Resting brains never rest: computational insights into potential cognitive architectures
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Deco, Gustavoca
- dc.contributor.author Jirsa, Viktor K.ca
- dc.contributor.author McIntosh, Arthur C.ca
- dc.date.accessioned 2016-07-19T07:36:34Z
- dc.date.available 2016-07-19T07:36:34Z
- dc.date.issued 2013ca
- dc.description.abstract Resting-state networks (RSNs), which have become a main focus in neuroimaging research, can be best simulated by large-scale cortical models in which networks teeter on the edge of instability. In this state, the functional networks are in a low firing stable state while they are continuously pulled towards multiple other configurations. Small extrinsic perturbations can shape task-related network dynamics, whereas perturbations from intrinsic noise generate excursions reflecting the range of available functional networks. This is particularly advantageous for the efficiency and speed of network mobilization. Thus, the resting state reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time and space. In this article, we propose a new theoretical framework for RSNs that can serve as a fertile ground for empirical testing.
- dc.format.mimetype application/pdfca
- dc.identifier.citation Deco G, Jirsa VK, McIntosh AC. Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci. 2013;36(5):268-74. DOI: 10.1016/j.tins.2013.03.001ca
- dc.identifier.doi http://dx.doi.org/10.1016/j.tins.2013.03.001
- dc.identifier.issn 0166-2236ca
- dc.identifier.uri http://hdl.handle.net/10230/27081
- dc.language.iso engca
- dc.publisher Elsevierca
- dc.relation.ispartof Trends in Neurosciences. 2013;36(5):268-74
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129ca
- dc.rights © Elsevier http://dx.doi.org/10.1016/j.tins.2013.03.001ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.title Resting brains never rest: computational insights into potential cognitive architecturesca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/acceptedVersionca