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000908114 1001_ $$0P:(DE-Juel1)174497$$aLayer, Moritz$$b0$$eCorresponding author$$ufzj
000908114 245__ $$aNNMT: Mean-Field Based Analysis Tools for Neuronal Network Models
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000908114 520__ $$aMean-field theory of neuronal networks has led to numerous advances in our analyticaland intuitive understanding of their dynamics during the past decades. In order tomake mean-field based analysis tools more accessible, we implemented an extensible,easy-to-use open-source Python toolbox that collects a variety of mean-field methodsfor the leaky integrate-and-fire neuron model. The Neuronal Network Mean-field Toolbox(NNMT) in its current state allows for estimating properties of large neuronal networks,such as firing rates, power spectra, and dynamical stability in mean-field and linearresponse approximation, without running simulations. In this article, we describe how thetoolbox is implemented, show how it is used to reproduce results of previous studies, anddiscuss different use-cases, such as parameter space explorations, or mapping differentnetwork models. Although the initial version of the toolbox focuses on methods for leakyintegrate-and-fire neurons, its structure is designed to be open and extensible. It aims toprovide a platform for collecting analytical methods for neuronal network model analysis,such that the neuroscientific community can take maximal advantage of them.
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000908114 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b1$$ufzj
000908114 7001_ $$0P:(DE-Juel1)176777$$aEssink, Simon$$b2$$ufzj
000908114 7001_ $$0P:(DE-Juel1)173607$$avan Meegen, Alexander$$b3$$ufzj
000908114 7001_ $$0P:(DE-Juel1)162131$$aBos, Hannah$$b4
000908114 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
000908114 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2022.835657$$gVol. 16, p. 835657$$p835657$$tFrontiers in neuroinformatics$$v16$$x1662-5196$$y2022
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