Cargando…

The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites

Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures,...

Descripción completa

Detalles Bibliográficos
Autores principales: Hendrickson, Eric B., Edgerton, Jeremy R., Jaeger, Dieter
Formato: Texto
Lenguaje:English
Publicado: Springer US 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3058356/
https://www.ncbi.nlm.nih.gov/pubmed/20623167
http://dx.doi.org/10.1007/s10827-010-0258-z
_version_ 1782200361943040000
author Hendrickson, Eric B.
Edgerton, Jeremy R.
Jaeger, Dieter
author_facet Hendrickson, Eric B.
Edgerton, Jeremy R.
Jaeger, Dieter
author_sort Hendrickson, Eric B.
collection PubMed
description Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of biological neural processing. It is not yet well understood which processing capabilities require detailed branching structures. Therefore, we analyzed the processing capabilities of full or partially branched reduced models. These models were created by collapsing the dendritic tree of a full morphological model of a globus pallidus (GP) neuron while preserving its total surface area and electrotonic length, as well as its passive and active parameters. Dendritic trees were either collapsed into single cables (unbranched models) or the full complement of branch points was preserved (branched models). Both reduction strategies allowed us to compare dynamics between all models using the same channel density settings. Full model responses to somatic inputs were generally preserved by both types of reduced model while dendritic input responses could be more closely preserved by branched than unbranched reduced models. However, features strongly influenced by local dendritic input resistance, such as active dendritic sodium spike generation and propagation, could not be accurately reproduced by any reduced model. Based on our analyses, we suggest that there are intrinsic differences in processing capabilities between unbranched and branched models. We also indicate suitable applications for different levels of reduction, including fast searches of full model parameter space. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10827-010-0258-z) contains supplementary material, which is available to authorized users.
format Text
id pubmed-3058356
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-30583562011-04-05 The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites Hendrickson, Eric B. Edgerton, Jeremy R. Jaeger, Dieter J Comput Neurosci Article Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of biological neural processing. It is not yet well understood which processing capabilities require detailed branching structures. Therefore, we analyzed the processing capabilities of full or partially branched reduced models. These models were created by collapsing the dendritic tree of a full morphological model of a globus pallidus (GP) neuron while preserving its total surface area and electrotonic length, as well as its passive and active parameters. Dendritic trees were either collapsed into single cables (unbranched models) or the full complement of branch points was preserved (branched models). Both reduction strategies allowed us to compare dynamics between all models using the same channel density settings. Full model responses to somatic inputs were generally preserved by both types of reduced model while dendritic input responses could be more closely preserved by branched than unbranched reduced models. However, features strongly influenced by local dendritic input resistance, such as active dendritic sodium spike generation and propagation, could not be accurately reproduced by any reduced model. Based on our analyses, we suggest that there are intrinsic differences in processing capabilities between unbranched and branched models. We also indicate suitable applications for different levels of reduction, including fast searches of full model parameter space. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10827-010-0258-z) contains supplementary material, which is available to authorized users. Springer US 2010-07-10 2011 /pmc/articles/PMC3058356/ /pubmed/20623167 http://dx.doi.org/10.1007/s10827-010-0258-z Text en © The Author(s) 2010 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Hendrickson, Eric B.
Edgerton, Jeremy R.
Jaeger, Dieter
The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title_full The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title_fullStr The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title_full_unstemmed The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title_short The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
title_sort capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3058356/
https://www.ncbi.nlm.nih.gov/pubmed/20623167
http://dx.doi.org/10.1007/s10827-010-0258-z
work_keys_str_mv AT hendricksonericb thecapabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites
AT edgertonjeremyr thecapabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites
AT jaegerdieter thecapabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites
AT hendricksonericb capabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites
AT edgertonjeremyr capabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites
AT jaegerdieter capabilitiesandlimitationsofconductancebasedcompartmentalneuronmodelswithreducedbranchedorunbranchedmorphologiesandactivedendrites