Phase synchronization of chaotic oscillators

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique

High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, necessitating the pursuit of low-energy alternatives for a long time. Adaptive optimization techniques and machine learning methods provide promising solutions for adaptive control of cardiac wave propagation. In this paper, the control of spiral waves and turbulence, as well as 2D and 3D heterogeneity in memristive neural network by using adaptive magnetic flux (AMF) is investigated based on dynamic learning of synchronization - alternating direction method of multipliers (DLS-ADMM). The results show that AMF can achieve global electrical synchronization under multiple complex conditions. There is a trade-off between AMF accuracy and computational speed, lowering the resolution of AMF requires a higher flux of magnetic fields to achieve the network synchronization, resulting in an increase in average Hamiltonian energy, which implies greater energy consumption. The AMF method is more energy efficient than existing DC and AC methods, but it relies on adequate resolution. The ADMM constraints can enhance the synchronization robustness and energy efficiency of DLS techniques, albeit at the cost of increased the computational complexity. The adaptive elimination of spiral waves and turbulence using AMF presented in this paper may provide a novel approach for the low-energy defibrillation studies, and its practical application and performance enhancement deserve further research.

Predictive role of endogenous phase lags between target brain regions in dual-site transcranial alternating current stimulation

BACKGROUND

Dual-site transcranial alternating current stimulation (tACS) provides a promising tool for modulating interregional brain connectivity by entraining neural oscillations. However, prior studies have reported inconsistent effects on connectivity and behavioral outcomes. They often focused on individualized stimulation-frequency as a key entrainment factor, while typically not focusing on the role of endogenous phase lags. To address this gap, we explored the predictive value of endogenous phase lags in dual-site tACS to modulate interhemispheric connectivity during dichotic listening.

METHODS

Thirty healthy participants (16 females) completed a dichotic listening task while undergoing simultaneous electroencephalography and tACS, including four bitemporal verum conditions with varying phase lags (0°, 45°, 90°, and 180°), and a sham condition across five sessions. Each session involved 20 min of 40-Hz tACS at a 0.5 mA peak-to-baseline amplitude applied to the temporal regions, with phase lags differing across sessions. Endogenous phase lags between the auditory cortices were calculated to explain changes in the laterality index (LI) across stimulation conditions by defining optimal and disruptive stimulation conditions for each participant.

RESULTS

Consistent with our hypothesis, our personalized analysis based on the calculated endogenous phase lags showed a significantly lower LI during the closest (optimal) stimulation condition compared to both the sham and farthest (disruptive) conditions. Conversely, the farthest stimulation condition did not statistically increase the LI compared to sham.

CONCLUSIONS

These findings highlight the importance of incorporating endogenous phase dynamics into dual-site tACS protocols, paving the way for more consistent and individualized neuromodulatory interventions.

Extreme events in two coupled chaotic oscillators

Since 1970, the Rössler system has remained as a considerably simpler and minimal-dimensional chaos serving system. Unveiling the dynamics of a system of two coupled chaotic oscillators that lead to the emergence of extreme events in the system is an engrossing and crucial scientific research area. Our present study focuses on the emergence of extreme events in a system of diffusively and bidirectionally two coupled Rössler oscillators and unraveling the mechanism behind the genesis of extreme events. We find the appearance of extreme events in three different observables: average velocity, synchronization error, and one transverse directional variable to the synchronization manifold. The emergence of extreme events in average velocity variables happens due to the occasional in-phase synchronization. The on-off intermittency plays a crucial role in the genesis of extreme events in the synchronization error dynamics and in the transverse directional variable to the synchronization manifold. The bubble transition of the chaotic attractor due to the on-off intermittency is illustrated for the transverse directional variable. We use generalized extreme value theory to study the statistics of extremes. The extreme events data sets concerning the average velocity variable follow a generalized extreme value distribution. The inter-event intervals of the extreme events in the average velocity variable spread well exponentially. The upshot of the interplay between the coupling strength and the frequency mismatch between the oscillators in the genesis of extreme events in the coupled system is depicted numerically.

Measure synchronization transition and its critical behavior in coupled camphor rotors

Building upon prior experimental research on measure synchronization (MS) in two coupled camphor rotors [Jain et al., Phys. Rev. E 108, 024217 (2023)], this paper presents an extensive theoretical study on MS transitions in both two and three coupled camphor rotors. For modelling, each camphor rotor is represented by a point particle that is confined to move along a unit circle around their centers, the camphor rotors are coupled with each other through interaction terms described by the repulsive Yukawa potential in between any two point particles. We find that by increasing the coupling intensity in between the camphor rotors, above certain critical coupling intensities, the camphor rotors achieve MS, including partial MS (PMS) and complete MS (CMS). The energy characteristics of MS in the camphor rotors are discussed. The results show that both phase locking and frequency locking are achieved at MS transitions. In addition, through Poincaré cross section analysis, we reveal the dynamic mechanism of various MS transitions.

Experimental evidence of community switching en route to global synchronization

Complex networks are made up of communities wherein dynamics occur. Here, a community is defined as an ensemble of identical Wien-bridge oscillators that are nearly synchronized. We explore experimental evidence of community switching, i.e., a subset of oscillators in a community synchronizing their frequencies to those of the other community. We demonstrate this phenomenon by varying the network connectivity and interaction strength. Employing Wien-bridge oscillators, we study experimentally, as well as in LTspice simulations, the phenomenon where an oscillator synchronizes its frequency to that of its neighboring community en route to global synchronization. Our results are in agreement with numerical simulations of the Kuramoto model reported by Kato and Kori in their novel work involving community switching [Phys. Rev. E 107, 014210 (2023)2470-004510.1103/PhysRevE.107.014210]. Due to the presence of noise, hysteresis is also observed in our experiment but not in LTspice simulations. Lastly, the onset of global synchrony for different topologies is studied.

