Approach to predictability via anticipated synchronization

Anticipated synchronization in human EEG data: Unidirectional causality with negative phase lag

Understanding the functional connectivity of the brain has become a major goal of neuroscience. In many situations the relative phase difference, together with coherence patterns, has been employed to infer the direction of the information flow. However, it has been recently shown in local field potential data from monkeys the existence of a synchronized regime in which unidirectionally coupled areas can present both positive and negative phase differences. During the counterintuitive regime, called anticipated synchronization (AS), the phase difference does not reflect the causality. Here we investigate coherence and causality at the alpha frequency band (f∼10 Hz) between pairs of electroencephalogram (EEG) electrodes in humans during a GO/NO-GO task. We show that human EEG signals can exhibit anticipated synchronization, which is characterized by a unidirectional influence from an electrode A to an electrode B, but the electrode B leads the electrode A in time. To the best of our knowledge, this is the first verification of AS in EEG signals and in the human brain. The usual delayed synchronization (DS) regime is also present between many pairs. DS is characterized by a unidirectional influence from an electrode A to an electrode B and a positive phase difference between A and B which indicates that the electrode A leads the electrode B in time. Moreover we show that EEG signals exhibit diversity in the phase relations: the pairs of electrodes can present in-phase, antiphase, or out-of-phase synchronization with a similar distribution of positive and negative phase differences.

Anticipated synchronization in coupled complex Ginzburg-Landau systems

We study the occurrence of anticipated synchronization in two complex Ginzburg-Landau systems coupled in a master-slave configuration. Master and slave systems are ruled by the same autonomous function, but the slave system receives the injection from the master and is subject to a negative delayed self-feedback loop. We give evidence that the magnitude of the largest anticipation time, obtained for complex-valued coupling constants, depends on the dynamical regime where the system operates (defect turbulence, phase turbulence, or bichaos) and scales with the linear autocorrelation time of the system. We also provide analytical conditions for the stability of the anticipated synchronization manifold that are in qualitative agreement with those obtained numerically. Finally, we report on the existence of anticipated synchronization in coupled two-dimensional complex Ginzburg-Landau systems.

Suppression of deterministic and stochastic extreme desynchronization events using anticipated synchronization

We numerically show that extreme events induced by parameter mismatches or noise in coupled oscillatory systems can be anticipated and suppressed before they actually occur. We show this in a main system unidirectionally coupled to an auxiliary system subject to a negative delayed feedback. Each system consists of two electronic oscillators coupled in a master-slave configuration. Extreme events are observed in this coupled system as large and sporadic desynchronization events. Under certain conditions, the auxiliary system can predict the dynamics of the main system. We use this to efficiently suppress the extreme events by applying a direct corrective reset to the main system.

Extending anticipation horizon of chaos synchronization schemes with time-delay coupling

We analyse anticipating synchronization in chaotic systems with time-delay coupling. Two algorithms for extending the prediction horizon are considered. One of them is based on the design of a suitable coupling matrix compensating the phase lag in the time-delay feedback term of the slave system. The second algorithm extends the first by incorporating, in the coupling law, information from many previous states of the master and slave systems. We demonstrate the efficiency of both algorithms with the simple dynamical model of coupled unstable spirals, as well as with the coupled Rössler systems. The maximum prediction time attained for the Rössler system is equal to the characteristic period of chaotic oscillations.

Time-shifted synchronization of chaotic oscillator chains without explicit coupling delays

We examine chains of unidirectionally coupled oscillators in which time-shifted synchronization occurs without explicit delays in the coupling. In numerical simulations and in an experimental system of electronic oscillators, we examine the time shift and the degree of distortion (primarily in the form of attenuation) of the waveforms of the oscillators located far from the drive oscillator. Surprisingly, under weak coupling we observe minimal attenuation in spite of a significant total time shift. In contrast, at higher coupling strengths the observed attenuation increases dramatically and approaches the value predicted by an analytically derived estimate. In this regime, we verify directly that generalized synchronization is maintained over the entire chain length despite severe attenuation. These results suggest that weak coupling generally may produce higher quality synchronization in systems for which truly identical synchronization is not possible.

Anticipatory synchronization with variable time delay and reset

A method to synchronize two chaotic systems with anticipation or lag, coupled in the drive response mode, is proposed. The coupling involves variable delay with three time scales. The method has the advantage that synchronization is realized with intermittent information about the driving system at intervals fixed by a reset time. The stability of the synchronization manifold is analyzed with the resulting discrete error dynamics. The numerical calculations in standard systems such as the Rössler and Lorenz systems are used to demonstrate the method and the results of the analysis.

Using synchronization for prediction of high-dimensional chaotic dynamics

We experimentally observe the nonlinear dynamics of an optoelectronic time-delayed feedback loop designed for chaotic communication using commercial fiber optic links, and we simulate the system using delay differential equations. We show that synchronization of a numerical model to experimental measurements provides a new way to assimilate data and forecast the future of this time-delayed high-dimensional system. For this system, which has a feedback time delay of 22 ns, we show that one can predict the time series for up to several delay periods, when the dynamics is about 15 dimensional.

Coupling design for a long-term anticipating synchronization of chaos

We propose an algorithm of coupling design for a long-term anticipating synchronization of chaos and demonstrate its efficacy for typical chaotic systems: namely, the Rössler system, the double-scroll Chua circuit, and the Lorenz system. The maximum prediction time attained with our algorithm is several times larger than with the diagonal coupling usually used in the literature.

Time shifts and correlations in synchronized chaos

We introduce a new method for predicting characteristics of the synchronized state achieved by a wide class of unidirectional coupling schemes. Specifically, we derive a transfer function from the coupling model that provides estimates of the correlation between the drive and response waveforms, and of the time shift (i.e., lag or anticipation) of the synchronized state. To demonstrate the method, we apply it to a simulated system of coupled Rossler oscillators as well as to an experimental system of coupled chaotic electronic circuits. Finally, we show that the transfer function can be exploited to design novel coupling schemes that significantly improve the correlation and increase the maximum achievable time shift.

Estimating nonlinear interdependences in dynamical systems using cellular nonlinear networks

We propose a method for estimating nonlinear interdependences between time series using cellular nonlinear networks. Our approach is based on the nonlinear dynamics of interacting nonlinear elements. We apply it to time series of coupled nonlinear model systems and to electroencephalographic time series from an epilepsy patient, and we show that an accurate approximation of symmetric and asymmetric realizations of a nonlinear interdependence measure can be achieved, thus allowing one to detect the strength and direction of couplings.