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@ARTICLE{Layer:905088,
      author       = {Layer, Moritz and Senk, Johanna and Essink, Simon and van
                      Meegen, Alexander and Bos, Hannah and Helias, Moritz},
      title        = {{A} mean-field toolbox for spiking neuronal network model
                      analysis},
      reportid     = {FZJ-2022-00387},
      year         = {2021},
      abstract     = {Mean-field theory of spiking neuronal networks has led to
                      numerous advances in our analytical and intuitive
                      understanding of the dynamics of neuronal network models
                      during the past decades. But, the elaborate nature of many
                      of the developed methods, as well as the difficulty of
                      implementing them, may limit the wider neuroscientific
                      community from taking maximal advantage of these tools. In
                      order to make them more accessible, we implemented an
                      extensible, easy-to-use open-source Python toolbox that
                      collects a variety of mean-field methods for the widely used
                      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 linear response approximation, without
                      running simulations on high performance systems. In this
                      article we describe how the toolbox is implemented, show how
                      it is used to calculate neuronal network properties, and
                      discuss different use-cases, such as extraction of network
                      mechanisms, parameter space exploration, or hybrid modeling
                      approaches. Although the initial version of the toolbox
                      focuses on methods that are close to our own past and
                      present research, its structure is designed to be open and
                      extensible. It aims to provide a platform for collecting
                      analytical methods for neuronal network model analysis and
                      we discuss how interested scientists can share their own
                      methods via this platform.},
      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) / 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)},
      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},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.12.14.472584},
      url          = {https://juser.fz-juelich.de/record/905088},
}