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@ARTICLE{Layer:908114,
author = {Layer, Moritz and Senk, Johanna and Essink, Simon and van
Meegen, Alexander and Bos, Hannah and Helias, Moritz},
title = {{NNMT}: {M}ean-{F}ield {B}ased {A}nalysis {T}ools for
{N}euronal {N}etwork {M}odels},
journal = {Frontiers in neuroinformatics},
volume = {16},
issn = {1662-5196},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2022-02384},
pages = {835657},
year = {2022},
abstract = {Mean-field theory of neuronal networks has led to numerous
advances in our analyticaland intuitive understanding of
their dynamics during the past decades. In order tomake
mean-field based analysis tools more accessible, we
implemented an extensible,easy-to-use open-source Python
toolbox that collects a variety of mean-field methodsfor the
leaky integrate-and-fire neuron model. The Neuronal Network
Mean-field Toolbox(NNMT) in its current state allows for
estimating properties of large neuronal networks,such as
firing rates, power spectra, and dynamical stability in
mean-field and linearresponse approximation, without running
simulations. In this article, we describe how thetoolbox is
implemented, show how it is used to reproduce results of
previous studies, anddiscuss different use-cases, such as
parameter space explorations, or mapping differentnetwork
models. Although the initial version of the toolbox focuses
on methods for leakyintegrate-and-fire neurons, its
structure is designed to be open and extensible. It aims
toprovide a platform for collecting analytical methods for
neuronal network model analysis,such that the
neuroscientific community can take maximal advantage of
them.},
cin = {INM-6 / IAS-6 / INM-10},
ddc = {610},
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) / HBP SGA1 -
Human Brain Project Specific Grant Agreement 1 (720270) /
HBP SGA2 - Human Brain Project Specific Grant Agreement 2
(785907) / HBP SGA3 - Human Brain Project Specific Grant
Agreement 3 (945539) / GRK 2416: MultiSenses-MultiScales:
Novel approaches to decipher neural processing in
multisensory integration (368482240) / JL SMHB - Joint Lab
Supercomputing and Modeling for the Human Brain (JL
SMHB-2021-2027) / MSNN - Theory of multi-scale neuronal
networks (HGF-SMHB-2014-2018) /
Open-Access-Publikationskosten Forschungszentrum Jülich
(OAPKFZJ) (491111487)},
pid = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)720270 /
G:(EU-Grant)785907 / G:(EU-Grant)945539 /
G:(GEPRIS)368482240 / G:(DE-Juel1)JL SMHB-2021-2027 /
G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(GEPRIS)491111487},
typ = {PUB:(DE-HGF)16},
pubmed = {35712677},
UT = {WOS:000810997200001},
doi = {10.3389/fninf.2022.835657},
url = {https://juser.fz-juelich.de/record/908114},
}