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Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

Overview of attention for article published in PLoS Computational Biology, July 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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1 X user
patent
1 patent
wikipedia
2 Wikipedia pages

Citations

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320 Dimensions

Readers on

mendeley
348 Mendeley
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3 CiteULike
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Title
Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties
Published in
PLoS Computational Biology, July 2011
DOI 10.1371/journal.pcbi.1002107
Pubmed ID
Authors

Etay Hay, Sean Hill, Felix Schürmann, Henry Markram, Idan Segev

Abstract

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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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 348 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
Germany 4 1%
Netherlands 4 1%
Japan 4 1%
United Kingdom 4 1%
France 3 <1%
Switzerland 3 <1%
Israel 2 <1%
Brazil 1 <1%
Other 10 3%
Unknown 304 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 90 26%
Researcher 71 20%
Student > Master 37 11%
Professor 28 8%
Student > Bachelor 28 8%
Other 53 15%
Unknown 41 12%
Readers by discipline Count As %
Neuroscience 98 28%
Agricultural and Biological Sciences 80 23%
Engineering 30 9%
Computer Science 27 8%
Physics and Astronomy 20 6%
Other 38 11%
Unknown 55 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 November 2023.
All research outputs
#4,836,328
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#3,865
of 8,960 outputs
Outputs of similar age
#24,636
of 130,285 outputs
Outputs of similar age from PLoS Computational Biology
#25
of 64 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 130,285 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.