↓ Skip to main content

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

Overview of attention for article published in Frontiers in Computational Neuroscience, April 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 X users

Readers on

mendeley
51 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons
Published in
Frontiers in Computational Neuroscience, April 2017
DOI 10.3389/fncom.2017.00027
Pubmed ID
Authors

Aslak Tveito, Karoline H. Jæger, Glenn T. Lines, Łukasz Paszkowski, Joakim Sundnes, Andrew G. Edwards, Tuomo Māki-Marttunen, Geir Halnes, Gaute T. Einevoll

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 27%
Researcher 11 22%
Professor 5 10%
Student > Doctoral Student 4 8%
Student > Bachelor 2 4%
Other 6 12%
Unknown 9 18%
Readers by discipline Count As %
Neuroscience 14 27%
Agricultural and Biological Sciences 8 16%
Engineering 8 16%
Physics and Astronomy 4 8%
Computer Science 2 4%
Other 5 10%
Unknown 10 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 May 2017.
All research outputs
#13,314,170
of 22,965,074 outputs
Outputs from Frontiers in Computational Neuroscience
#520
of 1,347 outputs
Outputs of similar age
#153,153
of 309,698 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#17
of 36 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,347 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 309,698 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.