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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.
Cognitive Neurodynamics
|March 14, 2014
Summary
Noise can surprisingly create ordered patterns in neuronal networks, enhancing temporal order and synchronization. An optimal network structure balances short and long-range connections for best results.
Area of Science:
- Computational Neuroscience
- Complex Systems
Background:
- Neuronal networks exhibit complex collective phenomena.
- Noise effects on network dynamics are crucial for understanding brain function.
Purpose of the Study:
- Investigate noise-induced patterns in small-world neuronal networks.
- Analyze the impact of network topology on spatiotemporal dynamics and synchronization.
Main Methods:
- Utilized a two-dimensional Rulkov map neuron model.
- Simulated small-world neuronal networks with varying noise levels and connection fractions.
Main Results:
- Intermediate noise levels induced spatially ordered patterns, demonstrating spatiotemporal coherence resonance.
- Small-world connectivity enhanced temporal order and synchronization.
- An optimal fraction of long-range connections was identified for maximizing temporal order and synchronization.
Conclusions:
- Noise can play a constructive role in neuronal network organization.
- Small-world network topology significantly influences network dynamics and collective behavior.
- Optimizing network structure is key to enhancing information processing in neuronal systems.
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