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.72s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Embodied hyperscanning for studying social interaction: A scoping review of simultaneous brain and body measurements

We systematically investigated the application of embodied hyperscanning methodologies in social neuroscience research. Hyperscanning enables the simultaneous recording of neurophysiological and physiological signals from multiple participants. We highlight the trend toward integrating Mobile Brain/Body Imaging (MoBI) within the 4E research framework, which emphasizes the interconnectedness of brain, body, and environment. Our analysis revealed a geographic concentration of studies in the Global North, calling for global collaboration and transcultural research to balance the field. The predominant use of Magneto/Electroencephalogram (M/EEG) in these studies suggests a traditional brain-centric perspective in social neuroscience. Future research directions should focus on integrating diverse techniques to capture the dynamic interplay between brain and body functions in real-world contexts. Our review also finds a preference for tasks involving natural settings. Nevertheless, the analysis in hyperscanning studies is often limited to physiological signal synchrony between participants. This suggests a need for more holistic and complex approaches that combine inter-corporeal synchrony with intra-individual measures. We believe that the future of the neuroscience of relationships lies in embracing the complexity of cognition, integrating diverse methods and theories to enrich our grasp of human social behavior in its natural contexts.

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.

Trying to forget alcohol: Brain mechanisms underlying memory suppression in young binge drinkers

People are able to voluntarily suppress unwanted thoughts or memories, a phenomenon known as suppression-induced forgetting or memory suppression. Despite harmful alcohol use, such as binge drinking, has been linked to impaired inhibitory control (IC) and augmented alcohol-cue reactivity, no study to date has assessed memory inhibition abilities towards alcohol-related cues in binge drinkers (BDs). Thus, the present preregistered study aimed to evaluate the behavioral and neurofunctional mechanisms associated with memory inhibition, specifically those related to the suppression of alcohol-related memories, in young BDs. For this purpose, electroencephalographic activity was recorded in eighty-two college students aged between 18 and 24 years old from the University of Minho (50% females; 40 non/low-drinkers [N/LDS] and 42 BDs) while they performed the Think/No-Think Alcohol task. Brain functional connectivity (FC) was calculated using the phase locking value and, subsequently, a dynamic seed-based analysis was conducted to explore the FC patterns between IC and memory networks. Comparatively to N/LDs, BDs exhibited decreased alpha-band FC between the anterior cingulate cortex and the left fusiform gyrus during attempts to suppress non-alcohol memories, accompanied by unsuccessful forgetting of those memories. Conversely, BDs displayed augmented gamma-band FC between the IC network and memory regions -i.e., hippocampus, parahippocampus and fusiform gyrus- during suppression of alcohol-related memories. Inhibitory abnormalities in BDs may lead to hypoconnectivity between IC and memory networks and deficient suppression of non-alcohol-related memories. However, while suppressing highly salient and reward-predicting stimuli, such as alcohol-related memories, BDs display a hyperconnectivity pattern between IC and memory networks, likely due to their augmented attention towards intrusive alcoholic memories and the attempts to compensate for potential underlying IC deficits. These findings hold important implications for alcohol research and treatment, as they open up new avenues for reducing alcohol use by shifting the focus to empowering suppression/control over alcohol-related memories. CLINICAL TRIAL REGISTRATION: [http://www.ClinicalTrials.gov], identifier [NCT05237414].

A thirty-year time series analyses identifies coherence between oscillations in Anthrax outbreaks and El Niño in Karnataka, India

Anthrax is an economically important zoonotic disease affecting both livestock and humans. The disease is caused by a spore forming bacterium, Bacillus anthracis, and is considered endemic to the state of Karnataka, India. It is critical to quantify the role of climatic factors in determining the temporal pattern of anthrax outbreaks, so that reliable forecasting models can be developed. These models will aid in establishing public health surveillance and guide strategic vaccination programs, which will reduce the economic loss to farmers, and prevent the spill-over of anthrax from livestock to humans. In this study, correlation and coherence between time series of anthrax outbreaks in livestock (1987-2016) and meteorological variables and Sea Surface Temperature anomalies (SST) were identified using a combination of cross-correlation analyses, spectral analyses (wavelets and empirical mode decomposition) and further quantified using a Bayesian time series regression model accounting for temporal autocorrelation. Monthly numbers of anthrax outbreaks were positively associated with a lagged effect of rainfall and wet day frequency. Long-term periodicity in anthrax outbreaks (approximately 6-8 years) was coherent with the periodicity in SST anomalies and outbreak numbers increased with decrease in SST anomalies. These findings will be useful in planning long-term anthrax prevention and control strategies in Karnataka state of India.

Enhancing spectral analysis in nonlinear dynamics with pseudoeigenfunctions from continuous spectra

The analysis of complex behavior in empirical data poses significant challenges in various scientific and engineering disciplines. Dynamic Mode Decomposition (DMD) is a widely used method to reveal the spectral features of nonlinear dynamical systems without prior knowledge. However, because of its infinite dimensions, analyzing the continuous spectrum resulting from chaos and noise is problematic. We propose a clustering-based method to analyze dynamics represented by pseudoeigenfunctions associated with continuous spectra. This paper describes data-driven algorithms for comparing pseudoeigenfunctions using subspaces. We used the recently proposed Residual Dynamic Mode Decomposition (ResDMD) to approximate spectral properties from the data. To validate the effectiveness of our method, we analyzed 1D signal data affected by thermal noise and 2D-time series of coupled chaotic systems exhibiting generalized synchronization. The results reveal dynamic patterns previously obscured by conventional DMD analyses and provide insights into coupled chaos's complexities.

Higher emotional synchronization is modulated by relationship quality in romantic relationships and not in close friendships

Emotions are fundamental to social interaction and deeply intertwined with interpersonal dynamics, especially in romantic relationships. Although the neural basis of interaction processes in romance has been widely explored, the underlying emotions and the connection between relationship quality and neural synchronization remain less understood. Our study employed EEG hyperscanning during a non-interactive video-watching paradigm to compare the emotional coordination between romantic couples and close friends. Couples showed significantly greater behavioral and prefrontal alpha synchronization than friends. Notably, couples with low relationship quality required heightened neural synchronization to maintain robust behavioral synchronization. Further support vector machine analysis underscores the crucial role of prefrontal activity in differentiating couples from friends. In summary, our research addresses gaps concerning how intrinsic emotions linked to relationship quality influence neural and behavioral synchronization by investigating a natural non-interactive context, thereby advancing our understanding of the neural mechanisms underlying emotional coordination in romantic relationships.

Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data

This paper presents the results of a study of the characteristics of phase synchronization between electrocardiography(ECG) and electroencephalography (EEG) signals during night sleep. Polysomnographic recordings of eight generally healthy subjects and eight patients with obstructive sleep apnea syndrome were selected as experimental data. A feature of this study was the introduction of an instantaneous phase for EEG and ECG signals using a continuous wavelet transform at the heart rate frequency using the concept of time scale synchronization, which eliminated the emergence of asynchronous areas of behavior associated with the "leaving" of the fundamental frequency of the cardiovascular system. Instantaneous phase differences were examined for various pairs of EEG and ECG signals during night sleep, and it was shown that in all cases the phase difference exhibited intermittency. Laminar areas of behavior are intervals of phase synchronization, i.e., phase capture. Turbulent intervals are phase jumps of 2π. Statistical studies of the observed intermittent behavior were carried out, namely, distributions of the duration of laminar sections of behavior were estimated. For all pairs of channels, the duration of laminar phases obeyed an exponential law. Based on the analysis of the movement of the phase trajectory on a rotating plane at the moment of detection of the turbulent phase, it was established that in this case the eyelet intermittency was observed. There was no connection between the statistical characteristics of laminar phase distributions for intermittent behavior and the characteristics of night breathing disorders (apnea syndrome). It was found that changes in statistical characteristics in the phase synchronization of EEG and ECG signals were correlated with blood pressure at the time of signal recording in the subjects, which is an interesting effect that requires further research.

Realization of logic gates in bi-directionally coupled nonlinear oscillators

Implementation of logic gates has been investigated in nonlinear dynamical systems from various perspectives over the years. Specifically, logic gates have been implemented in both single nonlinear systems and coupled nonlinear oscillators. The majority of the works in the literature have been done on the evolution of single oscillators into OR/AND or NOR/NAND logic gates. In the present study, we demonstrate the design of logic gates in bi-directionally coupled double-well Duffing oscillators by applying two logic inputs to the drive system alone along with a fixed bias. The nonlinear system, comprising both bi-directional components, exhibits varied logic behaviors within an optimal range of coupling strength. Both attractive and repulsive couplings yield similar and complementary logic behaviors in the first and second oscillators. These couplings play a major role in exhibiting fundamental and universal logic gates in simple nonlinear systems. Under a positive bias, both the first and second oscillators demonstrate OR logic gate for the attractive coupling, while exhibiting OR and NOR logic gates, respectively, for the repulsive coupling. Conversely, under a negative bias, both the first and second oscillators display AND logic gate for the attractive coupling, and AND and NAND logical outputs for the repulsive coupling. Furthermore, we confirm the robustness of the bi-directional oscillators against moderate noise in maintaining the desired logical outputs.

VPA-induced autism impairs memory ability through disturbing neural oscillatory patterns in offspring rats

Autism spectrum disorder (ASD) is a general neurodevelopmental disease characterized by unusual social communication and rigid, repetitive behavior patterns. The purpose of this study was to investigate the effects of ASD on the alteration of neural oscillatory patterns and synaptic plasticity, which commonly supported a wide range of basic and higher memory activities. Accordingly, a prenatal valproic acid (VPA) exposure rat model was established for studying autism. The behavioral experiments showed that the social orientation declined and the memory ability was significantly impaired in VPA rats, which was closely associated with the synaptic plasticity deficits. Neural oscillation is the rhythmic neuron-activity, and the pathological characteristics and neurological changes in autism may be peeped at the neural oscillatory analysis. Interestingly, neural oscillatory analysis showed that prenatal VPA exposure reduced the low-frequency power but increased high-frequency gamma (HG) power in the hippocampus CA1 area. Meanwhile, the coherence and synchronization between CA3 and CA1 were abnormally increased in the VPA group, especially in theta and HG rhythms. Furthermore, the cross-frequency coupling strength of theta-LG in the CA1 and CA3 → CA1 pathway was significantly attenuated, but the theta-HG coupling strength was increased. Additionally, prenatal VPA exposure inhibited the expression of SYP and NR2B but enhanced the expression of PSD-95 along with decreased synaptic plasticity. The neural oscillatory patterns in VPA-induced offspring were disturbed with the intensity and direction of neural information flow disordered, which are consistent with the changes in synaptic plasticity, suggesting that the decline in synaptic plasticity is the underlying mechanism.

Statistical Interdependence between Daily Precipitation and Extreme Daily Temperature in Regions of Mexico and Colombia

We study the statistical interdependence between daily precipitation and daily extreme temperature for regions of Mexico (14 climatic stations, period 1960-2020) and Colombia (7 climatic stations, period 1973-2020) using linear (cross-correlation and coherence) and nonlinear (global phase synchronization index, mutual information, and cross-sample entropy) synchronization metrics. The information shared between these variables is relevant and exhibits changes when comparing regions with different climatic conditions. We show that precipitation and temperature records from La Mojana are characterized by high persistence, while data from Mexico City exhibit lower persistence (less memory). We find that the information exchange and the level of coupling between the precipitation and temperature are higher for the case of the La Mojana region (Colombia) compared to Mexico City (Mexico), revealing that regions where seasonal changes are almost null and with low temperature gradients (less local variability) tend to display higher synchrony compared to regions where seasonal changes are very pronounced. The interdependence characterization between precipitation and temperature represents a robust option to characterize and analyze the collective dynamics of the system, applicable in climate change studies, as well as in changes not easily identifiable in future scenarios.

