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NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.

Moritz Layer1,2, Johanna Senk1, Simon Essink1,2

  • 1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany.

Frontiers in Neuroinformatics
|June 17, 2022
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Summary
This summary is machine-generated.

We developed the Neuronal Network Mean-field Toolbox (NNMT), an open-source Python tool for analyzing large neuronal networks. NNMT provides accessible mean-field methods for the leaky integrate-and-fire model, enabling faster insights without simulations.

Keywords:
(hybrid) modeling(spiking) neuronal networkcomputational neuroscienceintegrate-and-fire neuronmean-field theoryopen-source softwareparameter space explorationpython

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Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Systems Neuroscience

Background:

  • Mean-field theory has significantly advanced the understanding of neuronal network dynamics.
  • Analytical tools for mean-field analysis are crucial but can be complex to access.
  • The leaky integrate-and-fire model is a fundamental model for neuronal activity.

Purpose of the Study:

  • To create an accessible, extensible, open-source Python toolbox for mean-field analysis of neuronal networks.
  • To implement various mean-field methods specifically for the leaky integrate-and-fire neuron model.
  • To facilitate the estimation of large neuronal network properties without direct simulation.

Main Methods:

  • Implementation of an open-source Python toolbox named the Neuronal Network Mean-field Toolbox (NNMT).
  • Collection of diverse mean-field methods applicable to the leaky integrate-and-fire neuron model.
  • Development of functionalities for estimating firing rates, power spectra, and dynamical stability.

Main Results:

  • The NNMT allows for the estimation of key neuronal network properties using mean-field and linear response approximations.
  • The toolbox enables analysis without the need for computationally intensive simulations.
  • Demonstrated reproducibility of previous study results using the implemented toolbox.

Conclusions:

  • The NNMT provides an accessible platform for analyzing neuronal networks, enhancing the utility of mean-field theory.
  • The toolbox's extensible design supports future integration of additional analytical methods.
  • NNMT aims to empower the neuroscientific community with advanced analytical tools for neuronal network models.