A non-autonomous kind of Duffing equation

Agraïments: The research of AR is supported by the Swedish Research Council (VR Grant 2010/5905). The authors would like to thank the Göran Gustafsson Foundation UU/KTH for financial support.We study the periodic solutions of the following kind of non-autonomous Duffing differential equation y + ay − εy 3 = εh(t, y, y), with a > 0, ε a small parameter and h a C 2 function in its variables

A non-autonomous kind of Duffing equation

Agraïments: The research of AR is supported by the Swedish Research Council (VR Grant 2010/5905). The authors would like to thank the Göran Gustafsson Foundation UU/KTH for financial support.We study the periodic solutions of the following kind of non-autonomous Duffing differential equation y + ay − εy 3 = εh(t, y, y), with a > 0, ε a small parameter and h a C 2 function in its variables

Bifurcation structure of two Coupled Periodically driven double-well Duffing Oscillators

The bifurcation structure of coupled periodically driven double-well Duffing oscillators is investigated as a function of the strength of the driving force $f$ and its frequency $\Omega$. We first examine the stability of the steady state in linear response, and classify the different types of bifurcation likely to occur in this model. We then explore the complex behaviour associated with these bifurcations numerically. Our results show many striking departures from the behaviour of coupled driven Duffing Oscillators with single well-potentials, as characterised by Kozlowski et al \cite{k1}. In addition to the well known routes to chaos already encountered in a one-dimensional Duffing oscillator, our model exhibits imbricated period-doubling of both types, symmetry-breaking, sudden chaos and a great abundance of Hopf bifurcations, many of which occur more than once for a given driving frequency. We explore the chaotic behaviour of our model using two indicators, namely Lyapunov exponents and the power spectrum. Poincar\'e cross-sections and phase portraits are also plotted to show the manifestation of coexisting periodic and chaotic attractors including the destruction of $T^2$ tori doubling.Comment: 16 pages, 8 figure

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Parameter switching in a generalized Duffing system: Finding the stable attractors

This paper presents a simple periodic parameter-switching method which can find any stable limit cycle that can be numerically approximated in a generalized Duffing system. In this method, the initial value problem of the system is numerically integrated and the control parameter is switched periodically within a chosen set of parameter values. The resulted attractor matches with the attractor obtained by using the average of the switched values. The accurate match is verified by phase plots and Hausdorff distance measure in extensive simulations