The breaking of a delayed ring neural network contributes to stability: The rule and exceptions

Khokhlova T.N.; Kipnis M.M.

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dc.contributor.author	Khokhlova T.N.	en
dc.contributor.author	Kipnis M.M.	en
dc.date.accessioned	2018-06-18T09:24:17Z
dc.date.available	2018-06-18T09:24:17Z
dc.date.issued	2013
dc.identifier.issn	8936080
dc.identifier.uri	http://dspace.susu.ru/handle/0001.74/19662
dc.description.abstract	We prove that in our mathematical model the breaking of a delayed ring neural network extends the stability region in the parameters space, if the number of the neurons is sufficiently large. If the number of neurons is small, then a "paradoxical" region exists in the parameters space, wherein the ring neural configuration is stable, while the linear one is unstable. We study the conditions under which the paradoxical region is nonempty. We discuss how our mathematical modeling reflects neurosurgical operations with the severing of particular connections in the brain. © 2013 Elsevier Ltd.	en]
dc.language.iso	English
dc.relation.ispartof	Neural Networks	en]
dc.subject	Linear and ring neural configuration	en]
dc.subject	Ring neural networks	en]
dc.subject	Stability cone	en]
dc.subject	Stability regions	en]
dc.subject	Convergence of numerical methods	en]
dc.subject	Mathematical models	en]
dc.subject	Time delay	en]
dc.subject	Neural networks	en]
dc.subject	article	en]
dc.subject	artificial neural network	en]
dc.subject	mathematical analysis	en]
dc.subject	mathematical model	en]
dc.subject	nerve cell	en]
dc.subject	neurosurgery	en]
dc.subject	priority journal	en]
dc.subject	Linear and ring neural configuration	en]
dc.subject	Neural networks	en]
dc.subject	Stability	en]
dc.subject	The stability cone	en]
dc.subject	Time delay	en]
dc.subject	Algorithms	en]
dc.subject	Brain	en]
dc.subject	Humans	en]
dc.subject	Models, Neurological	en]
dc.subject	Models, Statistical	en]
dc.subject	Neural Networks (Computer)	en]
dc.subject	Neurons	en]
dc.subject	Neurosurgery	en]
dc.subject	Nonlinear Dynamics	en]
dc.title	The breaking of a delayed ring neural network contributes to stability: The rule and exceptions	en
dc.type	Article	en]
dc.identifier.doi	10.1016/j.neunet.2013.08.001
dc.identifier.scopus	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883750292&doi=10.1016%2fj.neunet.2013.08.001&partnerID=40&md5=a8d8e6e2f3ae6e4ac76174e48b36e7b3