Stochastic resonance in a bistable piecewise potential: analytical solution

Stochastic resonance in a single-well potential and its application in rolling bearing fault diagnosis

In this paper, a single-well model based on the piecewise function and classical bistable stochastic resonance (CBSR) is proposed. The steady state probability density of particles and mean first passage time in the model are calculated. The output characteristics and performance of the proposed model are analyzed through numerical simulation. On the basis of CBSR and the proposed model, an adaptive system is established (ACSSR) to generate the highest gain of signal-to-noise ratio (SNR_g). Finally, the effectiveness of ACSSR in weak signal detection is verified with both simulated and experimental input signals. The results indicate that the ACSSR could detect the defect signal correctly and improve the SNR_g.

Escape of particles in a time-dependent potential well

We investigate the escape of an ensemble of noninteracting particles inside an infinite potential box that contains a time-dependent potential well. The dynamics of each particle is described by a two-dimensional nonlinear area-preserving mapping for the variables energy and time, leading to a mixed phase space. The chaotic sea in the phase space surrounds periodic islands and is limited by a set of invariant spanning curves. When a hole is introduced in the energy axis, the histogram of frequency for the escape of particles, which we observe to be scaling invariant, grows rapidly until it reaches a maximum and then decreases toward zero at sufficiently long times. A plot of the survival probability of a particle in the dynamics as function of time is observed to be exponential for short times, reaching a crossover time and turning to a slower-decay regime, due to sticky regions observed in the phase space.

Noise-Induced Coherence and Network Oscillations in a Reduced Bursting Model

The dynamics of the Hindmarsh-Rose (HR) model of bursting thalamic neurons is reduced to a system of two linear differential equations that retains the subthreshold resonance properties of the HR model. Introducing a reset mechanism after a threshold crossing, we turn this system into a resonant integrate-and-fire (RIF) model. Using Monte-Carlo simulations and mathematical analysis, we examine the effects of noise and the subthreshold dynamic properties of the RIF model on the occurrence of coherence resonance (CR). Synchronized burst firing occurs in a network of such model neurons with excitatory pulse-coupling. The coherence level of the network oscillations shows a stochastic resonance-like dependence on the noise level. Stochastic analysis of the equations shows that the slow recovery from the spike-induced inhibition is crucial in determining the frequencies of the CR and the subthreshold resonance in the original HR model. In this particular type of CR, the oscillation frequency strongly depends on the intrinsic time scales but changes little with the noise intensity. We give analytical quantities to describe this CR mechanism and illustrate its influence on the emerging network oscillations. We discuss the profound physiological roles this kind of CR may have in information processing in neurons possessing a subthreshold resonant frequency and in generating synchronized network oscillations with a frequency that is determined by intrinsic properties of the neurons.

Scaling properties for a classical particle in a time-dependent potential well

Some scaling properties for a classical particle interacting with a time-dependent square-well potential are studied. The corresponding dynamics is obtained by use of a two-dimensional nonlinear area-preserving map. We describe dynamics within the chaotic sea by use of a scaling function for the variance of the average energy, thereby demonstrating that the critical exponents are connected by an analytic relationship.

Suppression of noise in nonlinear systems subjected to strong periodic driving

An overdamped Brownian motion in "quartic" potential subjected to periodic driving has been considered. This system for the case of a weak periodic driving has been intensively studied during past decade within the context of stochastic resonance. It has been demonstrated that for the case of predominantly suprathreshold driving (the driving amplitude is significantly larger than the static threshold) the noise in the output signal is strongly suppressed at a certain frequency range: the signal-to-noise ratio demonstrates resonant behavior as a function of frequency.

Diffusion through piecewise washboard potential

We consider the influence of small periodic oscillations of barriers on the stationary motion of a particle through a piecewise washboard potential. Up to the second order in the amplitude of oscillation the corrections to the flux can be both positive and negative and, for equal widths of the well and barrier, they do not depend on the frequency of the oscillations.

Markov analysis of stochastic resonance in a periodically driven integrate-and-fire neuron

We model the dynamics of the leaky integrate-and-fire neuron under periodic stimulation as a Markov process with respect to the stimulus phase. This avoids the unrealistic assumption of a stimulus reset after each spike made in earlier papers and thus solves the long-standing reset problem. The neuron exhibits stochastic resonance, both with respect to input noise intensity and stimulus frequency. The latter resonance arises by matching the stimulus frequency to the refractory time of the neuron. The Markov approach can be generalized to other periodically driven stochastic processes containing a reset mechanism.