Sleep-stage dependence and co-existence of cardio-respiratory coordination and phase synchronization

Interactions between the cardiac and respiratory systems play a pivotal role in physiological functioning. Nonetheless, the intricacies of cardio-respiratory couplings, such as cardio-respiratory phase synchronization (CRPS) and cardio-respiratory coordination (CRC), remain elusive, and an automated algorithm for CRC detection is lacking. This paper introduces an automated CRC detection algorithm, which allowed us to conduct a comprehensive comparison of CRPS and CRC during sleep for the first time using an extensive database. We found that CRPS is more sensitive to sleep-stage transitions, and intriguingly, there is a negative correlation between the degree of CRPS and CRC when fluctuations in breathing frequency are high. This comparative analysis holds promise in assisting researchers in gaining deeper insights into the mechanics of and distinctions between these two physiological phenomena. Additionally, the automated algorithms we devised have the potential to offer valuable insights into the clinical applications of CRC and CRPS.

Dynamics of a diffusive model for cancer stem cells with time delay in microRNA-differentiated cancer cell interactions and radiotherapy effects

Understand the dynamics of cancer stem cells (CSCs), prevent the non-recurrence of cancers and develop therapeutic strategies to destroy both cancer cells and CSCs remain a challenge topic. In this paper, we study both analytically and numerically the dynamics of CSCs under radiotherapy effects. The dynamical model takes into account the diffusion of cells, the de-differentiation (or plasticity) mechanism of differentiated cancer cells (DCs) and the time delay on the interaction between microRNAs molecules (microRNAs) with DCs. The stability of the model system is studied by using a Hopf bifurcation analysis. We mainly investigate on the critical time delay τ c , that represents the time for DCs to transform into CSCs after the interaction of microRNAs with DCs. Using the system parameters, we calculate the value of τ c for prostate, lung and breast cancers. To confirm the analytical predictions, the numerical simulations are performed and show the formation of spatiotemporal circular patterns. Such patterns have been found as promising diagnostic and therapeutic value in management of cancer and various diseases. The radiotherapy is applied in the particular case of prostate model. We calculate the optimum dose of radiation and determine the probability of avoiding local cancer recurrence after radiotherapy treatment. We find numerically a complete eradication of patterns when the radiotherapy is applied before a time t < τ c . This scenario induces microRNAs to act as suppressors as experimentally observed in prostate cancer. The results obtained in this paper will provide a better concept for the clinicians and oncologists to understand the complex dynamics of CSCs and to design more efficacious therapeutic strategies to prevent the non-recurrence of cancers.

Collective dynamics of coupled Lorenz oscillators near the Hopf boundary: Intermittency and chimera states

We study collective dynamics of networks of mutually coupled identical Lorenz oscillators near a subcritical Hopf bifurcation. Such systems exhibit induced multistable behavior with interesting spatiotemporal dynamics including synchronization, desynchronization, and chimera states. For analysis, we first consider a ring topology with nearest-neighbor coupling and find that the system may exhibit intermittent behavior due to the complex basin structures and dynamical frustration, where temporal dynamics of the oscillators in the ensemble switches between different attractors. Consequently, different oscillators may show a dynamics that is intermittently synchronized (or desynchronized), giving rise to intermittent chimera states. The behavior of the intermittent laminar phases is characterized by the characteristic time spent in the synchronization manifold, which decays as a power law. Such intermittent dynamics is quite general and is also observed in an ensemble of a large number of oscillators arranged in variety of network topologies including nonlocal, scale-free, random, and small-world networks.

Transition to period-3 synchronized state in coupled gauss maps

We study coupled Gauss maps in one dimension with nearest-neighbor interactions. We observe transitions from spatiotemporal chaos to period-3 states in a coarse-grained sense and synchronized period-3 states. Synchronized fixed points are frequently observed in one dimension. However, synchronized periodic states are rare. The obvious reason is that it is very easy to create defects in one dimension. We characterize all transitions using the following order parameter. Let x∗ be the fixed point of the map. The values above (below) x∗ are classified as +1 (-1) spins. We expect all sites to return to the same band after three time steps for a coarse-grained periodic or three-period state. We define the flip rate F(t) as the fraction of sites i such that si(3t-3)≠si(t). It is zero in the coarse-grained periodic state. This state may or may not be synchronized. We observe three different transitions. (a) If different sites reach different bands, the transition is in the directed-percolation universality class. (b) If all sites reach the same band, we find an Ising-type transition. (c) A synchronized period-3 state where a new exponent is observed. We also study the finite-size scaling at critical points. The exponents obtained indicate that the synchronized period-3 transition is in a new universality class.

Explosive synchronization in a turbulent reactive flow system

The occurrence of abrupt dynamical transitions in the macroscopic state of a system has received growing attention. We present experimental evidence for abrupt transition via explosive synchronization in a real-world complex system, namely, a turbulent reactive flow system. In contrast to the paradigmatic continuous transition to a synchronized state from an initially desynchronized state, the system exhibits a discontinuous synchronization transition with a hysteresis. We consider the fluctuating heat release rate from the turbulent flames at each spatial location as locally coupled oscillators that are coupled to the global acoustic field in the confined system. We analyze the synchronization between these two subsystems during the transition to a state of oscillatory instability and discover that explosive synchronization occurs at the onset of oscillatory instability. Further, we explore the underlying mechanism of interaction between the subsystems and construct a mathematical model of the same.

Macroscopic behavior of populations of quadratic integrate-and-fire neurons subject to non-Gaussian white noise

We study macroscopic behavior of populations of quadratic integrate-and-fire neurons subject to non-Gaussian noises; we argue that these noises must be α-stable whenever they are delta-correlated (white). For the case of additive-in-voltage noise, we derive the governing equation of the dynamics of the characteristic function of the membrane voltage distribution and construct a linear-in-noise perturbation theory. Specifically for the recurrent network with global synaptic coupling, we theoretically calculate the observables: population-mean membrane voltage and firing rate. The theoretical results are underpinned by the results of numerical simulation for homogeneous and heterogeneous populations. The possibility of the generalization of the pseudocumulant approach to the case of a fractional α is examined for both irrational and fractional rational α. This examination seemingly suggests the pseudocumulant approach or its modifications to be employable only for the integer values of α=1 (Cauchy noise) and 2 (Gaussian noise) within the physically meaningful range (0;2]. Remarkably, the analysis for fractional α indirectly revealed that, for the Gaussian noise, the minimal asymptotically rigorous model reduction must involve three pseudocumulants and the two-pseudocumulant model reduction is an artificial approximation. This explains a surprising gain of accuracy for the three-pseudocumulant models as compared to the two-pseudocumulant ones reported in the literature.

