10,662 research outputs found
Averaging approach to phase coherence of uncoupled limit-cycle oscillators receiving common random impulses
Populations of uncoupled limit-cycle oscillators receiving common random
impulses show various types of phase-coherent states, which are characterized
by the distribution of phase differences between pairs of oscillators. We
develop a theory to predict the stationary distribution of pairwise phase
difference from the phase response curve, which quantitatively encapsulates the
oscillator dynamics, via averaging of the Frobenius-Perron equation describing
the impulse-driven oscillators. The validity of our theory is confirmed by
direct numerical simulations using the FitzHugh-Nagumo neural oscillator
receiving common Poisson impulses as an example
A comparative study of two stochastic mode reduction methods
We present a comparative study of two methods for the reduction of the
dimensionality of a system of ordinary differential equations that exhibits
time-scale separation. Both methods lead to a reduced system of stochastic
differential equations. The novel feature of these methods is that they allow
the use, in the reduced system, of higher order terms in the resolved
variables. The first method, proposed by Majda, Timofeyev and Vanden-Eijnden,
is based on an asymptotic strategy developed by Kurtz. The second method is a
short-memory approximation of the Mori-Zwanzig projection formalism of
irreversible statistical mechanics, as proposed by Chorin, Hald and Kupferman.
We present conditions under which the reduced models arising from the two
methods should have similar predictive ability. We apply the two methods to
test cases that satisfy these conditions. The form of the reduced models and
the numerical simulations show that the two methods have similar predictive
ability as expected.Comment: 35 pages, 6 figures. Under review in Physica
Noise-Induced Synchronization and Clustering in Ensembles of Uncoupled Limit-Cycle Oscillators
We study synchronization properties of general uncoupled limit-cycle
oscillators driven by common and independent Gaussian white noises. Using phase
reduction and averaging methods, we analytically derive the stationary
distribution of the phase difference between oscillators for weak noise
intensity. We demonstrate that in addition to synchronization, clustering, or
more generally coherence, always results from arbitrary initial conditions,
irrespective of the details of the oscillators.Comment: 6 pages, 2 figure
Spatio-temporal Bounded Noises, and transitions induced by them in solutions of real Ginzburg-Landau model
In this work, we introduce two spatio-temporal colored bounded noises, based
on the zero-dimensional Cai-Lin and Tsallis-Borland noises. We then study and
characterize the dependence of the defined bounded noises on both a temporal
correlation parameter and on a spatial coupling parameter . The
boundedness of these noises has some consequences on their equilibrium
distributions. Indeed in some cases varying may induce a transition
of the distribution of the noise from bimodality to unimodality. With the aim
to study the role played by bounded noises on nonlinear dynamical systems, we
investigate the behavior of the real Ginzburg-Landau time-varying model
additively perturbed by such noises. The observed phase transitions
phenomenology is quite different from the one observed when the perturbations
are unbounded. In particular, we observed an inverse "order-to-disorder"
transition, and a re-entrant transition, with dependence on the specific type
of bounded noise.Comment: 12 (main text)+5 (supplementary) page
The space-clamped Hodgkin-Huxley system with random synaptic input: inhibition of spiking by weak noise and analysis with moment equations
We consider a classical space-clamped Hodgkin-Huxley model neuron stimulated
by synaptic excitation and inhibition with conductances represented by
Ornstein-Uhlenbeck processes. Using numerical solutions of the stochastic model
system obtained by an Euler method, it is found that with excitation only there
is a critical value of the steady state excitatory conductance for repetitive
spiking without noise and for values of the conductance near the critical value
small noise has a powerfully inhibitory effect. For a given level of inhibition
there is also a critical value of the steady state excitatory conductance for
repetitive firing and it is demonstrated that noise either in the excitatory or
inhibitory processes or both can powerfully inhibit spiking. Furthermore, near
the critical value, inverse stochastic resonance was observed when noise was
present only in the inhibitory input process.
The system of 27 coupled deterministic differential equations for the
approximate first and second order moments of the 6-dimensional model is
derived. The moment differential equations are solved using Runge-Kutta methods
and the solutions are compared with the results obtained by simulation for
various sets of parameters including some with conductances obtained by
experiment on pyramidal cells of rat prefrontal cortex. The mean and variance
obtained from simulation are in good agreement when there is spiking induced by
strong stimulation and relatively small noise or when the voltage is
fluctuating at subthreshold levels. In the occasional spike mode sometimes
exhibited by spinal motoneurons and cortical pyramidal cells the assunptions
underlying the moment equation approach are not satisfied
Early-Warning Signs for Pattern-Formation in Stochastic Partial Differential Equations
There have been significant recent advances in our understanding of the
potential use and limitations of early-warning signs for predicting drastic
changes, so called critical transitions or tipping points, in dynamical
systems. A focus of mathematical modeling and analysis has been on stochastic
ordinary differential equations, where generic statistical early-warning signs
can be identified near bifurcation-induced tipping points. In this paper, we
outline some basic steps to extend this theory to stochastic partial
differential equations with a focus on analytically characterizing basic
scaling laws for linear SPDEs and comparing the results to numerical
simulations of fully nonlinear problems. In particular, we study stochastic
versions of the Swift-Hohenberg and Ginzburg-Landau equations. We derive a
scaling law of the covariance operator in a regime where linearization is
expected to be a good approximation for the local fluctuations around
deterministic steady states. We compare these results to direct numerical
simulation, and study the influence of noise level, noise color, distance to
bifurcation and domain size on early-warning signs.Comment: Published in Communications in Nonlinear Science and Numerical
Simulation (2014
Colored noise in oscillators. Phase-amplitude analysis and a method to avoid the Ito-Stratonovich dilemma
We investigate the effect of time-correlated noise on the phase fluctuations
of nonlinear oscillators. The analysis is based on a methodology that
transforms a system subject to colored noise, modeled as an Ornstein-Uhlenbeck
process, into an equivalent system subject to white Gaussian noise. A
description in terms of phase and amplitude deviation is given for the
transformed system. Using stochastic averaging technique, the equations are
reduced to a phase model that can be analyzed to characterize phase noise. We
find that phase noise is a drift-diffusion process, with a noise-induced
frequency shift related to the variance and to the correlation time of colored
noise. The proposed approach improves the accuracy of previous phase reduced
models
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