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E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models
The transmembrane ionic currents that underlie changes in a cell's membrane potential give rise to electric fields in the extracellular space. In the context of brain activity, these electric fields form the basis for extracellularly recorded signals, such as multiunit activity, local field pot...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555196/ https://www.ncbi.nlm.nih.gov/pubmed/31214004 http://dx.doi.org/10.3389/fninf.2019.00035 |
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author | Shifman, Aaron R. Lewis, John E. |
author_facet | Shifman, Aaron R. Lewis, John E. |
author_sort | Shifman, Aaron R. |
collection | PubMed |
description | The transmembrane ionic currents that underlie changes in a cell's membrane potential give rise to electric fields in the extracellular space. In the context of brain activity, these electric fields form the basis for extracellularly recorded signals, such as multiunit activity, local field potentials and electroencephalograms. Understanding the underlying neuronal dynamics and localizing current sources using these signals is often challenging, and therefore effective computational modeling approaches are critical. Typically, the electric fields from neural activity are modeled in a post-hoc form, i.e., a traditional neuronal model is used to first generate the membrane currents, which in turn are then used to calculate the electric fields. When the conductivity of the extracellular space is high, the electric fields are weak, and therefore treating membrane currents and electric fields separately is justified. However, in brain regions of lower conductivity, extracellular fields can feed back and significantly influence the underlying transmembrane currents and dynamics of nearby neurons—this is often referred to as ephaptic coupling. The closed-loop nature of ephaptic coupling cannot be modeled using the post-hoc approaches implemented by existing software tools; instead, electric fields and neuronal dynamics must be solved simultaneously. To this end, we have developed a generalized modeling toolbox for studying ephaptic coupling in compartmental neuron models: E(L)FENN (E(L)ectric Field Effects in Neural Networks). In open loop conditions, we validate the separate components of E(L)FENN for modeling membrane dynamics and associated field potentials against standard approaches (NEURON and LFPy). Unlike standard approaches however, E(L)FENN enables the closed-loop condition to be modeled as well, in that the field potentials can feed back and influence membrane dynamics. As an example closed-loop case, we use E(L)FENN to study phase-locking of action potentials generated by a population of axons running parallel in a bundle. Being able to efficiently explore ephaptic coupling from a computational perspective using tools, such as E(L)FENN will allow us to better understand the physical basis of electric fields in the brain, as well as the conditions in which these fields may influence neuronal dynamics in general. |
format | Online Article Text |
id | pubmed-6555196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65551962019-06-18 E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models Shifman, Aaron R. Lewis, John E. Front Neuroinform Neuroscience The transmembrane ionic currents that underlie changes in a cell's membrane potential give rise to electric fields in the extracellular space. In the context of brain activity, these electric fields form the basis for extracellularly recorded signals, such as multiunit activity, local field potentials and electroencephalograms. Understanding the underlying neuronal dynamics and localizing current sources using these signals is often challenging, and therefore effective computational modeling approaches are critical. Typically, the electric fields from neural activity are modeled in a post-hoc form, i.e., a traditional neuronal model is used to first generate the membrane currents, which in turn are then used to calculate the electric fields. When the conductivity of the extracellular space is high, the electric fields are weak, and therefore treating membrane currents and electric fields separately is justified. However, in brain regions of lower conductivity, extracellular fields can feed back and significantly influence the underlying transmembrane currents and dynamics of nearby neurons—this is often referred to as ephaptic coupling. The closed-loop nature of ephaptic coupling cannot be modeled using the post-hoc approaches implemented by existing software tools; instead, electric fields and neuronal dynamics must be solved simultaneously. To this end, we have developed a generalized modeling toolbox for studying ephaptic coupling in compartmental neuron models: E(L)FENN (E(L)ectric Field Effects in Neural Networks). In open loop conditions, we validate the separate components of E(L)FENN for modeling membrane dynamics and associated field potentials against standard approaches (NEURON and LFPy). Unlike standard approaches however, E(L)FENN enables the closed-loop condition to be modeled as well, in that the field potentials can feed back and influence membrane dynamics. As an example closed-loop case, we use E(L)FENN to study phase-locking of action potentials generated by a population of axons running parallel in a bundle. Being able to efficiently explore ephaptic coupling from a computational perspective using tools, such as E(L)FENN will allow us to better understand the physical basis of electric fields in the brain, as well as the conditions in which these fields may influence neuronal dynamics in general. Frontiers Media S.A. 2019-05-31 /pmc/articles/PMC6555196/ /pubmed/31214004 http://dx.doi.org/10.3389/fninf.2019.00035 Text en Copyright © 2019 Shifman and Lewis. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Shifman, Aaron R. Lewis, John E. E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title | E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title_full | E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title_fullStr | E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title_full_unstemmed | E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title_short | E(L)FENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models |
title_sort | e(l)fenn: a generalized platform for modeling ephaptic coupling in spiking neuron models |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555196/ https://www.ncbi.nlm.nih.gov/pubmed/31214004 http://dx.doi.org/10.3389/fninf.2019.00035 |
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