Slow-fast Systems Research Articles

Slow passage through a Hopf-like bifurcation in piecewise linear systems: Application to elliptic bursting.

The phenomenon of slow passage through a Hopf bifurcation is ubiquitous in multiple-timescale dynamical systems, where a slowly varying quantity replacing a static parameter induces the solutions of the resulting slow-fast system to feel the effect of the Hopf bifurcation with a delay. This phenomenon is well understood in the context of smooth slow-fast dynamical systems; in the present work, we study it for the first time in piecewise linear (PWL) slow-fast systems. This special class of systems is indeed known to reproduce all features of their smooth counterpart while being more amenable to quantitative analysis and offering some level of simplification, in particular, through the existence of canonical (linear) slow manifolds. We provide conditions for a PWL slow-fast system to exhibit a slow passage through a Hopf-like bifurcation, in link with possible connections between canonical attracting and repelling slow manifolds. In doing so, we fully describe the so-called way-in/way-out function. Finally, we investigate this slow passage effect in the Doi-Kumagai model, a neuronal PWL model exhibiting elliptic bursting oscillations.

Second-order asymptotic expansion and thermodynamic interpretation of a fast–slow Hamiltonian system

This article includes a short survey of selected averaging and dimension reduction techniques for deterministic fast–slow systems. This survey includes, among others, classical techniques, such as the WKB approximation or the averaging method, as well as modern techniques, such as the GENERIC formalism. The main part of this article combines ideas of some of these techniques and addresses the problem of deriving a reduced system for the slow degrees of freedom (DOF) of a fast–slow Hamiltonian system. In the first part, we derive an asymptotic expansion of the averaged evolution of the fast–slow system up to second order, using weak convergence techniques and two-scale convergence. In the second part, we determine quantities which can be interpreted as temperature and entropy of the system and expand these quantities up to second order, using results from the first part. The results give new insights into the thermodynamic interpretation of the fast–slow system at different scales.

The Poincaré maps of a slow-fast stochastic system

This work is concerned with the Poincaré maps of a slow-fast stochastic system. Applying the random manifold reduced theory and the moving orthonormal system argument, it derives the effective construction of the stochastic Poincaré map. Moreover, employing the geometric distribution of the first exit time, it builds the stability of the stochastic Poincaré maps, that is, the stochastic Poincaré maps return to a small neighborhood of a fixed point of a deterministic Poincaré map with one time or even infinite times as the noise intensity tends to zero. It further investigates the limit distribution of the first exit location on the boundary of the domain via the harmonic measure.

Bursting oscillations with bifurcations of chaotic attractors in a modified Chua’s circuit

This paper aims at the bursting oscillations as well as the dynamical mechanism in non-smooth systems. A fourth-order Chua’s circuit is modified to establish a non-smooth system by introducing a periodic exciting current and a piecewise nonlinear resistor. Under the condition that the dimensionless excitation frequency is far less than 1, the system is a slow–fast system, and five representative bursting patterns are observed via changing the excitation amplitude. By regarding the whole term of periodic exciting current as a bifurcation parameter, several kinds of smooth or non-smooth bifurcations are obtained in the fast subsystem via numerical or theoretic method. Particularly, a so-called saddle-focus-induced catastrophic bifurcation(SFIC), which is generated by collision between a non-smooth chaotic attractor and a saddle-focus, is observed. In addition, bifurcation diagrams and the associated maximal Lyapunov exponent spectrum are also obtained numerically, which can illustrate the scenario from periodic solutions to chaotic solutions via period-doubling bifurcations. Based on the bifurcation analysis, the generation mechanism of the bursting patterns is revealed. Research shows that the SFIC bifurcation in the fast subsystem may result in a sudden jumping behavior in the slow–fast system from a chaotic attractor to another. A spiking attractor may be constructed by chaotic attractors and limit cycle attractors together, which can lead to chaotic–periodic bursting oscillations. Slow passage effect induced by Hopf bifurcation can lead to delay phenomenon of other bifurcation in the slow–fast system.

Dynamics in diffusive Leslie–Gower prey–predator model with weak diffusion

This paper is concerned with the diffusive Leslie–Gower prey–predator model with weak diffusion. Assuming that the diffusion rates of prey and predator are sufficiently small and the natural growth rate of prey is much greater than that of predators, the diffusive Leslie–Gower prey–predator model is a singularly perturbed problem. Using travelling wave transformation, we firstly transform our problem into a multiscale slow-fast system with two small parameters. We prove the existence of heteroclinic orbit, canard explosion phenomenon and relaxation oscillation cycle for the slow-fast system by applying the geometric singular perturbation theory. Thus, we get the existence of travelling waves and periodic solutions of the original reaction–diffusion model. Furthermore, we also give some numerical examples to illustrate our theoretical results.

