ArticleFrontiers in neuroinformatics2022
NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Multi-scale spiking network model of human cerebral cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing.Lipids in health and disease · 2023Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
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Authors and funding
6 authors.
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Abstract
Mean-field theory of neuronal networks has led to numerous advances in our analytical and intuitive understanding of their dynamics during the past decades. In order to make 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 methods for 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 linear response approximation, without running simulations. In this article, we describe how the toolbox is implemented, show how it is used to reproduce results of previous studies, and discuss different use-cases, such as parameter space explorations, or mapping different network models. Although the initial version of the toolbox focuses on methods for leaky integrate-and-fire neurons, its structure is designed to be open and extensible. It aims to provide 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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