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We have measured signals induced in 5 Corbino electrodes by spontaneous oscillations of 2D surface electrons on liquid helium at $\sim$0.3\,K, with a perpendicular magnetic field and microwave radiation. Analysis using multi-scale, time-resolved, methods yields results consistent with magnetoplasmons modulated by slow surface gravity waves, with the latter requiring consideration of the 3rd dimension. Calculation of phase differences and phase coherences between signals from differently-positioned pairs of electrodes enables reconstruction of the electron dynamics on the helium surface., Comment: 6 pages,4 figures

Standard dynamical systems theory is centred around the coordinate-invariant asymptotic-time properties of autonomous systems. We identify three limitations of this approach. Firstly, we discuss how the traditional approach cannot take into account the time-varying nature of dynamics of open systems. Secondly, we show that models with explicit dependence on time exhibit stark dynamic phenomena, even when they cannot be defined for infinite time. We see a bifurcation occurring in nonautonomous finite-time systems that cannot be identified by classical methods for infinite-time autonomous systems. Thirdly, even when a time-varying model can be extended to infinite time, the classical infinite-time approach is likely to miss dynamical phenomena that are more readily understood within the framework of finite-time dynamics. We conclude the potentially crucial importance of a nonautonomous finite-time approach to real-world, open systems.

Nonautonomous driving of an oscillator has been shown to enlarge the Arnold tongue in parameter space, but little is known about the analogous effect for a network of oscillators. To test the hypothesis that deterministic nonautonomous perturbation is a good candidate for stabilising complex dynamics, we consider a network of identical phase oscillators driven by an oscillator with a slowly time-varying frequency. We investigate both the short- and long-term stability of the synchronous solutions of this nonautonomous system. For attractive couplings we show that the region of stability grows as the amplitude of the frequency modulation is increased, through the birth of an intermittent synchronisation regime. For repulsive couplings, we propose a control strategy to stabilise the dynamics by altering very slightly the network topology. We also show how, without changing the topology, time-variability in the driving frequency can itself stabilise the dynamics. As a by-product of the analysis, we observe chimera-like states. We conclude that time-variability-induced stability phenomena are also present in networks, reinforcing the idea that this is quite realistic scenario for living systems to use in maintaining their functioning in the face of ongoing perturbations.

Synchronisation and stability under periodic oscillatory driving are well-understood, but little is known about the effects of aperiodic driving, despite its abundance in nature. Here, we consider oscillators subject to driving with slowly varying frequency, and investigate both short-term and long-term stability properties. For a phase oscillator, we find that, counter-intuitively, such variation is guaranteed to enlarge the Arnold tongue in parameter space. Using analytical and numerical methods that provide information on time-variable dynamical properties, we find that the growth of the Arnold tongue is specifically due to the growth of a region of intermittent synchronisation where trajectories alternate between short-term stability and short-term neutral stability, giving rise to stability on average. We also present examples of higher-dimensional nonlinear oscillators where a similar stabilisation phenomenon is numerically observed. Our findings help support the case that in general, deterministic non-autonomous perturbation is a very good candidate for stabilising complex dynamics., Comment: to be published in Phys. Rev. E

The dynamical systems found in Nature are rarely isolated. Instead they interact and influence each other. The coupling functions that connect them contain detailed information about the functional mechanisms underlying the interactions and prescribe the physical rule specifying how an interaction occurs. Here, we aim to present a coherent and comprehensive review encompassing the rapid progress made recently in the analysis, understanding and applications of coupling functions. The basic concepts and characteristics of coupling functions are presented through demonstrative examples of different domains, revealing the mechanisms and emphasizing their multivariate nature. The theory of coupling functions is discussed through gradually increasing complexity from strong and weak interactions to globally-coupled systems and networks. A variety of methods that have been developed for the detection and reconstruction of coupling functions from measured data is described. These methods are based on different statistical techniques for dynamical inference. Stemming from physics, such methods are being applied in diverse areas of science and technology, including chemistry, biology, physiology, neuroscience, social sciences, mechanics and secure communications. This breadth of application illustrates the universality of coupling functions for studying the interaction mechanisms of coupled dynamical systems., Comment: Rev. Mod. Phys. 89, 045001 (2017)

