A timestepper-based approach for the coarse-grained analysis of microscopic neuronal simulators on networks: Bifurcation and rare-events micro- to macro-computations

Spiliotis, KG; Siettos, CI

dc.contributor.author	Spiliotis, KG	en
dc.contributor.author	Siettos, CI	en
dc.date.accessioned	2014-03-01T01:35:03Z
dc.date.available	2014-03-01T01:35:03Z
dc.date.issued	2011	en
dc.identifier.issn	0925-2312	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20954
dc.subject	Bifurcation analysis	en
dc.subject	Complex networks	en
dc.subject	Complex systems	en
dc.subject	Degree distribution	en
dc.subject	Equation Free computations	en
dc.subject	Multi-scale dynamics	en
dc.subject	Neuronal models	en
dc.subject	Nonlinear dynamics	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Bifurcation analysis	en
dc.subject.other	Complex networks	en
dc.subject.other	Degree distributions	en
dc.subject.other	Equation-Free	en
dc.subject.other	Multi-scale dynamics	en
dc.subject.other	Neuronal model	en
dc.subject.other	Non-linear dynamics	en
dc.subject.other	Bifurcation (mathematics)	en
dc.subject.other	Dynamics	en
dc.subject.other	Large scale systems	en
dc.subject.other	Stochastic models	en
dc.subject.other	article	en
dc.subject.other	data extraction	en
dc.subject.other	mathematical computing	en
dc.subject.other	mathematical model	en
dc.subject.other	nerve cell	en
dc.subject.other	nerve cell network	en
dc.subject.other	priority journal	en
dc.subject.other	simulator	en
dc.subject.other	stochastic model	en
dc.title	A timestepper-based approach for the coarse-grained analysis of microscopic neuronal simulators on networks: Bifurcation and rare-events micro- to macro-computations	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.neucom.2011.06.018	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.neucom.2011.06.018	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	We show how the Equation Free approach for multi-scale computations can be exploited to extract, in a computational rigorous and systematic way the emergent dynamical attributes, from detailed large-scale microscopic stochastic models of neurons that interact on complex networks. In particular we show how bifurcation, stability analysis and estimation of mean appearance times of rare events can be derived bypassing the need for obtaining analytical approximations, providing an "on-demand" model reduction with respect to the underlying degree distribution. (C) 2011 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Neurocomputing	en
dc.identifier.doi	10.1016/j.neucom.2011.06.018	en
dc.identifier.isi	ISI:000296212400089	en
dc.identifier.volume	74	en
dc.identifier.issue	17	en
dc.identifier.spage	3576	en
dc.identifier.epage	3589	en