Synchronization Patterns Research Articles

250-Year reconstruction of pollarding events reveals sharp management changes in Iberian ash woodlands

Key messageTree-ring growth of pollarded narrow-leaved ash trees in Central Spain reveals traditional management cessation in 1970 and property-specific management patterns.Tree pollarding was a dominant management strategy of European forests for centuries creating open agroforestry landscapes with important cultural and environmental values. This traditional practice has been widely abandoned in last decades with a subsequent impact in terms of biodiversity and cultural loss. Central Spain hosts the largest and best-preserved area of pollarded narrow-leaved ash (Fraxinus angustifolia Vahl.) woodlands in Europe. The main aim of this research is to obtain rigorous historical records of pollarding frequency to get adequate information for traditional ash management. We used dendrochronological techniques to evaluate temporal changes of pollarding frequency and rotation length. We analysed the stand level synchrony and the effect of land property on pollarding activity from 322 trees growing in eight pollard stands in Central Spain. Pollarding events were unequivocally identified at tree level by a characteristic change in growth pattern. We identified 2426 tree-level pruning events with the first event dated in 1777. Historical pruning recurrence ranged between 5 and 10 years with higher pollarding frequency on private lands. Pruning events within each site were synchronous, suggesting the existence of a rotational schema within each stand. Pruning frequency decreased drastically in the 1970s matching with the depopulation of rural areas and the general abandonment of traditional practices. Pollarding practices have recovered in recent decades although with lower intensity and lacking the synchronic historical patterns. Providing technical and economic support to make this traditional activity profitably would have strong environmental revenue due the multiple ecosystem services provided by pollarded ashes.

Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

AbstractA deeper understanding of the intricate relationship between the two components of the Asian summer monsoon (ASM)—the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM)—is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network‐based approach, we identify two dominant synchronization pathways between ISM and EASM—a southern mode between the Arabian Sea and southeastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July—and their associated large‐scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden–Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher‐frequency mode of the BSISO is likely to support the shifting between the modes of ISM–EASM connection.

Controlling patterns of synchrony.

Controlling patterns of synchrony.

Saturated and asymmetric saturated impulsive control synchronization of coupled delayed inertial neural networks with time-varying delays

This paper considers control systems with impulses that are saturated and asymmetrically saturated which are used to examine the synchronization of inertial neural networks (INNs) with time-varying delay and coupling delays. Under the theoretical discussions, mixed delays, such as transmission delay and coupling delay are presented for inertial neural networks. The addressed INNs are transformed into first order differential equations utilizing variable transformation on INNs and then certain adequate conditions are derived for the exponential synchronization of the addressed model by substituting saturation nonlinearity with a dead-zone function. In addition, an asymmetric saturated impulsive control approach is given to realize the exponential synchronization of addressed INNs in the leader-following synchronization pattern. Finally, simulation results are used to validate the theoretical research findings.

On the Expressiveness of Mixed Choice Sessions

Session types provide a flexible programming style for structuring interaction, and are used to guarantee a safe and consistent composition of distributed processes. Traditional session types include only one-directional input (external) and output (internal) guarded choices. This prevents the session-processes to explore the full expressive power of the pi-calculus where the mixed choices are proved more expressive than the (non-mixed) guarded choices. To account this issue, recently Casal, Mordido, and Vasconcelos proposed the binary session types with mixed choices (CMV+). This paper carries a surprising, unfortunate result on CMV+: in spite of an inclusion of unrestricted channels with mixed choice, CMV+'s mixed choice is rather separate and not mixed. We prove this negative result using two methodologies (using either the leader election problem or a synchronisation pattern as distinguishing feature), showing that there exists no good encoding from the pi-calculus into CMV+, preserving distribution. We then close their open problem on the encoding from CMV+ into CMV (without mixed choice), proving its soundness and thereby that the encoding is good up to coupled similarity.

