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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 |