The number of limit cycles for regularized piecewise polynomial systems is unbounded

In this paper, we extend the slow divergence-integral from slow-fast systems, due to De Maesschalck, Dumortier and Roussarie, to smooth systems that limit onto piecewise smooth ones as ϵ→0. In slow-fast systems, the slow divergence-integral is an integral of the divergence along a canard cycle with respect to the slow time and it has proven very useful in obtaining good lower and upper bounds of limit cycles in planar polynomial systems. In this paper, our slow divergence-integral is based upon integration along a generalized canard cycle for a piecewise smooth two-fold bifurcation (of type visible-invisible called VI3). We use this framework to show that the number of limit cycles in regularized piecewise smooth polynomial systems is unbounded.

Slow-fast systems with fractional environment and dynamics

We prove a fractional averaging principle for interacting slow-fast systems. The mode of convergence is in Hölder norm in probability. The main technical result is a quenched ergodic theorem on the conditioned fractional dynamics. We also establish geometric ergodicity for a class of fractional-driven stochastic differential equations, improving a recent result of Panloup and Richard.

A model for the collective synchronization of flashing in Photinus carolinus.

Recent empirical investigations have characterized the synchronized flashing behaviours of male Photinus carolinus fireflies in their natural habitat in Great Smoky Mountain National Park as well as in controlled environments. We develop a model for the flash dynamics of an individual firefly based on a canonical elliptic burster, a slow-fast dynamical system that produces a repeating pattern of multiple flashes followed by a quiescent period. We show that a small amount of noise renders that oscillation very irregular, but when multiple model fireflies interact through their flashes, the behaviour becomes much more periodic. We show that the aggregate behaviour is qualitatively similar to the experimental findings. We next distribute the fireflies in a two-dimensional spatial domain and vary the interaction range. In addition to synchronization, various spatio-temporal patterns involving propagation of activity emerge spontaneously. Finally, we allow a certain number of fireflies to move and demonstrate how their speed affects the rate and degree of synchronization.

Partial Exponential Stability Analysis of Slow–Fast Systems via Periodic Averaging

This article presents some new criteria for the partial exponential stability of a slow–fast nonlinear system with a fast scalar variable using periodic averaging methods. Unlike classical averaging techniques, we construct an averaged system by averaging over this fast scalar variable instead of the time variable. We show that the partial exponential stability of the averaged system implies that of the original one. We then apply the obtained criteria to the study of remote synchronization of Kuramoto–Sakaguchi oscillators coupled by a star network with two peripheral nodes. We show that detuning the natural frequency of the central mediating oscillator increases the robustness of the remote synchronization against phase shifts. This article appears to be the first-known attempt to analytically study the phase-unlocked remote synchronization.

Emergence of Canard induced mixed mode oscillations in a slow–fast dynamics of a biophysical excitable model

We study the dynamics of a biophysically motivated slow–fast FitzHugh–Rinzel (FHR) model neurons in understanding the complex dynamical behavior of neural computation. We discuss the mathematical frameworks of diverse excitabilities and repetitive firing responses due to the applied stimulus using the slow–fast system. The results focus on the multiple time scale dynamics that include canard phenomenon induced mixed mode oscillations (MMOs) and mixed mode bursting oscillations (MMBOs). The bifurcation structure of the system is examined with injected current stimulus as the relevant parameter. We use the folded node theory to study the canards near the fold points. Further, we demonstrate the homoclinic bifurcation and the transition route to chaos through MMOs. It helps us in understanding the fundamentals of such complex rich neuronal responses. To show the chaotic nature in certain parameter regime, we compute the Lyapunov spectrum as a function of time and predominant parameter, I, that establishes our findings. Finally, we conclude that our observed results may have major significance and discuss the potential applications of MMOs in neural dynamics.

Deterministic homogenization under optimal moment assumptions for fast–slow systems. Part 2

We consider deterministic homogenization for discrete-time fast-slow systems of the form $$ X_{k+1} = X_k + n^{-1}a_n(X_k,Y_k) + n^{-1/2}b_n(X_k,Y_k)\;, \quad Y_{k+1} = T_nY_k\;$$ and give conditions under which the dynamics of the slow equations converge weakly to an It\^o diffusion $X$ as $n\to\infty$. The drift and diffusion coefficients of the limiting stochastic differential equation satisfied by $X$ are given explicitly. This extends the results of [Kelly-Melbourne, J. Funct. Anal. 272 (2017) 4063--4102] from the continuous-time case to the discrete-time case. Moreover, our methods (c\`adl\`ag $p$-variation rough paths) work under optimal moment assumptions. Combined with parallel developments on martingale approximations for families of nonuniformly expanding maps in Part 1 by Korepanov, Kosloff & Melbourne, we obtain optimal homogenization results when $T_n$ is such a family of maps.

