title: Noise induced complexity: patterns and collective phenomena in a small-world neuronal network.
abstract: The effects of noise on patterns and collective phenomena are studied in a small-world neuronal network with the dynamics of each neuron being described by a two-dimensional Rulkov map neuron. It is shown that for intermediate noise levels, noise-induced ordered patterns emerge spatially, which supports the spatiotemporal coherence resonance. However, the inherent long range couplings of small-world networks can effectively disrupt the internal spatial scale of the media at small fraction of long-range couplings. The temporal order, characterized by the autocorrelation of a firing rate function, can be greatly enhanced by the introduction of small-world connectivity. There exists an optimal fraction of randomly rewired links, where the temporal order and synchronization can be optimized.
authors.affiliation: Department of Dynamics and Control, Beihang University, Beijing, 100191 China ; School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350007 China.
authors.forename: Yanhong
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Department of Dynamics and Control, Beihang University, Beijing, 100191 China.
authors.forename: Qingyun
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Mathematics and Computer Science, Avram Iancu University, 400380 Cluj-Napoca, Romania ; Romanian Institute of Science and Technology, 400487 Cluj-Napoca, Romania.
authors.forename: Marius-F
authors.initials: MF
authors.lastname: Danca
keywords: Coherence resonance; Pattern; Small-world neural network; Synchronization