Chronotaxic systems represent deterministic nonautonomous oscillatory systems which are capable of resisting continuous external perturbations while having a complex time-dependent dynamics. Until their recent introduction in \emph{Phys. Rev. Lett.} \textbf{111}, 024101 (2013) chronotaxic systems had often been treated as stochastic, inappropriately, and the deterministic component had been ignored. While the previous work addressed the case of the decoupled amplitude and phase dynamics, in this paper we develop a generalized theory of chronotaxic systems where such decoupling is not required. The theory presented is based on the concept of a time-dependent point attractor or a driven steady state and on the contraction theory of dynamical systems. This simplifies the analysis of chronotaxic systems and makes possible the identification of chronotaxic systems with time-varying parameters. All types of chronotaxic dynamics are classified and their properties are discussed using the nonautonomous Poincar\'e oscillator as an example. We demonstrate that these types differ in their transient dynamics towards a driven steady state and according to their response to external perturbations. Various possible realizations of chronotaxic systems are discussed, including systems with temporal chronotaxicity and interacting chronotaxic systems., Comment: 9 pages, 8 figures

Having reviewed the aspects of the linear and synchrosqueezed time-frequency representations (TFRs) needed for their understanding and correct use in Part I of this review, we now consider three more subtle issues that are nonetheless of crucial importance for effective application of these methods. (i) What effect do the window/wavelet parameters have on the resultant TFR, and how can they most appropriately be chosen? (ii) What are the errors inherent in the two reconstruction methods (direct and ridge) and which of them is the better? (iii) What are the advantages and drawbacks associated with synchrosqueezing? To answer these questions, we perform a detailed numerical and theoretical study of the TFRs under consideration. We consider the relevant estimates in the presence of the complications that arise in practical applications including interference between components, amplitude modulation, frequency modulation, and noise. Taken together, the results provide an in-depth understanding of the issues in question., Comment: 39 pages, 28 figures

Time-frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed variants (SWFT, SWT), provide powerful analysis tools. However, there are many important issues related to the practical use of TFRs that need to be clarified. Here we present a thorough review of these TFRs, summarizing all theoretical, practical and numerical aspects of their use, reconsidering some conventions and introducing new concepts and procedures. The purposes of this work are: (i) to provide a consistent overview of the computation, properties, and use of the (S)WFT/(S)WT methods; (ii) to establish general standards related to their use, both theoretical and practical; and (iii) to provide clean and optimized algorithms and MatLab codes, appropriate for any window or wavelet., Comment: 45 pages, 14 figures

The extraction of oscillatory components and their properties from different time-frequency representations, such as windowed Fourier transform and wavelet transform, is an important topic in signal processing. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download., Comment: 13 pages, 7 figures, plus 4 supplementary figures

In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments. To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial., Comment: Matlab codes can be found on http://py-biomedical.lancaster.ac.uk/

Large networks of coupled oscillators appear in many branches of science, so that the kinds of phenomena they exhibit are not only of intrinsic interest but also of very wide importance. In 1975, Kuramoto proposed an analytically tractable model to describe such systems, which has since been successfully applied in many contexts and remains a subject of intensive research. Some related problems, however, remain unclarified for decades, such as the properties of the oscillator glass state discovered by Daido in 1992. Here we present a detailed analysis of a very general form of the Kuramoto model. In particular, we find the conditions when it can exhibit glassy behavior, which represents a kind of synchronous disorder in the present case. Furthermore, we discover a new and intriguing phenomenon that we refer to as {\it superrelaxation} where, for a class of parameter distributions, the oscillators feel no interaction at all during relaxation to incoherence, a phenomenon reminiscent of superfluidity or superconductivity. Our findings offer the possibility of creating glassy states and observing superrelaxation in real systems, thus paving the way to a cascade of applications and further research in the field., Comment: 9 pages, 5 figures