Synchronization in STDP-driven memristive neural networks with time-varying topology

Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.

Topological data analysis of the synchronization of a network of Rössler chaotic electronic oscillators

Synchronization study allows a better understanding of the exchange of information among systems. In this work, we study experimental data recorded from a set of Rössler-like chaotic electronic oscillators arranged in a complex network, where the interactions between the oscillators are given in terms of a connectivity matrix, and their intensity is controlled by a global coupling parameter. We use the zero and one persistent homology groups to characterize the point clouds obtained from the signals recorded in pairs of oscillators. We show that the normalized persistent entropy (NPE) allows us to characterize the effective coupling between pairs of oscillators because it tends to increase with the coupling strength and to decrease with the distance between the oscillators. We also observed that pairs of oscillators that have similar degrees and are nearest neighbors tend to have higher NPE values than pairs with different degrees. However, large variability is found in the NPE values. Comparing the NPE behavior with that of the phase-locking value (PLV, commonly used to evaluate the synchronization of phase oscillators), we find that for large enough coupling, PLV only displays a monotonic increase, while NPE shows a richer behavior that captures variations in the behavior of the oscillators. This is due to the fact that PLV only captures coupling-induced phase changes, while NPE also captures amplitude changes. Moreover, when we consider the same network but with Kuramoto phase oscillators, we also find that NPE captures the transition to synchronization (as it increases with the coupling strength), and it also decreases with the distance between the oscillators. Therefore, we propose NPE as a data analysis technique to try to differentiate pairs of oscillators that have strong effective coupling because they are first or near neighbors, from those that have weaker coupling because they are distant neighbors.

Lactation physiokinetics-using advances in technology for a fresh perspective on human milk transfer

Introduction

Though the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood.

Methods

Combining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors to characterize human milk flow from the breast to the infant's stomach to identify points of regulation. This exploratory study used this framework to characterize Maternal/Infant Lactation physioKinetics (MILK) utilizing a Biosensor ARray (BAR) as a data collection method.

Results

Participants tolerated the MILKBAR well during data collection. Changes in breast turgor and temperature were significant and related to the volume of milk transferred from the breast. The total milk volume transferred was evaluated in relation to contact force, oral pressure, and jaw movement. Contact force was correlated with milk flow. Oral pressure appears to be a redundant measure and reflective of jaw movements.

Discussion

Nipple and breast turgor, jaw movement, and swallowing were associated with the mass of milk transferred to the infant's stomach. More investigation is needed to better quantify the mass of milk transferred in relation to each variable and understand how each variable regulates milk transfer.

Contrarian role of phase and phase velocity coupling in synchrony of second-order phase oscillators

Positive phase coupling plays an attractive role in inducing in-phase synchrony in an ensemble of phase oscillators. Positive coupling involving both amplitude and phase continues to be attractive, leading to complete synchrony in identical oscillators (limit cycle or chaotic) or phase coherence in oscillators with heterogeneity of parameters. In contrast, purely positive phase velocity coupling may originate a repulsive effect on pendulumlike oscillators (with rotational motion) to bring them into a state of diametrically opposite phases or a splay state. Negative phase velocity coupling is necessary to induce synchrony or coherence in the general sense. The contrarian roles of phase coupling and phase velocity coupling on the synchrony of networks of second-order phase oscillators have been explored here. We explain our proposition using networks of two model systems, a second-order phase oscillator representing the pendulum or the superconducting Josephson junction dynamics, and a voltage-controlled oscillations in neurons model. Numerical as well as semianalytical approaches are used to confirm our results.

Synchronization of multiple rigid body systems: A survey

The multi-agent system has been a hot topic in the past few decades owing to its lower cost, higher robustness, and higher flexibility. As a particular multi-agent system, the multiple rigid body system received a growing interest for its wide applications in transportation, aerospace, and ocean exploration. Due to the non-Euclidean configuration space of attitudes and the inherent nonlinearity of the dynamics of rigid body systems, synchronization of multiple rigid body systems is quite challenging. This paper aims to present an overview of the recent progress in synchronization of multiple rigid body systems from the view of two fundamental problems. The first problem focuses on attitude synchronization, while the second one focuses on cooperative motion control in that rotation and translation dynamics are coupled. Finally, a summary and future directions are given in the conclusion.

Electrophysiological connectivity of logical deduction: Early cortical MEG study

Complex human reasoning involves minimal abilities to extract conclusions implied in the available information. These abilities are considered "deductive" because they exemplify certain abstract relations among propositions or probabilities called deductive arguments. However, the electrophysiological dynamics which supports such complex cognitive processes has not been addressed yet. In this work we consider typically deductive logico-probabilistically valid inferences and aim to verify or refute their electrophysiological functional connectivity differences from invalid inferences with the same content (same relational variables, same stimuli, same relevant and salient features). We recorded the brain electrophysiological activity of 20 participants (age = 20.35 ± 3.23) by means of an MEG system during two consecutive reasoning tasks: a search task (invalid condition) without any specific deductive rules to follow, and a logically valid deductive task (valid condition) with explicit deductive rules as instructions. We calculated the functional connectivity (FC) for each condition and conducted a seed-based analysis in a set of cortical regions of interest. Finally, we used a cluster-based permutation test to compare the differences between logically valid and invalid conditions in terms of FC. As a first novel result we found higher FC for valid condition in beta band between regions of interest and left prefrontal, temporal, parietal, and cingulate structures. FC analysis allows a second novel result which is the definition of a propositional network with operculo-cingular, parietal and medial nodes, specifically including disputed medial deductive "core" areas. The experiment discloses measurable cortical processes which do not depend on content but on truth-functional propositional operators. These experimental novelties may contribute to understand the cortical bases of deductive processes.

