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Updated: May 2, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Noise induced complexity: patterns and collective phenomena in a small-world neuronal network
Yanhong Zheng1, Qingyun Wang2, Marius-F Danca3
1Department of Dynamics and Control, Beihang University, Beijing, 100191 China ; School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350007 China.
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.
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