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• W2219596751 abstract "Journal of Integrative NeuroscienceVol. 14, No. 02, pp. 253-277 (2015) Research ArticlesNo AccessNonparametric directionality measures for time series and point process dataDavid M. HallidayDavid M. HallidayDepartment of Electronics, University of York, York, YO10 5DD, UK Search for more papers by this author https://doi.org/10.1142/S0219635215300127Cited by:17 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThe need to determine the directionality of interactions between neural signals is a key requirement for analysis of multichannel recordings. Approaches most commonly used are parametric, typically relying on autoregressive models. A number of concerns have been expressed regarding parametric approaches, thus there is a need to consider alternatives. We present an alternative nonparametric approach for construction of directionality measures for bivariate random processes. The method combines time and frequency domain representations of bivariate data to decompose the correlation by direction. Our framework generates two sets of complementary measures, a set of scalar measures, which decompose the total product moment correlation coefficient summatively into three terms by direction and a set of functions which decompose the coherence summatively at each frequency into three terms by direction: forward direction, reverse direction and instantaneous interaction. It can be undertaken as an addition to a standard bivariate spectral and coherence analysis, and applied to either time series or point-process (spike train) data or mixtures of the two (hybrid data). In this paper, we demonstrate application to spike train data using simulated cortical neurone networks and application to experimental data from isolated muscle spindle sensory endings subject to random efferent stimulation.Keywords:Directionalitycoherencenonparametrictime seriespoint processnetworksGranger causality References L. A. Baccala and K. Sameshima , Biol. Cybern. 84 , 463 ( 2001 ) . Crossref, Medline, ISI, Google Scholar M. S. Bartlett , Nature 161 , 686 ( 1948 ) . Crossref, ISI, Google Scholar P. Bloomfield , Fourier Analyis of Time Series — An Introduction ( John Wiley & Sons Inc , New York , 2002 ) . Google Scholar H. Bokil et al. , J. Neurosci. Methods 159 , 337 ( 2007 ) . Crossref, Medline, ISI, Google ScholarD. R. Brillinger, The spectral analysis of stationary interval functions, Proc. Sixth Berkeley Symp. Math. Statist. Probab, eds. L. M. LeCam, J. Neyman and E. Scott (1972) pp. 483–513. Google Scholar D. R. Brillinger , Proc. IEEE 62 , 1628 ( 1974 ) . 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Stevenson and Rob Mason1 Aug 2016 | Journal of Neuroscience Methods, Vol. 268 Recommended Vol. 14, No. 02 Metrics History Received 9 October 2014 Accepted 25 February 2015 Published: 11 May 2015 KeywordsDirectionalitycoherencenonparametrictime seriespoint processnetworksGranger causalityPDF download" @default.
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