title: On a non-Markov neuronal model and its approximations.
abstract: Single neuron's activity modeling is considered with reference to some earlier contributions in which a non-Markov Gaussian process is assumed to describe the time course of the neuron's membrane potential. After re-formulating the problem in a rigorous framework and pinpointing the limits of validity of such a model, the available results on the firing probability density are compared with those obtained by us by means of an ad hoc numerical algorithm implemented for the leaky integrator diffusion firing model and with some data constructed by a simulation procedure of non-Markov Gaussian processes with pre-assigned covariances. Throughout this paper, the notion of 'correlation time' plays a fundamental role for the neuronal coding process modeling.
authors.affiliation: Dipartimento di Matematica, University of Basilicata, Potenza, Italy.
authors.forename: E
authors.initials: E
authors.lastname: Di Nardo
authors.forename: A G
authors.initials: AG
authors.lastname: Nobile
authors.forename: E
authors.initials: E
authors.lastname: Pirozzi
authors.forename: L M
authors.initials: LM
authors.lastname: Ricciardi
mesh_terms: D008390:Markov Chains; D008954:Models, Biological; D009474:Neurons / Q000502:physiology*