Basin entropy as an indicator of a bifurcation in a time-delayed system.

The basin entropy is a measure that quantifies, in a system that has two or more attractors, the predictability of a final state, as a function of the initial conditions. While the basin entropy has been demonstrated on a variety of multistable dynamical systems, to the best of our knowledge, it has not yet been tested in systems with a time delay, whose phase space is infinite dimensional because the initial conditions are functions defined in a time interval [-τ,0], where τ is the delay time. Here, we consider a simple time-delayed system consisting of a bistable system with a linear delayed feedback term. We show that the basin entropy captures relevant properties of the basins of attraction of the two coexisting attractors. Moreover, we show that the basin entropy can give an indication of the proximity of a Hopf bifurcation, but fails to capture the proximity of a pitchfork bifurcation. The Hopf bifurcation is detected because before the fixed points become unstable, a oscillatory, limit-cycle behavior appears that coexists with the fixed points. The new limit cycle modifies the structure of the basins of attraction, and this change is captured by basin entropy that reaches a maximum before the Hopf bifurcation. In contrast, the pitchfork bifurcation is not detected because the basins of attraction do not change as the bifurcation is approached. Our results suggest that the basin entropy can yield useful insights into the long-term predictability of time-delayed systems, which often have coexisting attractors.

Development and decay of vortex flows in viscoelastic fluids between concentric cylinders.

We study the development and decay of vortex in viscoelastic fluids between coaxial cylinders by means of experiments with solutions of polyacrylamide and glycerin and numerical simulations. The transient process is triggered when the inner cylinder is either abruptly started or stopped while the outer is kept fixed. The azimuthal velocity, obtained by means of digital particle velocimetry, exhibits oscillations before reaching the stationary state. The development of the vortex is characterized by means of the overshoot, i.e. the difference between the maximum and the stationary velocity. Analogously, in the decay of the vortex, the azimuthal velocity changes its direction and the relevant parameter is the undershoot defined as the maximum reversed transient velocity. To get a deeper insight into this phenomenon, the experimental results are supplemented with numerical simulations of rheological models as the Oldroyd-B and White-Metzer. The results obtained with the first model reveal the dependence of the overshoot and undershoot with the elasticity number of the fluid. Using the White-Metzer model we explain the increase of the overshoot produced by the reduction of the solvent viscosity in terms of the shear-thinning effects.

Small-worldness favours network inference in synthetic neural networks.

A main goal in the analysis of a complex system is to infer its underlying network structure from time-series observations of its behaviour. The inference process is often done by using bi-variate similarity measures, such as the cross-correlation (CC) or mutual information (MI), however, the main factors favouring or hindering its success are still puzzling. Here, we use synthetic neuron models in order to reveal the main topological properties that frustrate or facilitate inferring the underlying network from CC measurements. Specifically, we use pulse-coupled Izhikevich neurons connected as in the Caenorhabditis elegans neural networks as well as in networks with similar randomness and small-worldness. We analyse the effectiveness and robustness of the inference process under different observations and collective dynamics, contrasting the results obtained from using membrane potentials and inter-spike interval time-series. We find that overall, small-worldness favours network inference and degree heterogeneity hinders it. In particular, success rates in C. elegans networks - that combine small-world properties with degree heterogeneity - are closer to success rates in Erdös-Rényi network models rather than those in Watts-Strogatz network models. These results are relevant to understand better the relationship between topological properties and function in different neural networks.

Organization and identification of solutions in the time-delayed Mackey-Glass model.

Multistability in the long term dynamics of the Mackey-Glass (MG) delayed model is analyzed by using an electronic circuit capable of controlling the initial conditions. The system's phase-space is explored by varying the parameter values of two families of initial functions. The evolution equation of the electronic circuit is derived and it is shown that, in the continuous limit, it exactly corresponds to the MG model. In practice, when using a finite set of capacitors, an excellent agreement between the experimental observations and the numerical simulations is manifested. As the delay is increased, different periodic or aperiodic solutions appear. We observe abundant periodic solutions that have the same period but a different alternation of peaks of dissimilar amplitudes and propose a novel symbolic method to classify these solutions.

Inferring long memory processes in the climate network via ordinal pattern analysis.

We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long- and short-term memory processes. Data analyzed are the monthly averaged surface air temperature (SAT field), and the results suggest that the time variability of the SAT field is determined by patterns of oscillatory behavior that repeat from time to time, with a periodicity related to intraseasonal oscillations and to El Niño on seasonal-to-interannual time scales.

Experimental results on synchronization times and stable states in locally coupled light-controlled oscillators.

Recently, a new kind of optically coupled oscillators that behave as relaxation oscillators has been studied experimentally in the case of local coupling. Even though numerical results exist, there are no references about experimental studies concerning the synchronization times with local coupling. In this paper, we study both experimentally and numerically a system of coupled oscillators in different configurations, including local coupling. Synchronization times are quantified as a function of the initial conditions and the coupling strength. For each configuration, the number of stable states is determined varying the different parameters that characterize each oscillator. Experimental results are compared with numerical simulations.

Chaos-induced coherence in two independent food chains.

Coherence evolution of two food web models can be obtained under the stirring effect of chaotic advection. Each food web model sustains a three-level trophic system composed of interacting predators, consumers, and vegetation. These populations compete for a common limiting resource in open flows with chaotic advection dynamics. Here we show that two species (the top predators) of different colonies chaotically advected by a jetlike flow can synchronize their evolution even without migration interaction. The evolution is characterized as a phase synchronization. The phase differences (determined through the Hilbert transform) of the variables representing those species show a coherent evolution.

Steady-state stabilization due to random delays in maps with self-feedback loops and in globally delayed-coupled maps.

We study the stability of the fixed-point solution of an array of mutually coupled logistic maps, focusing on the influence of the delay times, , of the interaction between the and maps. Two of us recently reported [Phys. Rev. Lett. 94, 134102 (2005)] that if are random enough, the array synchronizes in a spatially homogeneous steady state. Here we study this behavior by comparing the dynamics of a map of an array of delayed-coupled maps with the dynamics of a map with self-feedback delayed loops. If is sufficiently large, the dynamics of a map of the array is similar to the dynamics of a map with self-feedback loops with the same delay times. Several delayed loops stabilize the fixed point, when the delays are not the same; however, the distribution of delays plays a key role; if the delays are all odd a periodic orbit (and not the fixed point) is stabilized. We present a linear stability analysis and apply some mathematical theorems that explain the numerical results.