Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for
studying properties of real neuronal systems. Experimental data of frequently sampled …

Abstract Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with
experimental data are important to understand the neural code and neural synchronization …

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

This brief presents a general framework for the continuous-time nonlinear event-based state
estimation problem. Using the information from observations made by event-based …

We consider the first-passage time problem for the Feller-type diffusion process, having
infinitesimal drift B 1 (x, t)= α (t) x+ β (t) and infinitesimal variance B 2 (x, t)= 2 r (t) x, defined …

Estimation of parameters in stochastic processes has been thoroughly investigated for
decades and the asymptotic properties of the estimators are known. However, reaching the …

Napjainkban a pénzügyi intézmények, a szabályozás követelményeinek és a belső
motivációs erőknek köszönhetően, egyre intenzívebben foglalkoznak kockázataikkal. Az …

Abstract An Ornstein-Uhlenbeck process is the most basic mean-reversion model and has
been used in various fields such as finance and biology. In some instances, reflecting …

We consider the fixed and exponential time-stepping Euler algorithms, with boundary tests,
to calculate the mean first exit times (MFET) of two one-dimensional neural diffusion models …

A fundamental question concerning the way the visual world is represented in our brain is
how a cortical cell responds when its classical receptive field contains a plurality of stimuli …