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Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity

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dc.contributor.author Piccinini, Juan
dc.contributor.author Perez Ipiña, Ignacio
dc.contributor.author Laufs, Helmut
dc.contributor.author Kringelbach, Morten L.
dc.contributor.author Deco, Gustavo
dc.contributor.author Sanz Perl, Yonatan
dc.contributor.author Tagliazucchi, Enzo
dc.date.accessioned 2021-05-10T10:09:15Z
dc.date.available 2021-05-10T10:09:15Z
dc.date.issued 2021
dc.identifier.citation Piccinini J, Perez Ipiñna I, Laufs H, Kringelbach M, Deco G, Sanz Perl Y, Tagliazucchi E. Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity. Chaos. 2021 Feb 17;31:023128. DOI: 10.1063/5.0025543
dc.identifier.issn 1054-1500
dc.identifier.uri http://hdl.handle.net/10230/47376
dc.description.abstract An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart–Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifested in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constrain the future development of biophysically realistic large-scale models.
dc.description.sponsorship This work was supported by funding from the Agencia Nacional De Promocion Cientifica Y Tecnologica (Argentina) (Grant No. PICT-2018-03103). The authors acknowledge the Toyoko 2020 program for granting cloud computing services.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher American Institute of Physics (AIP)
dc.relation.ispartof Chaos. 2021 Feb 17;31:023128
dc.relation.isreferencedby https://figshare.com/articles/dataset/Awake_Time_Series/12814250/1
dc.rights © American Institute of Physics. The following article appeared in Piccinini J, Perez Ipiñna I, Laufs H, Kringelbach M, Deco G, Sanz Perl Y, Tagliazucchi E. Chaos. 023128, 2021 and may be found at https://aip.scitation.org/doi/10.1063/5.0025543
dc.title Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1063/5.0025543
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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