% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@INPROCEEDINGS{Stella:893710,
author = {Stella, Alessandra and Bouss, Peter and Palm, Günther and
Grün, Sonja},
title = {{S}urrogate techniques for testing higher order
correlations in parallel spike trains},
reportid = {FZJ-2021-02782},
year = {2021},
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.},
month = {Jun},
date = {2021-06-28},
organization = {International Conference of
Mathematical Neuroscience, Online
(Germany), 28 Jun 2021 - 1 Jul 2021},
subtyp = {Invited},
cin = {INM-6 / IAS-6 / INM-10},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
I:(DE-Juel1)INM-10-20170113},
pnm = {5231 - Neuroscientific Foundations (POF4-523) / 571 -
Connectivity and Activity (POF3-571) / HAF - Helmholtz
Analytics Framework (ZT-I-0003) / HBP SGA2 - Human Brain
Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
Human Brain Project Specific Grant Agreement 3 (945539)},
pid = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF3-571 /
G:(DE-HGF)ZT-I-0003 / G:(EU-Grant)785907 /
G:(EU-Grant)945539},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/893710},
}