Vitality of Neural Networks under Reoccurring Catastrophic Failures

Shira Sardi, Amir Goldental, Hamutal Amir, Roni Vardi, Ido Kanter

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Catastrophic failures are complete and sudden collapses in the activity of large networks such as economics, electrical power grids and computer networks, which typically require a manual recovery process. Here we experimentally show that excitatory neural networks are governed by a non-Poissonian reoccurrence of catastrophic failures, where their repetition time follows a multimodal distribution characterized by a few tenths of a second and tens of seconds timescales. The mechanism underlying the termination and reappearance of network activity is quantitatively shown here to be associated with nodal time-dependent features, neuronal plasticity, where hyperactive nodes damage the response capability of their neighbors. It presents a complementary mechanism for the emergence of Poissonian catastrophic failures from damage conductivity. The effect that hyperactive nodes degenerate their neighbors represents a type of local competition which is a common feature in the dynamics of real-world complex networks, whereas their spontaneous recoveries represent a vitality which enhances reliable functionality.

Original languageEnglish
Article number31674
JournalScientific Reports
StatePublished - 17 Aug 2016

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© The Author(s) 2016.


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