Mathematical models are an important tool for neuroscientists. During the last 30 years
many papers have appeared on single neuron description and specifically on stochastic …

Nonlinear Wiener processes have been widely applied for degradation analysis in practical
engineering. To improve the accuracy of failure time distribution approximation itself, a novel …

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals,
where the decision maker accumulates evidence until the process hits either an upper or …

Diffusion models are widely-used and successful accounts of the time course of two-choice
decision making. Most diffusion models assume constant boundaries, which are the …

In this article, we summarize the main parts of the first exit time theory developed in
connection to the life table data and the resulting theoretical and applied issues. New tools …

Point processes often have a natural interpretation with respect to a continuous process. We
propose a point process construction that describes arrival time observations in terms of the …

Given a two-dimensional correlated diffusion process, we determine the joint density of the
first passage times of the process to some constant boundaries. This quantity depends on …

We solve the problem of optimal stopping of a Brownian motion subject to the constraint that
the stopping time's distribution is a given measure consisting of finitely many atoms. In …

Weexplore a method for finding the State H (t) of a Stochastic System when the first exit time
or hitting time probability density function g (t) is given. The task is to find an analytic form for …

We prove existence and uniqueness for the inverse-first-passage time problem for soft-killed
Brownian motion using rather elementary methods relying on basic results from probability …