Stochastic Sensitivity Function Research Articles

Analysis of Noise-Induced Chaos-Order Transitions in Rulkov Model Near Crisis Bifurcations

Noise-induced transitions from chaos to order in nonlinear systems with crisis bifurcations are studied. In this study, a discrete-time Rulkov system is used as a conceptual model of the neuronal activity. We investigate probabilistic mechanisms of noise-induced transitions from chaotic spiking to quiescence in zones of crisis bifurcations. To analyze these transitions parametrically, we apply a mathematical technique based on the stochastic sensitivity functions and confidence domains. A stochastic phenomenon of the shifts of crisis bifurcation points and the expansion of the order window under increasing noise is discussed and analyzed. Using our analytical approach, we construct a parametric description of chaotic and regular regimes for the randomly forced Rulkov model.

Dynamics of traveling waves in fluctuating nonlocal media

The article deals with nonlocal hydrodynamic models for structured media with a fluctuating parameter. We are interested in the structure of traveling wave solutions disturbed by noise. Using the stochastic sensitivity function technique, the confidence ellipses for periodic trajectories obeying the period doubling scenario, hidden and spiral periodic orbits are derived. To identify the peculiarities of confidence ellipses, we consider the variation of eccentricity and area over the period of a periodic trajectory. We show that the dynamics of eccentricity of noisy limit cycle, up to triple period, has the number of minima coinciding with the cycle’s multiplicity, whereas this is not in the case of quadruple cycle. The profiles of function for the areas of confidence ellipses characterize the heterogeneous anatomy of stochastic attractors and possess scaling properties for multiple cycles. Considering the eccentricity and area of confidence ellipses for the spiral trajectory existing in the vicinity of Shilnikov homoclinic loop, the intensive oscillations of eccentricity and area are observed when the confidence ellipses are derived for the flow near the one dimensional manifold of Shilnikov’s orbit.

Noise-induced transitions in a stochastic Goodwin-type business cycle model

We motivate and specify a stochastic Goodwin-type business cycle model. Our analysis focusses on a subset of the parameter space where several attractors coexist. Applying a semi-numerical approach based on the stochastic sensitivity function and confidence domains due to Milstein and Ryashko (1995), we study random transitions between stable attractors in the context of the Goodwin-type economy embedded in an uncertain environment. Relying on a mix of analytical considerations and simulations we demonstrate that under weak noise levels regime switching is a prominent feature in the presence of low saving rates. Moreover, we explain how increased uncertainty can induce an essentially unpredictable income process out of an apparently stable high-income level situation. All dynamic phenomena are explained in terms of key concepts constituting the stochastic sensitivity function method.

Stochastic Sensitivity Analysis of Noise-Induced Mixed-Mode Oscillations in Morris--Lecar Neuron Model

The effect of noise on the two-dimensional Morris--Lecar neuron model is studied. In the deterministic case, this system exhibits mono- and bistable dynamic regimes. In the parametric zone, where the stable equilibrium and the stable limit cycle coexist, the phenomenon of noise-induced transitions between the attractors is investigated. In the parametric zone of monostability, when the single attractor of the deterministic system is the stable equilibrium, the stochastic generation of large amplitude oscillations is also observed. We show that under the stochastic disturbances, in both cases the system demonstrates noise-induced mixed-mode oscillations, i.e., the alternation of small and large amplitude oscillations. For the parametric analysis of this phenomenon, an approach combining stochastic sensitivity function technique, confidence domains and Mahalanobis distance methods is suggested.

Stochastic sensitivity and variability of glycolytic oscillations in the randomly forced Sel’kov model

In present paper, we study underlying mechanisms of the stochastic excitability in glycolysis on the example of the model proposed by Sel’kov. A stochastic variant of this model with the randomly forced influx of the substrate is considered. Our analysis is based on the stochastic sensitivity function technique. A detailed parametric analysis of the stochastic sensitivity of attractors is carried out. A range of parameters where the stochastic model is highly sensitive to noise is determined, and a supersensitive Canard cycle is found. Phenomena of the stochastic excitability and variability of forced equilibria and cycles are demonstrated and studied. It is shown that in the zone of Canard cycles noise-induced chaos is observed.

On the Stochastic Sensitivity and Noise-Induced Transitions of a Kaldor-Type Business Cycle Model

In the paper, we consider a Kaldor-type model of the business cycle with external additive and internal parametric disturbances. We study analytically and numerically the probability properties of stochastically forced equilibria and limit cycles via stochastic sensitivity function technique. In particular, we discuss the effects of additive and parametric noises on the economic variables and we detect some stochastic bifurcations such as a P-bifurcation, i.e a phenomenon of noise-induced transition from monostability to bistability. This stochastic bistability causes a new trigger regime in economic dynamics.

