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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},
}