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  4. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties
 
research article

Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties

Hay, Etay
•
Hill, Sean  
•
Schürmann, Felix  
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2011
PLoS Computational Biology

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na(+)-spiking behavior as well as key dendritic active properties, including Ca(2+) spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca(2+) spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.

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Type
research article
DOI
10.1371/journal.pcbi.1002107
Web of Science ID

WOS:000293333200013

PubMed ID

21829333

Author(s)
Hay, Etay
•
Hill, Sean  
•
Schürmann, Felix  
•
Markram, Henry  
•
Segev, Idan
Date Issued

2011

Published in
PLoS Computational Biology
Volume

7

Issue

7

Article Number

e1002107

Subjects

Models

•

Neurological

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LNMC  
GR-FSCH  
BBP-GR-HILL  
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Available on Infoscience
January 28, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/88262
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