Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloExtraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.
Anno di pubblicazione2009
Autore/iR. Storchi; G. E. M. Biella; D. Liberati; G. Baselli
Affiliazioni autori1. Department of Biomedical Sciences, University of Modena, Modena 2. Institute of Molecular Bioimaging and Physiology, Milan 3. National Research Council, Institute of Molecular Bioimaging and Physiology, Segrate (MI) 4. Department of Electronic and Information, National Research Council, Politechnic School of Milan, Milano 5. Department of Biomedical Engineering, Politechnic School of Milan, Milano
Autori CNR e affiliazioni
  • inglese
AbstractBACKGROUND: Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. METHODOLOGY/PRINCIPAL FINDINGS: Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time. CONCLUSIONS/SIGNIFICANCE: We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine dae4299
Pagine a-
Pagine totali13
RivistaPloS one
Attiva dal 2006
Editore: Public Library of Science - San Francisco, CA
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1932-6203
Titolo chiave: PloS one
Titolo proprio: PloS one
Titolo abbreviato: PLoS ONE
Titoli alternativi:
  • Public Library of Science one
  • PLoS 1
Numero volume della rivista4
Fascicolo della rivista-
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000265483000008)
  • Scopus (Codice:2-s2.0-59549084938)
  • PubMed (Codice:19173006)
Parole chiave-
Link (URL, URI)-
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-
    • Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

    Dati associati a vecchie tipologie
    I dati associati a vecchie tipologie non sono modificabili, derivano dal cambiamento della tipologia di prodotto e hanno solo valore storico.

    Dati storici
    I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
    Area disciplinareComputer Science & Engineering
    Area valutazione CIVRIngegneria industriale e informatica
    Rivista ISIPLOS ONE [00098NN]
    NoteCorresponding author: R. Storchi ( IF 2009: 4.351 disponibile elettronicamente: (doi:10.1371/journal.pone.0004299)