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 …

Parameters in diffusion neuronal models are divided into two groups; intrinsic and input
parameters. Intrinsic parameters are related to the properties of the neuronal membrane and …

A stochastic model of solid tumor growth based on deterministic Gompertz law is presented.
Tumor cells evolution is described by a one-dimensional diffusion process limited by two …

Neuronal adaptation as well as interdischarge interval correlations have been shown to be
functionally important properties of physiological neurons. We explore the dynamics of a …

For the study of the behavior of noisy neuronal models, Rodriguez and Tuckwell have
introduced an elegant and systematic method which consists of replacing the system of …

A reliable Monte Carlo method for the evaluation of first passage times of diffusion
processes through boundaries is proposed. A nested algorithm that simulates the first …

We propose an approach to compute the boundary crossing probabilities for a class of
diffusion processes which can be expressed as piecewise monotone (not necessarily one-to …

We consider the logistic growth model and analyze its relevant properties, such as the limits,
the monotony, the concavity, the inflection point, the maximum specific growth rate, the lag …

Many phenomena in nature are described by excitable systems driven by colored noise. The
temporal correlations in the fluctuations hinder an analytical treatment. We here present a …

The stochastic Feller neuronal model is studied, and estimators of the model input
parameters, depending on the firing regime of the process, are derived. Closed expressions …