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Journal of Computational Neuroscience

, Volume 47, Issue 2–3, pp 141–166 | Cite as

Reduced order models of myelinated axonal compartments

  • Daniel IoanEmail author
  • Ruxandra Bărbulescu
  • Luis Miguel Silveira
  • Gabriela Ciuprina
Article

Abstract

The paper presents a hierarchical series of computational models for myelinated axonal compartments. Three classes of models are considered, either with distributed parameters (2.5D EQS–ElectroQuasi Static, 1D TL-Transmission Lines) or with lumped parameters (0D). They are systematically analyzed with both analytical and numerical approaches, the main goal being to identify the best procedure for order reduction of each case. An appropriate error estimator is proposed in order to assess the accuracy of the models. This is the foundation of a procedure able to find the simplest reduced model having an imposed precision. The most computationally efficient model from the three geometries proved to be the analytical 1D one, which is able to have accuracy less than 0.1%. By order reduction with vector fitting, a finite model is generated with a relative difference of 10− 4 for order 5. The dynamical models thus extracted allow an efficient simulation of neurons and, consequently, of neuronal circuits. In such situations, the linear models of the myelinated compartments coupled with the dynamical, non-linear models of the Ranvier nodes, neuronal body (soma) and dendritic tree give global reduced models. In order to ease the simulation of large-scale neuronal systems, the sub-models at each level, including those of myelinated compartments should have the lowest possible order. The presented procedure is a first step in achieving simulations of neural systems with accuracy control.

Keywords

Neuron Axon Myelination Dynamical model Reduced order models Accuracy control EQS field Analytical approach Modal analysis Numerical methods FEM FIT BEM FDM Cable model Neuronal circuits 

Notes

Acknowledgements

This work was supported by TD COST Action TD1307 European Model Reduction Network (EU-MORNET).

The work reported in this article was partly supported by national funds through the Portuguese “Fundação para a Ciência e a Tecnologia” (FCT) with reference UID/CEC/50021/2019 as well as project PTDC/EEI-EEE/31140/2017.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University Politehnica of BucharestBucharestRomania
  2. 2.INESC-IDLisboaPortugal

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