  We consider a fractional Ornstein-Uhlenbeck process involving a stochastic
forcing term in the drift, as a solution of a linear stochastic differential
equation driven by a fractional Brownian motion. For such process we specify
mean and covariance functions, concentrating on their asymptotic behavior. This
gives us a sort of short- or long-range dependence, under specified hypotheses
on the covariance of the forcing process. Applications of this process in
neuronal modeling are discussed, providing an example of a stochastic forcing
term as a linear combination of Heaviside functions with random center.
Simulation algorithms for the sample path of this process are finally given.
