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| Home > Publications database > Surrogate techniques for testing higher order correlations in parallel spike trains |
| Conference Presentation (Invited) | FZJ-2021-02782 |
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2021
Please use a persistent id in citations: http://hdl.handle.net/2128/28439
Abstract: Spike train surrogates are an essential methodology to assess significance of statistical properties of electrophysiological data. Numerous methods have been already introduced (Date 1998, Gruen et al. 2009, Louis et al. 2010, Pipa et al. 2002,2003,2007, Smith and Kohn 2008, Ricci et al. 2019) and the resulting properties studied in depth, in particular for the case of spike time correlations (Gruen et al. 2009, Louis et al. 2010).The temporal scale of higher-order spike coordination in cortex is still debated in the field (Shadlen and Newsome 1998, London et al. 2010, Mokeichev et al. 2007, Russo et al. 2018, Williams et al. 2020). Moreover, it still has to be investigated from which intrinsic statistical characteristics (e.g. firing rate co-modulations, CV, ISI distribution) have influence in the formation of spike patterns. The choice of the surrogate technique is central, since it determines the null hypothesis, where higher order correlation is not a feature of the spike train, i.e. spike trains are mutually independent and only chance correlations are present. For the case of statistical testing for precise temporal correlations, the classical surrogate choice is Uniform Dithering (Date et al. 1998).Here we show a number of problematics arising from the application of Uniform Dithering in the context of testing fine-tune temporal correlation in form of spatio-temporal spike patterns with the SPADE method (Torre et al. 2013/2016, Quaglio et al. 2017, Stella et al. 2019). We use already developed surrogate techniques and introduce new ones, and study analytically (when possible) and through simulations the statistical features that are kept and destroyed from surrogate manipulation. Finally, we generate artificial datasets modeled on real experimental data through non stationary point process models, such as Poisson process, Poisson process with dead time (Deger et al. 2011) and Gamma process, comparing the outcome of the SPADE analysis across surrogate techniques.
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