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| 001 | 908114 | ||
| 005 | 20240313103121.0 | ||
| 024 | 7 | _ | |a 10.3389/fninf.2022.835657 |2 doi |
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| 100 | 1 | _ | |a Layer, Moritz |0 P:(DE-Juel1)174497 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models |
| 260 | _ | _ | |a Lausanne |c 2022 |b Frontiers Research Foundation |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Essink, Simon |0 P:(DE-Juel1)176777 |b 2 |u fzj |
| 700 | 1 | _ | |a van Meegen, Alexander |0 P:(DE-Juel1)173607 |b 3 |u fzj |
| 700 | 1 | _ | |a Bos, Hannah |0 P:(DE-Juel1)162131 |b 4 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 5 |u fzj |
| 773 | _ | _ | |a 10.3389/fninf.2022.835657 |g Vol. 16, p. 835657 |0 PERI:(DE-600)2452979-5 |p 835657 |t Frontiers in neuroinformatics |v 16 |y 2022 |x 1662-5196 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/908114/files/layer2022-nnmt.pdf |y OpenAccess |
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