Boosting of stable synchronization in coupled non-identical counter-rotating chaotic systems

Achieving synchronization in coupled non-identical chaotic systems has been a difficult endeavor, and improving the stability of synchronization in such systems poses additional challenges. This research work addresses these challenges by identifying stable synchronization in coupled non-identical chaotic systems and enhancing its stability. The study explores chaotic attractors that arise from various system parameters to provide generalized results. Furthermore, the impact of the transient uncoupling factor on improving synchronization stability in coupled non-identical counter-rotating chaotic oscillators is discussed. By investigating these aspects, the research aims to contribute to the understanding and advancement of synchronization in coupled non-identical chaotic systems.

Generalized synchronization mediated by a flat coupling between structurally nonequivalent chaotic systems

Synchronization of chaotic systems is usually investigated for structurally equivalent systems typically coupled through linear diffusive functions. Here, we focus on a particular type of coupling borrowed from a nonlinear control theory and based on the optimal placement of a sensor-a device measuring the chosen variable-and an actuator-a device applying the actuating (control) signal to a variable's derivative-in the response system, leading to the so-called flat control law. We aim to investigate the dynamics produced by a response system that is flat coupled to a drive system and to determine the degree of generalized synchronization between them using statistical and topological arguments. The general use of a flat control law for getting generalized synchronization is discussed.

Phase transitions on a multiplex of swarmalators

Dynamics of bidirectionally coupled swarmalators subject to attractive and repulsive couplings is analyzed. The probability of two elements in different layers being connected strongly depends on a defined vision range r_{c} which appears to lead both layers in different patterns while varying its values. Particularly, the interlayer static sync π has been found and its stability is proven. First-order transitions are observed when the repulsive coupling strength σ_{r} is very small for a fixed r_{c} and, moreover, in the absence of the repulsive coupling, they also appear for sufficiently large values of r_{c}. For σ_{r}=0 and for sufficiently small values of r_{c}, both layers achieve a second-order transition in a surprising two steps that are characterized by the drop of the energy of the internal phases while increasing the value of the interlayer attractive coupling σ_{a} and later a smooth jump, up to high energy value where synchronization is achieved. During these transitions, the internal phases present rotating waves with counterclockwise and later clockwise directions until synchronization, as σ_{a} increases. These results are supported by simulations and animations added as supplemental materials.

Network of muscle fibers activation facilitates inter-muscular coordination, adapts to fatigue and reflects muscle function

Fundamental movement patterns require continuous skeletal muscle coordination, where muscle fibers with different timing of activation synchronize their dynamics across muscles with distinct functions. It is unknown how muscle fibers integrate as a network to generate and fine tune movements. We investigate how distinct muscle fiber types synchronize across arm and chest muscles, and respond to fatigue during maximal push-up exercise. We uncover that a complex inter-muscular network of muscle fiber cross-frequency interactions underlies push-up movements. The network exhibits hierarchical organization (sub-networks/modules) with specific links strength stratification profile, reflecting distinct functions of muscles involved in push-up movements. We find network reorganization with fatigue where network modules follow distinct phase-space trajectories reflecting their functional role and adaptation to fatigue. Consistent with earlier observations for squat movements under same protocol, our findings point to general principles of inter-muscular coordination for fundamental movements, and open a new area of research, Network Physiology of Exercise.

Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.

EEG-based functional connectivity for tactile roughness discrimination

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

In-phase and mixed-phase measure synchronization of camphor rotors

The numerical, analytical, and experimental analyses are presented for synchronizing two rotors under the Yukawa interaction. We report that the rotors exhibit in-phase and mixed-phase measure synchronizations for a pair of coupled rotors. Here, the analytical condition for synchronization is derived, tested numerically, and confirmed experimentally using coupled camphor infused rotors as a test bed. Moreover, the concept of measure synchronization is discussed. We report that, in conservative systems, not only the critical coupling parameter but initial conditions also play an essential role for estimating the measure synchronization region.

Universal dynamics of spatiotemporal entrainment with phase symmetry

We study the entrainment of a localized pattern to an external signal via its coupling to zero modes associated with broken symmetries. We show that when the pattern breaks internal symmetries, entrainment is governed by a multiple degrees-of-freedom dynamical system that has a universal structure, defined by the symmetry group and its breaking. We derive explicitly the universal locking dynamics for entrainment of patterns breaking internal phase symmetry, and calculate the locking domains and the stability and bifurcations of entrainment of complex Ginzburg-Landau solitons by an external pulse.

Phase slips in coupled oscillator systems

Phase slips are a typical dynamical behavior in coupled oscillator systems: the route to phase synchrony is characterized by intervals of constant phase difference interrupted by abrupt changes in the phase difference. Qualitatively similar to stick-slip phenomena, analysis of phase slip has mainly relied on identifying remnants of saddle-nodes or "ghosts." We study sets of phase oscillators and by examining the dynamics in detail, offer a more precise, quantitative description of the phenomenon. Phase shifts and phase sticks, namely, the temporary locking of phases required for phase slips, occur at stationary points of phase velocities. In networks of coupled phase oscillators, we show that phase slips between pairs of individual oscillators do not occur simultaneously, in general. We consider additional systems that show phase synchrony: one where saddle-node ghosts are absent, one where the coupling is similarity dependent, and two cases of coupled chaotic oscillators.