We investigate interacting phase oscillators whose mean field is at a different frequency from the mean or mode of their natural frequencies. The associated asymmetries lead to a macroscopic travelling wave. We show that the mean ensemble frequency of such systems differs from their entrainment frequency. In some scenarios these frequencies take values that, counter-intuitively, lie beyond the limits of the natural frequencies. The results indicate that a clear distinction should be drawn between the two variables describing the macroscopic dynamics of cooperative systems. This has important implications for real systems where a non-trivial distribution of parameters is common., Comment: to be published in Physical Review E

Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. (Phys. Rev. Lett. 109 024101, 2012) introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time- evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically-generated data, data from an analog electronic circuit, and cardio-respiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks., Comment: 15 pages, 13 figures, published in Physical Review E

We consider the Kuramoto model of an ensemble of interacting oscillators allowing for an arbitrary distribution of frequencies and coupling strengths. We define a family of traveling wave states as stationary in a rotating frame, and derive general equations for their parameters. We suggest empirical stability conditions which, for the case of incoherence, become exact. In addition to making new theoretical predictions, we show that many earlier results follow naturally from our general framework. The results are applicable in scientific contexts ranging from physics to biology., Comment: 5 pages, 1 figure

We introduce a new adaptive decomposition tool, which we refer to as Nonlinear Mode Decomposition (NMD). It decomposes a given signal into a set of physically meaningful oscillations for any waveform, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques - which together with the adaptive choice of their parameters make it extremely noise-robust - and surrogate data tests, used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals, and demonstrate its qualitative and quantitative superiority over the other existing approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loeve expansion and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary MATLAB codes for running NMD are freely available at http://www.physics.lancs.ac.uk/research/nbmphysics/diats/nmd/., Comment: 38 pages, 13 figures

A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips, and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions., Comment: 5 pages, 3 figures, accepted for Physical Review Letters

We introduce a generalization of the Kuramoto model by explicit consideration of time-dependent parameters. The oscillators' natural frequencies and/or couplings are supposed to be influenced by external, time-dependant fields, with constant or randomly-distributed strengths. As a result, the dynamics of an external system is being imposed on top of the autonomous one, a scenario that cannot be treated adequately by previous (adiabatic) approaches. We now propose an analysis which describes faithfully the overall dynamics of the system., Comment: http://link.aps.org/doi/10.1103/PhysRevE.86.046212

We discuss open problems related to the stochastic modeling of cardiac function. The work is based on an experimental investigation of the dynamics of heart rate variability (HRV) in the absence of respiratory perturbations. We consider first the cardiac control system on short time scales via an analysis of HRV within the framework of a random walk approach. Our experiments show that HRV on timescales of less than a minute takes the form of free diffusion, close to Brownian motion, which can be described as a non-stationary process with stationary increments. Secondly, we consider the inverse problem of modeling the state of the control system so as to reproduce the experimentally observed HRV statistics of. We discuss some simple toy models and identify open problems for the modelling of heart dynamics., Comment: Published in J. Stat. Mech. (2009) P01016 as the paper of the conference "Unsolved Problems of Noise in Physics, Biology and Technology", Lyon, 2008

A system of two coupled ensembles of phase oscillators can follow different routes to inter-ensemble synchronization. Following a short report of our preliminary results [Phys. Rev. E. {\bf 78}, 025201(R) (2008)], we present a more detailed study of the effects of coupling, noise and phase asymmetries in coupled phase oscillator ensembles. We identify five distinct synchronization regions, and new routes to synchronization that are characteristic of the coupling asymmetry. We show that noise asymmetry induces effects similar to that of coupling asymmetry when the latter is absent. We also find that phase asymmetry controls the probability of occurrence of particular routes to synchronization. Our results suggest that asymmetry plays a crucial role in controlling synchronization within and between oscillator ensembles, and hence that its consideration is vital for modeling real life problems.

