↓ Skip to main content

A model of electrophysiological heterogeneity in periglomerular cells

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
21 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A model of electrophysiological heterogeneity in periglomerular cells
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00049
Pubmed ID
Authors

Praveen Sethupathy, Daniel B. Rubin, Guoshi Li, Thomas A. Cleland

Abstract

Olfactory bulb (OB) periglomerular (PG) cells are heterogeneous with respect to several features, including morphology, connectivity, patterns of protein expression, and electrophysiological properties. However, these features rarely correlate with one another, suggesting that the differentiating properties of PG cells may arise from multiple independent adaptive variables rather than representing discrete cell classes. We use computational modeling to assess this hypothesis with respect to electrophysiological properties. Specifically, we show that the heterogeneous electrophysiological properties demonstrated in PG cell recordings can be explained solely by differences in the relative expression levels of ion channel species in the cell, without recourse to modifying channel kinetic properties themselves. This PG cell model can therefore be used as the basis for diverse cellular and network-level analyses of OB computations. Moreover, this simple basis for heterogeneity contributes to an emerging hypothesis that glomerular-layer interneurons may be better described as a single population expressing distributions of partially independent, potentially plastic properties, rather than as a set of discrete cell classes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 33%
Student > Bachelor 3 14%
Student > Ph. D. Student 3 14%
Student > Master 3 14%
Other 2 10%
Other 2 10%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Neuroscience 5 24%
Physics and Astronomy 2 10%
Biochemistry, Genetics and Molecular Biology 1 5%
Social Sciences 1 5%
Other 1 5%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 May 2013.
All research outputs
#13,681,551
of 22,708,120 outputs
Outputs from Frontiers in Computational Neuroscience
#609
of 1,336 outputs
Outputs of similar age
#161,890
of 280,717 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#54
of 131 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 54% 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 280,717 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 131 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 58% of its contemporaries.