Effectiveness of phase synchronization in chaotic food chain model with refugia and Allee effects during seasonal fluctuations

Seasonal effects powerfully shape the population dynamics with periodic climate changes because species naturally adjust their dynamics with seasonal variations. In response to these effects, sometimes population dynamics exhibit synchrony or generate chaos. However, synchronized dynamics enhance species' persistence in naturally unstable environments; thus, it is imperative to identify parameters that alter the dynamics of an ecosystem and bring it into synchrony. This study examines how ecological parameters enable species to adapt their dynamics to seasonal changes and achieve phase synchrony within ecosystems. For this, we incorporate seasonal effects as a periodic sinusoidal function into a tri-trophic food chain system where two crucial bio-controlling parameters, Allee and refugia effects, are already present. First, it is shown that the seasonal effects disrupt the limit cycle and bring chaos to the system. Further, we perform rigorous mathematical analysis to perform the dynamical and analytical properties of the nonautonomous version of the system. These properties include sensitive dependence on initial condition (SDIC), sensitivity analysis, bifurcation results, the positivity and boundedness of the solution, permanence, ultimate boundedness, and extinction scenarios of species. The SDIC characterizes the presence of chaotic oscillations in the system. Sensitivity analysis determines the parameters that significantly affect the outcome of numerical simulations. The bifurcation study concerning seasonal parameters shows a higher dependency of species on the frequency of seasonal changes than the severity of the season. The bifurcation study also examines the bio-controlling parameters and reveals various dynamic states within the system, such as fold, transcritical branch points, and Hopf points. Moreover, the mathematical analysis of our seasonally perturbed system reveals the periodic coexistence of all species and a globally attractive solution under certain parametric constraints. Finally, we examine the role of essential parameters that contribute to phase synchrony. For this, we numerically investigate the defining role of the coupling dimension coefficient, bio-controlling parameters, and other parameters associated with seasonality. This study infers that species can tune their dynamics to seasonal effects with low seasonal frequency, whereas the species' tolerance for the severity of seasonal effects is relatively high. The research also sheds light on the correlation between the degree of phase synchrony, prey biomass levels, and the severity of seasonal forcing. This study offers valuable insights into the dynamics of ecosystems affected by seasonal perturbations, with implications for conservation and management strategies.

Study of Generalized Chaotic Synchronization Method Incorporating Error-Feedback Coefficients

In this paper, taking the generalized synchronization problem of discrete chaotic systems as a starting point, a generalized synchronization method incorporating error-feedback coefficients into the controller based on the generalized chaos synchronization theory and stability theorem for nonlinear systems is proposed. Two discrete chaotic systems with different dimensions are constructed in this paper, the dynamics of the proposed systems are analyzed, and finally, the phase diagrams, Lyapunov exponent diagrams, and bifurcation diagrams of these are shown and described. The experimental results show that the design of the adaptive generalized synchronization system is achievable in cases in which the error-feedback coefficient satisfies certain conditions. Finally, a chaotic hiding image encryption transmission system based on a generalized synchronization approach is proposed, in which an error-feedback coefficient is introduced into the controller.

Chimera states in multiplex networks: Chameleon-like across-layer synchronization

Different across-layer synchronization types of chimera states in multilayer networks have been discovered recently. We investigate possible relations between them, for example, if the onset of some synchronization type implies the onset of some other type. For this purpose, we use a two-layer network with multiplex inter-layer coupling. Each layer consists of a ring of non-locally coupled phase oscillators. While oscillators in each layer are identical, the layers are made non-identical by introducing mismatches in the oscillators' mean frequencies and phase lag parameters of the intra-layer coupling. We use different metrics to quantify the degree of various across-layer synchronization types. These include phase-locking between individual interacting oscillators, amplitude and phase synchronization between the order parameters of each layer, generalized synchronization between the driver and response layer, and the alignment of the incoherent oscillator groups' position on the two rings. For positive phase lag parameter mismatches, we get a cascaded onset of synchronization upon a gradual increase of the inter-layer coupling strength. For example, the two order parameters show phase synchronization before any of the interacting oscillator pairs does. For negative mismatches, most synchronization types have their onset in a narrow range of the coupling strength. Weaker couplings can destabilize chimera states in the response layer toward an almost fully coherent or fully incoherent motion. Finally, in the absence of a phase lag mismatch, sufficient coupling turns the response dynamics into a replica of the driver dynamics with the phases of all oscillators shifted by a constant lag.

Cluster Synchronization Control for Discrete-Time Complex Dynamical Networks: When Data Transmission Meets Constrained Bit Rate

In this article, the cluster synchronization control problem is studied for discrete-time complex dynamical networks when the data transmission is subject to constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth, and the effects from the constrained bit rate onto the control performance of the cluster synchronization are evaluated. A sufficient condition is first proposed to guarantee the ultimate boundedness of the error dynamics of the cluster synchronization, and then, a bit-rate condition is established to reveal the fundamental relationship between the bit rate and the certain performance index of the cluster synchronization. Subsequently, two optimization problems are formulated to design the desired synchronization controllers with aim to achieve two distinct synchronization performance indices. The codesign issue for the bit-rate allocation protocol and the controller gains is further discussed to reduce the conservatism by locally minimizing a certain asymptotic upper bound of the synchronization error dynamics. Finally, three illustrative simulation examples are utilized to validate the feasibility and effectiveness of the developed synchronization control scheme.

Complexity in Geophysical Time Series of Strain/Fracture at Laboratory and Large Dam Scales: Review

One of the interesting directions of complexity theory is the investigation of the synchronization of mechanical behavior of large-scale systems by weak forcing, which is one of manifestations of nonlinearity/complexity of a system. The effect of periodic weak mechanical or electromagnetic forcing leading to synchronization was studied on the laboratory load-spring system as well as on a big dam's strain data. Due to synchronization, the phase space structure of the forced system strongly depends on the weak forcing intensity-determinism show itself in the recurrence of definite states of the forced system. The nonlinear dynamics of tilts/strains/seismicity near grand dams reflect both the complexity of the mentioned time series, connected with the natural agents (regional and local geodynamics), which were presented even before dam erection, as well as the effects of the water level (WL) variation in the reservoir, which is a quasi-periodic forcing superimposed on the natural geodynamic background. Both these effects are documented by the almost half-century of observations at the large Enguri Dam. The obtained data on the dynamics of strain/seismicity near a large dam can be used for the assessment of the possible risks, connected with the abrupt change of routine dynamics of construction.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

OBJECTIVE

Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities.

METHODS

EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested.

RESULTS

The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated.

CONCLUSIONS

Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data.

SIGNIFICANCE

Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.