Stochastic Bifurcations and Noise-Induced Chaos in 3D Neuron Model

The stochastically forced three-dimensional Hindmarsh–Rose model of neural activity is considered. We study the effect of random disturbances in parametric zones where the deterministic model exhibits mono- and bistable dynamic regimes with period-adding bifurcations of oscillatory modes. It is shown that in both cases the phenomenon of noise-induced bursting is observed. In the monostable zone, where the only attractor of the system is a stable equilibrium, this effect is connected with a stochastic generation of large-amplitude oscillations due to the high excitability of the model. In a parametric zone of coexisting stable equilibria and limit cycles, bursts appear due to noise-induced transitions between the attractors. For a quantitative analysis of the noise-induced bursting and corresponding stochastic bifurcations, an approach based on the stochastic sensitivity function (SSF) technique is applied. Our estimations of the strength of noise that generates such qualitative changes in stochastic dynamics are in a good agreement with the direct numerical simulation. A relationship of the noise-induced generation of bursts with transitions from order to chaos is discussed.

Stochastic sensitivity analysis of noise-induced order-chaos transitions in discrete-time systems with tangent and crisis bifurcations

Abstract We study noise-induced order-chaos transitions in discrete-time systems with tangent and crisis bifurcations. To study these transitions parametrically, we suggest a generalized mathematical technique using stochastic sensitivity functions and confidence domains for randomly forced equilibria, cycles, and chaotic attractors. This technique is demonstrated in detail for the simple one-dimensional stochastic system, in which points of crisis and tangent bifurcations are borders of the order window lying between two chaotic parametric zones. A stochastic phenomenon of the extension and shift of this window towards crisis bifurcation point, under increasing noise, is presented and analyzed. Shifts of borders of this order window are found as functions of the noise intensity. By our analytical approach based on stochastic sensitivity functions, we construct a parametric diagram of chaotic and regular regimes for the stochastically forced system.

Estimation of Critical Conditions for Noise-Induced Bifurcation in Nonautonomous Nonlinear Systems by Stochastic Sensitivity Function

This paper proposes an efficient but simple method to determine the approximate stationary probability distribution around periodic attractors of nonautonomous nonlinear systems under multiple time-dependent parametric noises and estimate the critical noise intensity for noise-induced explosive bifurcations under a given confidence probability. After adopting a stroboscopic map constructed by a method with higher accuracy and efficiency, nonautonomous dynamical systems around periodic attractors are transformed into mapping ones. Then the mean-square analysis method of discrete systems is used to derive the stochastic sensitivity function. Based on the confidence ellipses of stochastic attractors and the global structure of deterministic nonlinear systems, the critical noise intensity of noise-induced explosive bifurcations under a given confidence probability is estimated. A Mathieu–Duffing oscillator under both multiplicative and additive noises is studied to show the validity of the proposed method.

Stochastic Sensitivity Analysis for a Competitive Turbidostat Model with Inhibitory Nutrients

A stochastic model of turbidostat in which two microorganism species compete for an inhibitory growth-limiting nutrient is considered. In the deterministic case, the model has rich dynamics: a coexistence equilibrium and the washout equilibrium can be simultaneously stable, and a stable limit cycle may exist. In the stochastic case, a phenomenon of noise-induced extinction occurs. Namely, the stochastic trajectory near the deterministic coexistence equilibrium will tend to the washout equilibrium. Based on the stochastic sensitivity function technique, in this paper, we construct the confidence ellipse and then estimate the critical value of the intensity for noise generating a transition from coexistence to extinction. We also construct the confidence band to find the configurational arrangement of the stochastic cycle.

Stochastic sensitivity of a bistable energy model for visual perception

Modern trends in physiology, psychology and cognitive neuroscience suggest that noise is an essential component of brain functionality and self-organization. With adequate noise the brain as a complex dynamical system can easily access different ordered states and improve signal detection for decision-making by preventing deadlocks. Using a stochastic sensitivity function approach, we analyze how sensitive equilibrium points are to Gaussian noise in a bistable energy model often used for qualitative description of visual perception. The probability distribution of noise-induced transitions between two coexisting percepts is calculated at different noise intensity and system stability. Stochastic squeezing of the hysteresis range and its transition from positive (bistable regime) to negative (intermittency regime) are demonstrated as the noise intensity increases. The hysteresis is more sensitive to noise in the system with higher stability.

Stochastic Generation of Bursting Oscillations in the Three-dimensional Hindmarsh–Rose Model

We study the effect of random disturbances on the dynamics of the three-dimensional Hindmarsh–Rose model of neuronal activity. Due to the strong nonlinearity, even the original deterministic system exhibits diverse and complex dynamic regimes (various types of periodic oscillations, oscillations zones with period doubling and adding, coexistence of several attractors, chaos). In this paper, we consider a parametric zone where a stable equilibrium is the only attractor. We show that even in this zone with simple deterministic dynamics, under the random disturbances, such complex effect as the stochastic generation of bursting oscillations can occur. For a small noise, random states concentrate near the equilibrium. With the increase of the noise intensity, random trajectories can go far from the stable equilibrium, and along with small-amplitude oscillations around the equilibrium, bursts are observed. This phenomenon is analysed using the mathematical methods based on the stochastic sensitivity function technique. An algorithm of estimation of critical values for noise intensity is proposed.

