title: Noise correlation length effects on a Morris-Lecar neural network.
abstract: The role of spatially correlated stochastic perturbations on a Morris-Lecar neural network subject to an aperiodic subthreshold signal is analyzed in detail. Our results suggest that optimum signal-to-noise ratios can be obtained for two critical noise intensities due to the interplay of the subthreshold Poisson process and the correlated Gaussian forcing. For the second peak, most of the cells are periodically excited, the information transfer is enhanced, and a collective behavior develops measured in terms of the averaged activity of the network. The maximum signal-to-noise ratio increases with the correlation length, although it saturates for global coupling. It was found that there is a range of mean frequencies of the subthreshold signal that increases the signal-to-noise ratio output.
authors.affiliation: Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, Spain.
authors.forename: N
authors.initials: N
authors.lastname: Montejo
authors.forename: M N
authors.initials: MN
authors.lastname: Lorenzo
authors.forename: V
authors.initials: V
authors.lastname: Pérez-Villar
authors.forename: V
authors.initials: V
authors.lastname: Pérez-Muñuzuri
mesh_terms: D000818:Animals; D016477:Artifacts; D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D009415:Nerve Net; D009474:Neurons / Q000378:metabolism; D016011:Normal Distribution; D016012:Poisson Distribution; D013269:Stochastic Processes; D013997:Time Factors