ArticleScientific reports2017
Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- A statistical analysis method for probability distributions in Erdös-Rényi random networks with preferential cutting-rewiring operation.Frontiers in network physiology · 2024Article
- Spatial distribution of heterogeneity as a modulator of collective dynamics in pancreatic beta-cell networks and beyond.Frontiers in network physiology · 2023Article
- Two classes of functional connectivity in dynamical processes in networks.Journal of the Royal Society, Interface · 2021Article
- Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation.NPJ systems biology and applications · 2020Article
- Topological reinforcement as a principle of modularity emergence in brain networks.Network neuroscience (Cambridge, Mass.) · 2019Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.
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Registered trials
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