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An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons

Authors :
Aslak Tveito
Karoline H. Jæger
Glenn T. Lines
Łukasz Paszkowski
Joakim Sundnes
Andrew G. Edwards
Tuomo Māki-Marttunen
Geir Halnes
Gaute T. Einevoll
Source :
Frontiers in Computational Neuroscience, Vol 11 (2017)
Publication Year :
2017
Publisher :
Frontiers Media S.A., 2017.

Abstract

Two mathematical models are part of the foundation of Computational neurophysiology; (a) the Cable equation is used to compute the membrane potential of neurons, and, (b) volume-conductor theory describes the extracellular potential around neurons. In the standard procedure for computing extracellular potentials, the transmembrane currents are computed by means of (a) and the extracellular potentials are computed using an explicit sum over analytical point-current source solutions as prescribed by volume conductor theory. Both models are extremely useful as they allow huge simplifications of the computational efforts involved in computing extracellular potentials. However, there are more accurate, though computationally very expensive, models available where the potentials inside and outside the neurons are computed simultaneously in a self-consistent scheme. In the present work we explore the accuracy of the classical models (a) and (b) by comparing them to these more accurate schemes. The main assumption of (a) is that the ephaptic current can be ignored in the derivation of the Cable equation. We find, however, for our examples with stylized neurons, that the ephaptic current is comparable in magnitude to other currents involved in the computations, suggesting that it may be significant—at least in parts of the simulation. The magnitude of the error introduced in the membrane potential is several millivolts, and this error also translates into errors in the predicted extracellular potentials. While the error becomes negligible if we assume the extracellular conductivity to be very large, this assumption is, unfortunately, not easy to justify a priori for all situations of interest.

Details

Language :
English
ISSN :
16625188
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Computational Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.f7a6ae1e28354287b9e89ae116899879
Document Type :
article
Full Text :
https://doi.org/10.3389/fncom.2017.00027