TY  - JOUR
AU  - Layer, Moritz
AU  - Senk, Johanna
AU  - Essink, Simon
AU  - van Meegen, Alexander
AU  - Bos, Hannah
AU  - Helias, Moritz
TI  - NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models
JO  - Frontiers in neuroinformatics
VL  - 16
SN  - 1662-5196
CY  - Lausanne
PB  - Frontiers Research Foundation
M1  - FZJ-2022-02384
SP  - 835657
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 35712677
UR  - <Go to ISI:>//WOS:000810997200001
DO  - DOI:10.3389/fninf.2022.835657
UR  - https://juser.fz-juelich.de/record/908114
ER  -