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 …

Motivated by recent experiments, we consider the hydrodynamic capture of a microswimmer
near a stationary spherical obstacle. Simulations of model equations show that a swimmer …

The first analytical stochastic and dynamic model for optimizing transit service switching is
proposed for “smart transit” applications and for operating shared autonomous transit fleets …

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical
tractability. They have been widely applied to gain understanding of the underlying …

We derive general bounds on the probability that the empirical first-passage time τ n≡∑ i=
1 n τ i/n of a reversible ergodic Markov process inferred from a sample of n independent …

Understanding the timing of the peak of a disease outbreak forms an important part of
epidemic forecasting. In many cases, such information is essential for planning increased …

Abstract The Ornstein-Uhlenbeck process has been proposed as a model for the
spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the …

Stochastic differential equation (SDE) models of nerve cells for the most part neglect the
spatial dimension. Including the latter leads to stochastic partial differential equations …

Neurons temporally integrate input signals, translating them into timed output spikes.
Because neurons nonperiodically emit spikes, examining spike timing can reveal …