The coregulatory effects of emotionally focused therapy

AbstractThis study examined the heart rate synchrony between a single couple in emotionally focused therapy (EFT) as a method of measuring coregulatory effects over the course of treatment. A clinician who is an EFT certified therapist, supervisor, and trainer administered all eight sessions in a private practice setting. Surrogate synchrony analysis assessed heart rate synchrony between partners, indicating that significant heart rate synchrony was more likely over time. Significant heart rate synchrony was more likely during sessions that focused on clients’ sharing attachment injuries, bonds, and longings with their partner compared to sessions more focused on cognitive processing of the relationship. Additionally, there were delays in heart rate synchrony that occurred between the couple, which may be indicative of the emotional processing speed of the empathic response of each partner. Understanding the physiological synchrony patterns between dyads in treatment could clarify the potential of coregulation in the therapeutic process.

Feedback Pinning Control of Successive Lag Synchronization on a Dynamical Network.

In nature and human society, successive lag synchronization (SLS) is an important synchronization phenomenon. Compared with other synchronization patterns, the control theory of SLS is very lacking. To this end, we first introduce a complex dynamical network model with distributed delayed couplings, and design both the linear feedback pinning control and adaptive feedback pinning control to push SLS to the desired trajectories. Second, we obtain a series of sufficient conditions to achieve SLS to a desired trajectory with global stability. What is more, the control flow of SLS is given to show how to pick the pinned nodes accurately and set the feedback gains as well. Finally, since time-varying delay is common, we extend the constant time delay in SLS to be time varying. We find that the proposed pinning control schemes are still feasible if the coupling terms are appropriately adjusted. The theoretical results are verified on a neural network and the coupled Chua's circuits.

Synchronization of Belousov-Zhabotinsky oscillators with electrochemical coupling in a spontaneous process.

A passive electrochemical coupling approach is proposed to induce spontaneous synchronization between chemical oscillators. The coupling exploits the potential difference between a catalyst redox couple in the Belousov-Zhabotinsky (BZ) reaction, without external feedback, to induce surface reactions that impact the kinetics of the bulk system. The effect of coupling in BZ oscillators under batch condition is characterized using phase synchronization measures. Although the frequency of the oscillators decreases nonlinearly over time, by a factor of 2 or more within 100 cycles, the coupling is strong enough to maintain synchronization. In such a highly drifting system, the Gibbs-Shannon entropy of the cyclic phase difference distribution can be used to quantify the coupling effect. We extend the Oregonator BZ model to account for the drifting natural frequencies in batch condition and for electrochemical coupling, and numerical simulations of the effect of acid concentration on synchronization patterns are in agreement with the experiments. Because of the passive nature of coupling, the proposed coupling scheme can open avenues for designing pattern recognition and neuromorphic computation systems using chemical reactions in a spontaneous process.

Spatiotemporal pattern of periodic rhythms in delayed Van der Pol oscillators for the CPG-based locomotion of snake-like robot

This is the further research on the delayed half-center oscillator (DHCO) neural system presented in our previous paper (Song and Xu in Nonlinear Dyn 108:2595–2609, 2022. https://doi.org/10.1007/s11071-022-07222-y). The DHCO is used to construct a CPG (central pattern generator) neural system to control locomotion of a snake-like robot with pitch-yaw connecting configuration. To this end, we firstly give an improved model of the VDP (Van der Pol) oscillator. Employing mutually coupled delay, a pair of VDP oscillator is connected to produce an half-center oscillator (HCO) module with time delay that is called as a DHCO (delayed HCO) model. Based on the analysis of the Hopf bifurcation, periodic rhythm and their spatiotemporal patterns of the DHCO are illustrated in the different regions of parameters. The DHCO presents periodic rhythms with synchronous and anti-synchronous patterns, which is to control joint actuators combined in snake-like robot with pitch-yaw connecting configuration. To realize a backward propulsive wave to promote snake-like robotic locomotion, based on the DHCOs, we construct a chain type of the CPG neural system combined with a new unidirectional delay in which phase difference can be regulated. Numerical simulations are illustrated that the CPG neural system can control snake-like robot to move with serpentine, rectilinear, and side-winding patterns in the forward and backward directions. The results show that the snake-like robot can be controlled in expected locomotion patterns for a region but not a fixed value of the controlling parameters. Further, the corresponding regions of the parameters are obtained by using theoretical dynamical analysis but not a trial-and-error method. The snake-like robot gets smooth and stable gait transition with parameter changing.

