ArticleFrontiers in computational neuroscience2017
An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
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- Predicting neural responses to intra- and extra-cranial electric brain stimulation by means of the reciprocity theorem.PLoS computational biology · 2025Article
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- Multiformity of extracellular microelectrode recordings from Aδ neurons in the dorsal root ganglia: a computational modeling study.Journal of neurophysiology · 2024Article
- Electrophysiological damage to neuronal membrane alters ephaptic entrainment.Scientific reports · 2023Article
- Nano-scale solution of the Poisson-Nernst-Planck (PNP) equations in a fraction of two neighboring cells reveals the magnitude of intercellular electrochemical waves.PLoS computational biology · 2023Article
- Modelling the effect of ephaptic coupling on spike propagation in peripheral nerve fibres.Biological cybernetics · 2022Article
- Dorsal Root Ganglion Stimulation for Chronic Pain: Hypothesized Mechanisms of Action.The journal of pain · 2022Review
- Ephaptic entrainment in hybrid neuronal model.Scientific reports · 2022Article
- An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain.PLoS computational biology · 2021Article
- Computational prediction of drug response in short QT syndrome type 1 based on measurements of compound effect in stem cell-derived cardiomyocytes.PLoS computational biology · 2021Article
- From Millimeters to Micrometers; Re-introducing Myocytes in Models of Cardiac Electrophysiology.Frontiers in physiology · 2021Article
- Deriving the Bidomain Model of Cardiac Electrophysiology From a Cell-Based Model; Properties and Comparisons.Frontiers in physiology · 2021Article
- Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves.PLoS computational biology · 2020Article
- An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms.PLoS computational biology · 2020Article
- Finite Element Simulation of Ionic Electrodiffusion in Cellular Geometries.Frontiers in neuroinformatics · 2020Article
- Properties of cardiac conduction in a cell-based computational model.PLoS computational biology · 2019Article
- Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
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9 authors.
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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
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