Measuring multiple spike train synchrony (Articolo in rivista)

Type
Label
  • Measuring multiple spike train synchrony (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.jneumeth.2009.06.039 (literal)
Alternative label
  • Thomas Kreuz; Daniel Chicharro; Ralph G. Andrzejak; Julie S. Haas; Henry D.I. Abarbanel (2009)
    Measuring multiple spike train synchrony
    in Journal of neuroscience methods
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Thomas Kreuz; Daniel Chicharro; Ralph G. Andrzejak; Julie S. Haas; Henry D.I. Abarbanel (literal)
Pagina inizio
  • 287 (literal)
Pagina fine
  • 299 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.sciencedirect.com/science/article/pii/S0165027009003616 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 183 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • fasc. (2). Elsevier. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 13 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Institute for Nonlinear Sciences, University of California, San Diego, CA, USA Institute for Complex Systems, CNR, Sesto Fiorentino, Italy Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain Center for Brain Science, Harvard University, MA, USA Department of Physics and Marine Physical Labaratory (Scripps Institution of Oceanography), University of California, San Diego, CA, USA (literal)
Titolo
  • Measuring multiple spike train synchrony (literal)
Abstract
  • Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh–Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods. (literal)
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