An increase of inhibition drives the developmental decorrelation of neural activity.

Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.

Patterns of synchronization in 2D networks of inhibitory neurons.

Neural firing in many inhibitory networks displays synchronous assembly or clustered firing, in which subsets of neurons fire synchronously, and these subsets may vary with different inputs to, or states of, the network. Most prior analytical and computational modeling of such networks has focused on 1D networks or 2D networks with symmetry (often circular symmetry). Here, we consider a 2D discrete network model on a general torus, where neurons are coupled to two or more nearest neighbors in three directions (horizontal, vertical, and diagonal), and allow different coupling strengths in all directions. Using phase model analysis, we establish conditions for the stability of different patterns of clustered firing behavior in the network. We then apply our results to study how variation of network connectivity and the presence of heterogeneous coupling strengths influence which patterns are stable. We confirm and supplement our results with numerical simulations of biophysical inhibitory neural network models. Our work shows that 2D networks may exhibit clustered firing behavior that cannot be predicted as a simple generalization of a 1D network, and that heterogeneity of coupling can be an important factor in determining which patterns are stable.

Multi-population phase oscillator networks with higher-order interactions

The classical Kuramoto model consists of finitely many pairwisely coupled oscillators on the circle. In many applications a simple pairwise coupling is not sufficient to describe real-world phenomena as higher-order (or group) interactions take place. Hence, we replace the classical coupling law with a very general coupling function involving higher-order terms. Furthermore, we allow for multiple populations of oscillators interacting with each other through a very general law. In our analysis, we focus on the characteristic system and the mean-field limit of this generalized class of Kuramoto models. While there are several works studying particular aspects of our program, we propose a general framework to work with all three aspects (higher-order, multi-population, and mean-field) simultaneously. In this article, we investigate dynamical properties within the framework of the characteristic system. We identify invariant subspaces of synchrony patterns and study their stability. It turns out that the so called all-synchronized state, which is one special synchrony pattern, is never asymptotically stable. However, under some conditions and with a suitable definition of stability, the all-synchronized state can be proven to be at least locally stable. In summary, our work provides a rigorous mathematical framework upon which the further study of multi-population higher-order coupled particle systems can be based.

Periventricular nodular heterotopia is coupled with the neocortex during resting and task states.

Periventricular nodular heterotopia (PVNH) is a well-defined developmental disorder characterized by failed neuronal migration, which forms ectopic neuronal nodules along the ventricular walls. Previous studies mainly focus on clinical symptoms caused by the PVNH tissue, such as seizures. However, little is known about whether and how neurons in the PVNH tissue functionally communicate with neurons in the neocortex. To probe this, we applied magnetoencephalography (MEG) and stereo-electroencephalography (sEEG) recordings to patients with PVNH during resting and task states. By estimating frequency-resolved phase coupling strength of the source-reconstructed neural activities, we found that the PVNH tissue was spontaneously coupled with the neocortex in the α-β frequency range, which was consistent with the synchronization pattern within the neocortical network. Furthermore, the coupling strength between PVNH and sensory areas effectively modulated the local neural activity in sensory areas. In both MEG and sEEG visual experiments, the PVNH tissue exhibited visual-evoked responses, with a similar pattern and latency as the ipsilateral visual cortex. These findings demonstrate that PVNH is functionally integrated into cognition-related cortical circuits, suggesting a co-development perspective of ectopic neurons after their migration failure.

