Fractionally integrated Gauss-Markov processes and applications

We investigate the stochastic processes obtained as the fractional Riemann-Liouville inte-gral of order alpha is an element of (0 , 1) of Gauss-Markov processes. The general expressions of the mean, variance and covariance functions are given. Due to the central role, for the fractional inte-gral of standard Brownian motion and of the non-stationary/stationary Ornstein-Uhlenbeck processes, the covariance functions are carried out in closed-form. In order to clarify how the fractional order parameter alpha affects these functions, their numerical evaluations are shown and compared also with those of the corresponding processes obtained by ordi-nary Riemann integral. The results are useful for fractional neuronal models with long range memory dynamics and involving correlated input processes. The simulation of these fractionally integrated processes can be performed starting from the obtained covariance functions. A suitable neuronal model is proposed. Graphical comparisons are provided and discussed.