We study oscillation death (OD) in a well-known coupled-oscillator system that has been used to model cardiovascular phenomena. We derive exact analytic conditions that allow the prediction of OD through the two known bifurcation routes, in the same model, and for different numbers of coupled oscillators. Our exact analytic results enable us to generalize OD as a multiparameter-sensitive phenomenon. It can be induced, not only by changes in couplings, but also by changes in the oscillator frequencies or amplitudes. We observe synchronization transitions as a function of coupling and confirm the robustness of the phenomena in the presence of noise. Numerical and analogue simulations are in good agreement with the theory., Comment: 6 pages, 6 figures, to be published in Europhysics Letters (Feb 2009)

There is growing evidence in favour of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in particular, that $\theta$ (3.5-7.5 Hz) and $\delta$ ($0.5<$3.5 Hz) oscillations facilitate information coding. The $\theta$ and $\delta$ rhythms are shown to be involved in various sleep stages, and during an{\ae}sthesia, and they undergo changes with the depth of an{\ae}sthesia. We introduce a thalamocortical model of interacting neuronal ensembles to describe phase relationships between $\theta$ and $\delta$ oscillations, especially during deep and light an{\ae}sthesia. Asymmetric and long range interactions among the thalamocortical neuronal oscillators are taken into account. The model results are compared with the experimental observations of Musizza et al. {\it J. Physiol. (London)} 2007 580:315-326. The $\delta$ and $\theta$ activities are found to be separately generated and are governed by the thalamus and cortex respectively. Changes in the degree of intra--ensemble and inter--ensemble synchrony imply that the neuronal ensembles inhibit information coding during deep an{\ae}sthesia and facilitate it during light an{\ae}sthesia., Comment: 18 pages, 3 figures

We report that asymmetrically interacting ensembles of oscillators (AIEOs) follow novel routes to synchrony. These routes seem to be a characteristic feature of coupling asymmetry. We show that they are unaffected by white noise except that the entrainment frequencies are shifted. The probability of occurrence of the routes is determined by phase asymmetry. The identification of these phenomena offers new insight into synchrony between oscillator ensembles and suggest new ways in which it may be controlled., Comment: 4 pages, 6 figures

We consider two coupled phase oscillators in the presence of proportional ("common") and independent white noises. The global synchronization properties of the system are analytically studied via the Fokker-Planck equation. When the "effective coupling" is big compared to the common noises, the former favors and the latter hinder synchronization. On the contrary, when the coupling is small compared to the proportional noises, we find that the latter induce synchronization, optimally when their intensities are big and in the n:m synchronization ratio. Furthermore, in such case a small value of the coupling is better for synchronization. Finally, we show that synchronization, which is a global property, must not be studied via local stability such as with Lyapunov exponents., Comment: 5 pages, 4 figures, revtex. Added an example that shows that synchronization, which is a global property, must not be studied by means of a local stability analysis such as via Lyapunov exponents. Other changes of medium importance were made as well

We consider a rotator whose equation of motion for the angle $\theta$ consists of the zeroth and first Fourier modes. Numerical analysis based on the trailing of saddle-node bifurcations is used to locate the n:1 Arnold tongues where synchronization occurs. Several of them are wide enough for high-order synchronization to be seen in passive observations. By sweeping the system parameters within a certain range, we find that the stronger the dependence of $\dot\theta$ on $\theta$, the wider the regions of synchronization. Use of a synchronization index reveals a vast number of very narrow n:m Arnold tongues. A competition phenomenon among the tongues is observed, in that they "push" and "squeeze" one another: as some tongues widen, others narrow. Two mechanisms for transitions between different n:m synchronization states are considered: slow variation of the driving frequency, and the influence of low-frequency noise on the rotator., Comment: 10 pages, 7 figures, revtex. Many major changes compared to previous version. As appears in Physical Review E, except that the last editions by the publishers do not appear here

We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modelling from blood pressure time-series data. This new method is applicable to a broad range of stochastic dynamical models, and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time-series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using surrogate data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed., Comment: 11 pages, 8 figures, 2 tables

A new technique is introduced to reconstruct a nonlinear stochastic model of the cardiorespiratory interaction. Its inferential framework uses a set of polynomial basis functions representing the nonlinear force governing the system oscillations. The strength and direction of coupling, and the noise intensity are simultaneously inferred from a univariate blood pressure signal, monitored in a clinical environment. The technique does not require extensive global optimization and it is applicable to a wide range of complex dynamical systems subject to noise., Comment: 5 pages, 4 figures, 1 table. accepted for publication in Phys. Rev. Lett. 2005