Functional control of oscillator networks

Oscillatory activity is ubiquitous in natural and engineered network systems. The interaction scheme underlying interdependent oscillatory components governs the emergence of network-wide patterns of synchrony that regulate and enable complex functions. Yet, understanding, and ultimately harnessing, the structure-function relationship in oscillator networks remains an outstanding challenge of modern science. Here, we address this challenge by presenting a principled method to prescribe exact and robust functional configurations from local network interactions through optimal tuning of the oscillators’ parameters. To quantify the behavioral synchrony between coupled oscillators, we introduce the notion of functional pattern, which encodes the pairwise relationships between the oscillators’ phases. Our procedure is computationally efficient and provably correct, accounts for constrained interaction types, and allows to concurrently assign multiple desired functional patterns. Further, we derive algebraic and graph-theoretic conditions to guarantee the feasibility and stability of target functional patterns. These conditions provide an interpretable mapping between the structural constraints and their functional implications in oscillator networks. As a proof of concept, we apply the proposed method to replicate empirically recorded functional relationships from cortical oscillations in a human brain, and to redistribute the active power flow in different models of electrical grids.

Wavelet Analysis Reveals Phenology Mismatch between Leaf Phenology of Temperate Forest Plants and the Siberian Roe Deer Molting under Global Warming

Global warming is deeply influencing various ecological processes, especially regarding the phenological synchronization pattern between species, but more cases around the world are needed to reveal it. We report how the forest leaf phenology and ungulate molting respond differently to climate change, and investigate whether it will result in a potential phenology mismatch. Here, we explored how climate change might alter phenological synchronization between forest leaf phenology and Siberian roe deer (Capreolus pygargus) molting in northeast China based on a camera-trapping dataset of seven consecutive years, analyzing forest leaf phenology in combination with records of Siberian roe deer molting over the same period by means of wavelet analysis. We found that the start of the growing season of forest leaf phenology was advanced, while the end of the growing season was delayed, so that the length of the growing season was prolonged. Meanwhile, the start and the end of the molting of Siberian roe deer were both advanced in spring, but in autumn, the start of molting was delayed while the end of molting was advanced. The results of wavelet analysis also suggested the time lag of synchronization fluctuated slightly from year to year between forest leaf phenology and Siberian roe deer molting, with a potential phenology mismatch in spring, indicating the effect of global warming on SRD to forest leaf phenology. Overall, our study provides new insight into the synchronization between forest leaf phenology and ungulate molting, and demonstrates feasible approaches to data collection and analysis using camera-trapping data to explore global warming issues.

Fractional-Order Memristive Wilson Neuron Model: Dynamical Analysis and Synchronization Patterns

Fractional nonlinear systems have been considered in many fields due to their ability to bring memory-dependent properties into various systems. Therefore, using fractional derivatives to model real-world phenomena, such as neuronal dynamics, is of significant importance. This paper presents the fractional memristive Wilson neuron model and studies its dynamics as a single neuron. Furthermore, the collective behavior of neurons is researched when they are locally and diffusively coupled in a ring topology. It is found that the fractional-order neurons are bistable in some values of the fractional order. Additionally, complete synchronization, lag synchronization, phase synchronization, and sine-like synchronization patterns can be observed in the constructed network with different fractional orders.

The impact of cathodal tDCS on the GABAergic system in the epileptogenic zone: A multimodal imaging study.

