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• W2416392502 abstract "We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on tree--like structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly-weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution), and show that it does facilitate the self-sustained activity." @default.
• W2416392502 created "2016-06-24" @default.
• W2416392502 creator A5046444281 @default.
• W2416392502 creator A5054875221 @default.
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• W2416392502 date "2015-12-22" @default.
• W2416392502 modified "2023-09-24" @default.
• W2416392502 title "Memory-induced mechanism for self-sustaining activity in networks" @default.
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• W2416392502 doi "https://doi.org/10.1103/physreve.92.062824" @default.
• W2416392502 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26764761" @default.
• W2416392502 hasPublicationYear "2015" @default.
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