Inferring synchronizability of networked heterogeneous oscillators with machine learning

In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogeneous oscillators on a complex network in order to improve the synchronization performance. Whereas methods have been proposed to address this question in the literature, the methods are all based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here, we show that this question can be addressed by the model-free technique of a feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

Two Operational Modes of Cardio-Respiratory Coupling Revealed by Pulse-Respiration Quotient

Due to the fact that respiratory breath-to-breath and cardiac intervals between two successive R peaks (BBI and RRI, respectively) are not temporally concurrent, in a previous paper, we proposed a method to calculate both the integer and non-integer parts of the pulse respiration quotient (PRQ = BBI/RRI = PRQ_int + b1 + b2), b1 and b2 being parts of the border RRIs for each BBI. In this work, we study the correlations between BBI and PRQ, as well as those between BBI and mean RRI within each BBI (mRRI), on a group of twenty subjects in four conditions: in supine and standing positions, in combination with spontaneous and slow breathing. Results show that the BBI vs. PRQ correlations are positive; whereas the breathing regime had little or no effect on the linear regression slopes, body posture did. Two types of scatter plots were obtained with the BBI vs. mRRI correlations: one showed points aggregated around the concurrent PRQ_int lines, while the other showed randomly distributed points. Five out of six of the proposed aggregation measures confirmed the existence of these two cardio-respiratory coupling regimes. We also used b1 to study the positions of R pulses relative to the respiration onsets and showed that they were more synchronous with sympathetic activation. Overall, this method should be used in different pathological states.

Seeking Repeating Anthropogenic Seismic Sources: Implications for Seismic Velocity Monitoring at Fault Zones

Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high-frequency (>1 Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high-frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.

Age-dependent cardiorespiratory directional coupling in wake-resting state

Objective.Cooperation in the cardiorespiratory system helps maintain internal stability. Various types of system interactions have been investigated; however, the characteristics of the interactions have mostly been studied using data collected in well-defined physiological states, such as sleep. Furthermore, most analyses provided general information about the interaction, making it difficult to quantify how the systems influenced one another.Approach.Cardiorespiratory directional coupling was investigated in different age groups (20 young and 19 elderly subjects) in a wake-resting state. The directionality index (DI) was calculated using instantaneous phases from the heartbeat interval and respiratory signal to provide information about the strength and direction of interaction between the systems. Statistical analysis was performed between the groups on the DI and independent measures of directionality (nc_r: influence from cardiac system to respiratory system, and nc_c: influence from the respiratory system to the cardiac system).Main results.The values of DI were -0.52 and -0.17 in the young and elderly groups, respectively (p< 0.001). Furthermore, the values of nc_rand nc_cwere found to be significantly different between the groups (p< 0.001), respectively.Significance.Changes in both directions between the systems influence different aspects of cardiorespiratory coupling between the groups. This observation could be linked to different levels of autonomic modulation associated with ageing. Our approach could aid in quantitatively tracking and comprehending how systems interact in response to physiological and environmental changes. It could also be used to understand how abnormal interaction characteristics influence physiological system dysfunctions and disorders.

Dominant Attractor in Coupled Non-Identical Chaotic Systems

The dynamical interplay of coupled non-identical chaotic oscillators gives rise to diverse scenarios. The incoherent dynamics of these oscillators lead to the structural impairment of attractors in phase space. This paper investigates the couplings of Lorenz-Rössler, Lorenz-HR, and Rössler-HR to identify the dominant attractor. By dominant attractor, we mean the attractor that is less changed by coupling. For comparison and similarity detection, a cost function based on the return map of the coupled systems is used. The possible effects of frequency and amplitude differences between the systems on the results are also examined. Finally, the inherent chaotic characteristic of systems is compared by computing the largest Lyapunov exponent. The results suggest that in each coupling case, the attractor with the greater largest Lyapunov exponent is dominant.

Preparing to sing: respiratory patterns underlying motor readiness for song

Evidence for motor preparation and planning comes from neural activity preceding neural commands to activate the effectors; such preparatory activity is observed in pallial areas controlling learned motor behaviors. Vocal learning in songbirds is an example of a learned, sequential motor behavior that is a respiratory motor act and where there is evidence for neuromuscular planning. Respiration is the foundation of vocalization, elucidating the neural control of song motor planning requires studying respiratory antecedents of song initiation. Despite the importance of respiration in song production, few studies have investigated respiratory antecedents of impending vocalizations. Therefore, we investigated respiratory patterns in male zebra finches (Taeniopygia guttata) and Bengalese finches (Lonchura striata domestica) prior to, during, and following song bouts. In both species, compared with quiet respiration, song respiratory patterns were generated with higher amplitude, faster tempo, and ∼70% of the respiratory cycle is in the expiratory phase. In female-directed and isolation song, both species show a change in the respiratory tempo and the proportion of time spent inhaling prior to song. Following song, only zebra finches show systematic changes in respiratory patterns; they spend a greater proportion of the respiratory cycle in the expiratory phase for 1 s after song, which is likely due to hyperventilation during song. Accelerated respiratory rhythms before song may reflect the motor preparation for the upcoming song production; species differences in preparatory motor activity could be related to the degree to which motor planning is required; finally, song termination may be dictated by respiratory demands.NEW & NOTEWORTHY Motor planning for vocal production in birdsong manifests as an adaptation of respiratory characteristics prior to song. The songbird's respiratory system anticipates the upcoming song production by accelerating the respiratory tempo and increasing the proportion of time spent inhaling.

The impact of inner-coupling and time delay on synchronization: From single-layer network to hypernetwork

Though synchronization of complex dynamical systems has been widely studied in the past few decades, few studies pay attention to the impact of network parameters on synchronization in hypernetworks. In this paper, we focus on a specific hypernetwork model consisting of coupled Rössler oscillators and investigate the impact of inner-coupling and time delay on the synchronized region (SR). For the sake of simplicity, the inner-coupling matrix is chosen from three typical forms, which result in classical bounded, unbounded, and empty SR in a single-layer network, respectively. The impact of inner-couplings or time delays on unbounded SR is the most interesting one among the three types of SR. Once the SR of one subnetwork is unbounded, the SR of the whole hypernetwork is also unbounded with a different inner-coupling matrix. In a hypernetwork with unbounded SR, the time delays change not only the size but also the type of SR. In a hypernetwork with bounded or empty SR, the time delays have almost no effect on the type of SR.