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@ARTICLE{vanMeegen:885635,
author = {van Meegen, Alexander and Kühn, Tobias and Helias, Moritz},
title = {{L}arge {D}eviation {A}pproach to {R}andom {R}ecurrent
{N}euronal {N}etworks: {R}ate {F}unction, {P}arameter
{I}nference, and {A}ctivity {P}rediction},
reportid = {FZJ-2020-03976},
year = {2020},
abstract = {Statistical field theory captures collective
non-equilibrium dynamics of neuronal networks, but it does
not address the inverse problem of searching the
connectivity to implement a desired dynamics. We here show
for an analytically solvable network model that the
effective action in statistical field theory is identical to
the rate function in large deviation theory; using field
theoretical methods we derive this rate function. It takes
the form of a Kullback-Leibler divergence and enables
data-driven inference of model parameters and Bayesian
prediction of time series.},
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) / 571 -
Connectivity and Activity (POF3-571) / HBP SGA2 - Human
Brain Project Specific Grant Agreement 2 (785907) / MSNN -
Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
/ PhD no Grant - Doktorand ohne besondere Förderung
(PHD-NO-GRANT-20170405)},
pid = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-571 /
G:(EU-Grant)785907 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
G:(DE-Juel1)PHD-NO-GRANT-20170405},
typ = {PUB:(DE-HGF)25},
eprint = {2009.08889},
howpublished = {arXiv:2009.08889},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2009.08889;\%\%$},
url = {https://juser.fz-juelich.de/record/885635},
}