41 research outputs found
Effective phase description of noise-perturbed and noise-induced oscillations
An effective description of a general class of stochastic phase oscillators
is presented. For this, the effective phase velocity is defined either by
invariant probability density or via first passage times. While the first
approach exhibits correct frequency and distribution density, the second one
yields proper phase resetting curves. Their discrepancy is most pronounced for
noise-induced oscillations and is related to non-monotonicity of the phase
fluctuations
Reconstruction of a scalar voltage-based neural field network from observed time series
We present a general method for reconstruction of a network of nonlinearly
coupled neural fields from the observations. A prominent example of such a
system is a dynamical random neural network model studied by Sompolinsky et. al
[Phys. Rev. Lett., v. 61, 259 (1988)]. We develop a technique for inferring the
properties of the system from the observations of the chaotic voltages. Only
the structure of the model is assumed to be known, while the nonlinear gain
functions of the interactions, the matrix of the coupling constants, and the
time constants of the local dynamics are reconstructed from the time series.Comment: 5 page