Complex intermittency blurred by noise: theory and application to neural dynamics (Articolo in rivista)

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Label
  • Complex intermittency blurred by noise: theory and application to neural dynamics (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1103/PhysRevE.82.015103 (literal)
Alternative label
  • Allegrini P.; Menicucci D.; Bedini R.; Gemignani A.; Paradisi P. (2010)
    Complex intermittency blurred by noise: theory and application to neural dynamics
    in Physical review. E, Statistical, nonlinear and soft matter physics (Online)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Allegrini P.; Menicucci D.; Bedini R.; Gemignani A.; Paradisi P. (literal)
Pagina inizio
  • 015103-1 (literal)
Pagina fine
  • 015103-4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://pre.aps.org/abstract/PRE/v82/i1/e015103 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 82 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Physical Review E, vol. 82 article n. 015103(R). The American Physical Society, 2010. (literal)
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  • 4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy Centro EXTREME, Scuola Superiore Sant'Anna, P.zza Martiri della Libertà 7, 56127 Pisa, Italy Dipartmento di Scienze Fisiologiche, Università di Pisa, Via San Zeno 31, 56127 Pisa, Italy Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" (ISTI-CNR), Via Moruzzi 1, 56124 Pisa, Italy (literal)
Titolo
  • Complex intermittency blurred by noise: theory and application to neural dynamics (literal)
Abstract
  • We propose a model for the passage between metastable states of mind dynamics. As changing points we use the rapid transition processes simultaneously detectable in EEG signals related to different cortical areas. Our model consists of a non-Poissonian intermittent process, which signals that the brain is in a condition of complexity, upon which a Poisson process is superimposed. We provide an analytical solution for the waiting- time distribution for the model, which is well obeyed by physiological data. Although the role of the Poisson process remains unexplained, the model is able to reproduce many behaviors reported in literature, although they seem contradictory. (literal)
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