Complex self-sustained oscillation patterns in modular excitable networks
arXiv:1807.08849 · doi:10.1103/PhysRevE.98.042308
Abstract
We study the relationship between the modularity of scale-free excitable networks and their ability to support self-sustained oscillation patterns. We find that the probability for a network of given degree-distribution exponent to be able to support self-sustained oscillations is strongly affected by its modularity. In addition, both high- and low-modularity networks are more likely to exhibit long-period oscillation patterns than those with intermediate modularity, but the degrees of complexity and correlation in these two cases are different. The long-period oscillations cannot be explained by a minimum-length Winfree loop, but instead arise from the interplay between two or more propagating waves. Finally, we introduce a new method that can be used to analyze the structure of the self-sustained oscillation sources at different levels of detail and show that the period of the oscillation pattern is statistically correlated with the fraction of modules that are part of the oscillation source.
arXiv comment9 pages, 8 figures
References in corpus (11)
- Finding and evaluating community structure in networks
- Modularity and community structure in networks
- Community detection in graphs
- Fast algorithm for detecting community structure in networks
- Finding community structure in networks using the eigenvectors of matrices
- Benchmark graphs for testing community detection algorithms
- Self-sustained activity in a small-world network of excitable neurons
- Structure and control of self-sustained target waves in excitable small-world networks
- Emergence of self-sustained patterns in small-world excitable media
- Many Attractors, Long Chaotic Transients, and Failure in Small-World Networks of Excitable Neurons
- Pattern formation in oscillatory complex networks consisting of excitable nodes