Deterministic homogenization under optimal moment assumptions for fast-slow systems. Part 1

Nous étudions un problème d’homogénéisation déterministe (avec convergence vers une équation différentielle stochastique) pour un système multi-échelle de la forme suivante : xk+1=xk+n−1an(xk,yk)+n−1/2bn(xk,yk),yk+1=Tnyk, où la dynamique rapide est donnée par une famille Tn de transformations non uniformément dilatantes. La partie 1 prolonge nos travaux récents sur l’approximation par des martingales pour des familles de transformations non uniformément dilatantes. Nous montrons un principe d’invariance faible itéré, et établissons des bornes optimales sur les moments dans ce cadre (ces bornes sont nouvelles même pour une transformation non uniformément dilatante T fixée). En combinant ceci et des développements parallèles sur la théorie des chemins rugueux par Chevyrev, Friz, Korepanov, Melbourne et Zhang, nous obtenons les résultats d’homogénéisation dans la partie 2.

Reservoir computing with diverse timescales for prediction of multiscale dynamics

Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons. We evaluate computational performance of the proposed model in two time series prediction tasks related to four chaotic fast-slow dynamical systems. In a one-step-ahead prediction task where input data are provided only from the fast subsystem, we show that the proposed model yields better performance than the standard RC model with identical LI neurons. Our analysis reveals that the timescale required for producing each component of target multiscale dynamics is appropriately and flexibly selected from the reservoir dynamics by model training. In a long-term prediction task, we demonstrate that a closed-loop version of the proposed model can achieve longer-term predictions compared to the counterpart with identical LI neurons depending on the hyperparameter setting.Received 23 August 2021Accepted 28 June 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L032014Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNeuronal dynamicsPhysical SystemsDynamical systemsMultiple time scale dynamicsNonlinear Dynamics

Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm

Machine learning becomes popular and is used for a wide range of problems in various areas of applied sciences. In dynamical systems, machine learning methods are applied to solve differential equations. In this paper, we develop an artificial network to solve systems of ordinary differential equations. For the network, we use a multilayer perceptron networks, which is a fully connected feedforward network to predict the flow of a specific system. In order to improve the long time estimation of the method, we introduced an upper bound control strategy. To deal with stiff ODE systems(such as slow-fast coupled system), a new algorithm, Finite Neural Element method, is introduced. By numerical simulation, the novel algorithm is proved to have better efficiency and accuracy than direct machine learning method.

The emergence of polyglot entrainment responses to periodic inputs in vicinities of Hopf bifurcations in slow-fast systems.

Several distinct entrainment patterns can occur in the FitzHugh-Nagumo (FHN) model under external periodic forcing. Investigating the FHN model under different types of periodic forcing reveals the existence of multiple disconnected 1:1 entrainment segments for constant, low enough values of the input amplitude when the unforced system is in the vicinity of a Hopf bifurcation. This entrainment structure is termed polyglot to distinguish it from the single 1:1 entrainment region (monoglot) structure typically observed in Arnold tongue diagrams. The emergence of polyglot entrainment is then explained using phase-plane analysis and other dynamical system tools. Entrainment results are investigated for other slow-fast systems of neuronal, circadian, and glycolytic oscillations. Exploring these models, we found that polyglot entrainment structure (multiple 1:1 regions) is observed when the unforced system is in the vicinity of a Hopf bifurcation and the Hopf point is located near a knee of a cubic-like nullcline.

Reactivity, Attenuation, and Transients in Metapopulations

Transient dynamics often differ drastically from the asymptotic dynamics of systems. In this paper we analyze transient dynamics in birth-jump metapopulations where dispersal occurs immediately after birth (e.g., via larval dispersal). We address the choice of appropriate norms as well as the effect of stage structure on transient dynamics. We advocate the use of the $\ell_1$ norm, because of its biological interpretation, and extend the transient metrics of reactivity and attenuation to birth-jump metapopulations in this norm. By way of examples we compare this norm to the more commonly used $\ell_2$ norm. Our focus is the case where transient dynamics are very different than asymptotic dynamics. We provide simple examples of metapopulations where this is the case and also show how increasing the number of habitat patches can increase this difference. We then connect the reactivity and attenuation of metapopulations to the source-sink classification of habitat patches and demonstrate how to meaningfully measure reactivity when metapopulations are stage-structured, with a focus on marine metapopulations. Our paper makes three primary contributions. First, it provides guidance to readers as to the appropriate norm and scalings for studying transients in birth-jump metapopulations. Second, it provides three examples of transient behavior in metapopulations involving slow-fast systems, crawl-bys, and high dimensionality. Third, it connects the concepts of reactivity and attenuation to the source-sink classification of habitat patches more commonly found in marine metapopulations.

