@article{14312,
author = {Aslak Tveito and Karoline J{\ae}ger and Glenn Lines and {\L}ukasz Paszkowski and Joakim Sundnes and Andrew Edwards and Tuomo M{\"a}ki-Marttunen and Geir Halnes and Gaute Einevoll},
title = {An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons},
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.},
year = {2017},
journal = {Frontiers in Computational Neuroscience},
volume = {11},
pages = {27},
publisher = {Frontiers Media SA},
issn = {1662-5188},
url = {http://journal.frontiersin.org/article/10.3389/fncom.2017.00027},
doi = {10.3389/fncom.2017.00027},
}