%0 Journal Article
%A Layer, Moritz
%A Senk, Johanna
%A Essink, Simon
%A van Meegen, Alexander
%A Bos, Hannah
%A Helias, Moritz
%T NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models
%J Frontiers in neuroinformatics
%V 16
%@ 1662-5196
%C Lausanne
%I Frontiers Research Foundation
%M FZJ-2022-02384
%P 835657
%D 2022
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 35712677
%U <Go to ISI:>//WOS:000810997200001
%R 10.3389/fninf.2022.835657
%U https://juser.fz-juelich.de/record/908114