Abelian Integrals and Non-generic Turning Points

In this paper we initiate the study of the Chebyshev property of Abelian integrals generated by a non-generic turning point in planar slow-fast systems. Such Abelian integrals generalize the Abelian integrals produced by a slow-fast Hopf point (or generic turning point), introduced in Dumortier et al. (Discrete Contin Dyn Syst Ser S 2(4):723–781, 2009), and play an important role in studying the number of limit cycles born from the non-generic turning point.

Slow-Fast Model and Therapy Optimization for Oncolytic Treatment of Tumors.

The present work studies models of oncolytic virotherapy without space variable in which virus replication occurs at a faster time scale than tumor growth. We address the questions of the modeling of virus injection in this slow-fast system and of the optimal timing for different treatment strategies. To this aim, we first derive the asymptotic of a three-species slow-fast model and obtain a two-species dynamical system, where the variables are tumor cells and infected tumor cells. We fully characterize the behavior of this system depending on the various biological parameters. In the second part, we address the modeling of virus injection and its expression in the two-species system, where the amount of virus does not appear explicitly. We prove that the injection can be described by an instantaneous jump in the phase plane, where a certain amount of tumors cells are transformed instantly into infected tumor cells. This description allows discussing qualitatively the timing of different injections in the frame of successive treatment strategies. This work is illustrated by numerical simulations. The timing and amount of injected virus may have counterintuitive optimal values; nevertheless, the understanding is clear from the phase space analysis.

Canards, relaxation oscillations, and pattern formation in a slow-fast ratio-dependent predator-prey system

The prey-predator system is an elementary building block in many complicated models of ecological dynamics that exhibit complex dynamical behaviour and as such it remains to be a focus of intense research. Here we consider a new model that incorporates multiple timescales (in the form of a ‘slow-fast’ system) with the ratio-dependent predator response. In the nonspatial case, we study this model exhaustively using an array of analytical tools to demonstrate the existence of canard cycles and relaxation oscillation passing through the close vicinity of the complicated singular point of the system. In the spatial case, the model exhibits a wide variety of spatio-temporal patterns that have been studied using extensive numerical simulations. In particular, we reveal the explicit dependence of the slow-fast timescale parameter on the Turing instability threshold and show how this affects the properties of the emerging stationary population patches. We also show that the self-organized spatial heterogeneity of the species can reduce the risk of extinction and can stabilize the chaotic oscillations. We argue that incorporating multiple timescales can enhance our understanding for studying realistic scenarios of local extinction and periodic outbreaks of the species. Our results suggest that the ubiquitous complexity of ecosystem dynamics observed in nature stems from the elementary level of ecological interactions such as the prey-predator system.

OPTIMAL PLACEMENT OF MARINE PROTECTED AREAS TO AVOID THE EXTINCTION OF THE FISH STOCK

In this paper, we propose to study a fishery model with variable price. We assume that the price evolves much faster than the rest of the system. Under certain assumptions, this makes it possible to consider the fishery system as a slow–fast system, on two time scales, and to study it with a reduced model of dimension two. Two main cases can occur. The first one which we called catastrophic equilibrium corresponds to over-exploitation leading to fish extinction and a booming price. The second case corresponds to a sustainable fishery equilibrium which is stable. The possible effects of the creation of marine protected areas (MPAs), sites where fishing is prohibited, on the fish stock and fishery are evaluated. We show that MPAs can have a positive effect on the restoration of depleted fish stocks by destabilizing the catastrophic equilibrium and keeping only one positive equilibrium which will be globally asymptotically stable. This problem is addressed by proposing a model with MPA for the fish dynamics. Fish are assumed to move between MPA and fishing area and are subject to harvesting through fishing. We show that to avoid the extinction of the stock and stabilize the fishery in the long term, it is necessary to define a fishing zone such that the ratio of its carrying capacity to its surface is small enough. We further show that with a judicious choice of the surface area of the MPA, it is possible to optimize the total capture.