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@ARTICLE{Spitzner:888460,
author = {Spitzner, Franz Paul and Dehning, Jonas and Wilting, J. and
Hagemann, A. and Neto, J. P. and Zierenberg, J. and
Priesemann, V.},
title = {{MR}. {E}stimator, a toolbox to determine intrinsic
timescales from subsampled spiking activity},
reportid = {FZJ-2020-04928},
year = {2020},
abstract = {Here we present our Python toolbox 'MR. Estimator' to
reliably estimate the intrinsic timescale from
electrophysiologal recordings of heavily subsampled systems.
Originally intended for the analysis of time series from
neuronal spiking activity, our toolbox is applicable to a
wide range of systems where subsampling - the difficulty to
observe the whole system in full detail - limits our
capability to record. Applications range from epidemic
spreading to any system that can be represented by an
autoregressive process. In the context of neuroscience, the
intrinsic timescale can be thought of as the duration over
which any perturbation reverberates within the network; it
has been used as a key observable to investigate a
functional hierarchy across the primate cortex and serves as
a measure of working memory. It is also a proxy for the
distance to criticality and quantifies a system's dynamic
working point.},
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 = {574 - Theory, modelling and simulation (POF3-574)},
pid = {G:(DE-HGF)POF3-574},
typ = {PUB:(DE-HGF)25},
eprint = {2007.03367},
howpublished = {arXiv:2007.03367},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2007.03367;\%\%$},
pubmed = {pmid:33914774},
url = {https://juser.fz-juelich.de/record/888460},
}