is widespread in oviparous taxa. It has been demonstrated that many species use sounds to coordinate synchronous hatching, being widespread among archosaurs (birds and crocodilians). Recent studies have shown that some turtle species produce vocalisations from within the egg, but the role of this behaviour in synchronising hatch is untested. The small amount of information about sound production by turtle embryos, limited to a handful of closely related species, precludes any inferences based on differences in their ecology, reproductive behaviour and phylogenetic context. With the goal to investigate if coordinated synchronous behaviour is mediated by within-egg vocalisations in turtles, we recorded clutches from six different turtle species. The selected animals present different ecological and reproductive niches and belong to distinct phylogenetic lineages at the family level. We aimed to understand: (1) what is the phylogenetic distribution of within-egg vocal behaviour among turtles; (2) if asynchronous turtle species vocalise from within the egg; (3) if clutch size influences synchronous behaviour and (4) if within-egg turtle calls follow any phylogenetic signal. The new evidence provides light to the current knowledge about synchronous behaviour and within-egg calls, challenging previous hypothesis that within-egg sounds are accidentally produced as side-effects of other behaviours.

Two-dimensional viscous study of coupled nonlinear fluid resonances in two narrow gaps

The wave-induced hydrodynamics of coupled nonlinear piston-mode fluid resonances within two narrow gaps between three barges are numerically investigated using a two-dimensional viscous wave flume. This study aims to explore the time-dependent nonlinear interactions between fluid oscillations in the two gaps. The coupled synchronous dynamic behaviors of fluid oscillations during the transient evolution stage are first examined in terms of amplitude and frequency modulation. It is shown that phase dynamics, including phase slipping, trapping, and locking, play significant roles in establishing the coupled synchronous dynamic evolutions of fluid oscillations in the two gaps. The quasi-steady state of the amplitude- and phase-frequency responses of fluid oscillations within the gaps, along with the reflection and transmission waves in front of and behind the three-barge system, are further analyzed. This analysis clarifies the significance of viscous damping energy dissipation and radiation damping energy transfer involved in gap resonance problems. This clarification also explains the performance of fully nonlinear potential flow solvers in predicting fluid resonances in the two narrow gaps. Finally, the nonlinear dynamic features of fluid oscillations are examined. The effects of incident wave nonlinearity, i.e., wave steepness, on resonant frequencies, response amplitudes, energy dissipation, and reflection and transmission coefficients are investigated. Harmonic analysis via Fourier transformation reveals the contributions of first-, second-, and third-order harmonics to the overall response amplitudes. The physical insights gained from this study provide a deeper understanding of the coupled nonlinear dynamics of piston-mode fluid resonances in multiple narrow gaps.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.