Semi-analytical expression of stochastic closed curve attractors in nonlinear dynamical systems under weak noise

In this paper, a simple but accurate semi-analytical method to approximate probability density function of stochastic closed curve attractors is proposed. The expression of distribution applies to systems with strong nonlinearities, while only weak noise condition is needed. With the understanding that additive noise does not change the longitudinal distribution of the attractors, the high-dimensional probability density distribution is decomposed into two low-dimensional distributions: the longitudinal and the transverse probability density distributions. The longitudinal distribution can be calculated from the deterministic systems, while the probability density in the transverse direction of the curve can be approximated by the stochastic sensitivity function method. The effectiveness of this approach is verified by comparing the expression of distribution with the results of Monte Carlo numerical simulations in several planar systems.

Approximating Chaotic Attractors by Period-Three Cycles in Discrete Stochastic Systems

We study a distribution of random states forced outwards of the general deterministic attractor (regular or chaotic) for one-dimensional discrete-time systems with unimodal map. To approximate this distribution, we replace the original system by the appropriate modeling system with stable 3-cycle and use the stochastic sensitivity function technique. Constructive abilities of the suggested approach are demonstrated in the analysis of noise-induced transitions from chaos to order.

Order and chaos in the stochastic Hindmarsh–Rose model of the neuron bursting

We study the stochastic dynamics of the two-dimensional Hindmarsh–Rose model. In the deterministic case, this system demonstrates mono- and bi-stable dynamic regimes. In the parametric zone of the coexisting stable equilibrium and limit cycle, the phenomenon of noise-induced transitions between the attractors is studied. In another parametric region, where the deterministic system has the only stable equilibrium, the stochastic generation of high-amplitude oscillations is also observed. We show that under the random disturbances, the system demonstrates noise-induced bursting: an alternation of small fluctuations near the equilibrium and high-amplitude oscillations. For the quantitative analysis of noise-induced bursting, an approach combining stochastic sensitivity function technique and confidence domains method is suggested. Constructive abilities of this method for the estimation of critical values for noise intensity corresponding to the qualitative changes in stochastic dynamics are demonstrated and confirmed by the good agreement with the direct numerical simulation. An interplay between noise-induced bursting and mutual transformations “order–chaos” is discussed.

Analysis of dynamic regimes in stochastically forced Kaldor model

We consider the business cycle Kaldor model forced by random noise. Detailed parametric analysis of deterministic system is carried out and zones of coexisting stable equilibrium and stable limit cycle are found. Noise-induced transitions between these attractors are studied using stochastic sensitivity function technique and confidence domains method. Critical values of noise intensity corresponding to noise-induced transitions “equilibrium → cycle” and “cycle → equilibrium” are estimated. Dominants in combined stochastic regimes are discussed.

Stochastic sensitivity analysis of the attractors for the randomly forced Ricker model with delay

Abstract Stochastically forced regular attractors (equilibria, cycles, closed invariant curves) of the discrete-time nonlinear systems are studied. For the analysis of noisy attractors, a unified approach based on the stochastic sensitivity function technique is suggested and discussed. Potentialities of the elaborated theory are demonstrated in the parametric analysis of the stochastic Ricker model with delay nearby Neimark–Sacker bifurcation.

Stochastic Bifurcations and Noise-Induced Chaos in a Dynamic Prey–Predator Plankton System

We consider the stochastic Truscott–Brindley dynamical model of the interacting populations of prey and predator. We study a new phenomenon of the stochastic cycle splitting. In a zone of Canard cycles, using the stochastic sensitivity function technique, we find a critical value of the parameter corresponding to the supersensitive cycle. In the neighborhood of this critical value, a comparative parametrical analysis of the phenomenon of the stochastic cycle splitting is performed. It is shown that the bifurcation of the stochastic cycle splitting is accompanied by the noise-induced chaotization.

Confidence Domains in the Analysis of Noise-Induced Transition to Chaos for Goodwin Model of Business Cycles

Stochastically forced Goodwin model of business cycles is considered. In a multistable zone, we study the phenomena of noise-induced transitions and chaotization. To clarify a probabilistic mechanism of these phenomena found numerically, we apply a semi-analytical method based on the stochastic sensitivity function technique and confidence domains. Using elaborated method, we estimate a critical value of the noise intensity corresponding to the onset of chaos.

Stochastic sensitivity analysis of periodic attractors in non-autonomous nonlinear dynamical systems based on stroboscopic map

Abstract To apply stochastic sensitivity function method, which can estimate the probabilistic distribution of stochastic attractors, to non-autonomous dynamical systems, a 1 / N -period stroboscopic map for a periodic motion is constructed in order to discretize the continuous cycle into a discrete one. In this way, the sensitivity analysis of a cycle for discrete map can be utilized and a numerical algorithm for the stochastic sensitivity analysis of periodic solutions of non-autonomous nonlinear dynamical systems under stochastic disturbances is devised. An external excited Duffing oscillator and a parametric excited laser system are studied as examples to show the validity of the proposed method.