ObjectivesWe aimed to investigate the antiepileptic effects of cathodal transcranial direct current stimulation (c-tDCS) and mechanisms of action based on its effects on the neurotransmitters responsible for the abnormal synchrony patterns seen in pharmacoresistant epilepsy. This is the first study to test the impact of neurostimulation on epileptiform interictal discharges (IEDs) and to measure brain metabolites in the epileptogenic zone (EZ) and control regions simultaneously in patients with pharmacoresistant epilepsy.MethodsThis is a hypothesis-driven pilot prospective single-blinded repeated measure design study in patients diagnosed with pharmacoresistant epilepsy of temporal lobe onset. We included seven patients who underwent two sessions of c-tDCS (sham followed by real). The real tDCS session was 20 min in duration and had a current intensity of 1.5 mA delivered via two surface electrodes that had dimensions of 3 × 4 cm. The cathode electrode was placed at FT7 in the center whereas the anode at Oz in the center. After each session, we performed electroencephalographic recording to count epileptiform IEDs over 30 min. We also performed magnetic resonance spectroscopy (MRS) to measure brain metabolite concentrations in the two areas of interest (EZ and occipital region), namely, gamma-aminobutyric acid (GABA), glutamate (Glx), and glutathione. We focused on a homogenous sample where the EZ and antiepileptic medications are shared among patients.ResultsReal tDCS decreased the number of epileptiform IEDs per min (from 9.46 ± 2.68 after sham tDCS to 5.37 ± 3.38 after real tDCS), p = 0.018, as compared to sham tDCS. GABA was decreased in the EZ after real c-tDCS stimulation as compared to sham tDCS (from 0.129 ± 0.019 to 0.096 ± 0.018, p = 0.02). The reduction in EZ GABA correlated with the reduction in the frequency of epileptiform IED per min (rho: 0.9, p = 0.003).ConclusionThese results provide a window into the antiepileptic mechanisms of action of tDCS, based on local and remote changes in GABA and neural oscillatory patterning responsible for the generation of interictal epileptiform discharges.

Three-dimensional dynamics and synchronization of two coupled fluid-conveying pipes with intermediate springs

A system of two coupled oscillating structures may experience dynamical synchronization under coupling effects. Dynamical synchronization refers to the process in which two vibrating structures under coupling effects may display similar dynamics and vibrational behaviors even though their important parameters are not equivalent. In this study, the three-dimensional dynamics and synchronization behaviors of two coupled fluid-conveying pipes connected with linear springs are investigated. In the analytical model, the geometric nonlinearities due to axial elongation of the two pipes and the extension of linear springs are considered. The flow velocities of the internal fluid of the two pipes may be steady or pulsatile. In the case of the flow velocities of the two pipes being steady, the lowest several natural frequencies and the post-buckling behavior of the two-pipe system are obtained by changing the flow velocities in the two pipes. When one pipe conveys pulsatile fluid and the other conveys steady fluid, the dynamical bifurcation and synchronization behaviors of the two-pipe system are analyzed, with several typical synchronization patterns being explored by means of time traces, phase portraits, power spectral density (PSD) diagrams, Poincaré maps, etc. It is shown that making use of synchronization characteristics of the two-pipe system may provide a possible way to control the vibrations of the two-pipe system and to achieve some special engineering objectives.

Synchrony in directed connectomes

Synchronisation plays a fundamental role in a variety of physiological functions, such as visual perception, cognitive function, sleep and arousal. The precise role of the interplay between local dynamics and directed cortical topology on the propensity for cortical structures to synchronise, however, remains poorly understood. Here, we study the impact that directed network topology has on the synchronisation properties of the brain by considering a range of species and parcellations, including the cortex of the cat and the Macaque monkey, as well as the nervous system of the C. elegans round worm. We deploy a Kuramoto phase model to simulate neural dynamics on the aforementioned connectomes, and investigate the extent to which network directionality influences distributed patterns of neural synchrony. In particular, we find that network directionality induces both slower synchronisation speeds and more robust phase locking in the presence of network delays. Moreover, in contrast to large-scale connectomes, we find that recently observed relations between resting state directionality patterns and network structure appear to break down for invertebrate networks such as the C. elegans connectome, thus suggesting that observed variations in directed network topology at different scales can significantly impact patterns of neural synchrony. Our results suggest that directionality plays a key role in shaping network dynamics and moreover that its exclusion risks simplifying neural activation dynamics in a potentially significant way.

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning.

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain "high-order functional connectivity (HOFC)" network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex "chronnectome" metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.