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Go Uchida

Publications and source records attributed to Go Uchida.

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First passage time properties of diffusion with a broad class of stochastic diffusion coefficients

This study investigates the first passage time (FPT) properties of particles with a broad class of positive stochastic diffusion coefficients (DCs), representing diffusion in heterogeneous environments or of particles with conformational fluctuations. We demonstrate that for diffusion in a one-dimensional semi-infinite domain with an absorbing boundary, particles will eventually reach the absorbing boundary with probability one. We also show that a stochastic DC provides higher transport efficiency in an early arrival of particles at the boundary than would be expected under diffusion whose DC is the ensemble average of the stochastic DC. Furthermore, a stochastic DC with a larger supremum exhibits a more efficient transport even if ensemble averages are the same. For ergodic DCs, we show three more properties: the mean FPT diverges, the enhancement of early-arrival efficiency diminishes over long times, and the FPT distribution converges to a L\'evy-Smirnov distribution in the long-time limit. These properties are shown to arise from the convergence of the time-averaged DC to the ensemble average, with the convergence speed determined by the DC's fluctuation time scale. We finally discuss the similarities and differences of FPT properties between three-dimensional diffusion outside a spherical absorbing boundary and the one-dimensional diffusion. Our results indicate that fluctuations in DCs may need to be non-Markov and/or non-ergodic to allow efficient transport of particles to distant targets. Our results also suggest that fluctuations in a DC play an important role, for example, in diffusion-limited reactions triggered by single molecules in physics, chemistry, or biology.

cond-mat.stat-mech

Coherence resonance for time-averaged measures

Noise can induce time order in the dynamics of nonlinear dynamical systems. For example, coherence resonance occurs in various neuron models driven by a noise. In studies of coherence resonance, ensemble-averaged measures of the coherence are often used. In the present study, we examine coherence resonance for time-averaged measures. For the examination, we use a Hodgkin-Huxley neuron model driven by a constant current and a noise. We firstly show that for large times, the neuron is in a stationary state irrespective of initial conditions of the neuron. We then show numerical evidence that in the stationary state, a given noise sample path uniquely determines the dynamics of the neuron. We then present numerical evidence suggesting that time-averaged coherence measures of the dynamics is independent of noise sample paths and is equal to ensemble-averaged coherence measures. On the basis of this property, we show that coherence resonance is not only a phenomenon related to ensemble-averaged measures but also a phenomenon that holds for time-averaged measures.

nlin.AO

Diffusion with a broad class of stochastic diffusion coefficients

In many physical or biological systems, diffusion can be described by Brownian motions with stochastic diffusion coefficients (DCs). In the present study, we investigate properties of the diffusion with a broad class of stochastic DCs with a novel approach. We show that for a finite time, the propagator is non-Gaussian and heavy-tailed. This means that when the mean square displacements are the same, for a finite time, some of the diffusing particles with stochastic DCs diffuse farther than the particles with deterministic DCs or exhibiting a fractional Brownian motion. We also show that when a stochastic DC is ergodic, the propagator converges to a Gaussian distribution in the long time limit. The speed of convergence is determined by the autocovariance function of the DC.

cond-mat.stat-mech