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

Journal of Computational Neuroscience
Eric B HendricksonDieter Jaeger

Abstract

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 prese...Continue Reading

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Citations

Jan 19, 2011·Journal of Computational Neuroscience·Eric B HendricksonDieter Jaeger
Feb 23, 2011·Journal of Computational Neuroscience·Sherry-Ann BrownLeslie M Loew
Mar 8, 2013·PLoS Computational Biology·Romain Daniel CazéBoris Gutkin
Apr 12, 2016·Communicative & Integrative Biology·Hojeong Kim, C J Heckman
Aug 19, 2015·Journal of Computational Neuroscience·E Yu SmirnovaA V Chizhov
Mar 3, 2015·Frontiers in Computational Neuroscience·Pietro BalbiPaolo Massobrio
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Jul 23, 2020·Journal of Neurophysiology·Jeffrey A Ruffolo, Andrew D McClellan
Jan 17, 2020·Nature Communications·Oren AmsalemIdan Segev
Apr 9, 2021·Computers in Biology and Medicine·Mai GamalSherif M Elbasiouny

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