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Systematic analysis of the contributions of stochastic voltage gated channels to neuronal noise

Overview of attention for article published in Frontiers in Computational Neuroscience, September 2014
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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9 X users

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Title
Systematic analysis of the contributions of stochastic voltage gated channels to neuronal noise
Published in
Frontiers in Computational Neuroscience, September 2014
DOI 10.3389/fncom.2014.00105
Pubmed ID
Authors

Cian O'Donnell, Mark C. W. van Rossum

Abstract

Electrical signaling in neurons is mediated by the opening and closing of large numbers of individual ion channels. The ion channels' state transitions are stochastic and introduce fluctuations in the macroscopic current through ion channel populations. This creates an unavoidable source of intrinsic electrical noise for the neuron, leading to fluctuations in the membrane potential and spontaneous spikes. While this effect is well known, the impact of channel noise on single neuron dynamics remains poorly understood. Most results are based on numerical simulations. There is no agreement, even in theoretical studies, on which ion channel type is the dominant noise source, nor how inclusion of additional ion channel types affects voltage noise. Here we describe a framework to calculate voltage noise directly from an arbitrary set of ion channel models, and discuss how this can be use to estimate spontaneous spike rates.

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The data shown below were collected from the profiles of 9 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 2%
France 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Researcher 11 21%
Student > Master 8 15%
Student > Bachelor 6 12%
Professor 4 8%
Other 7 13%
Unknown 4 8%
Readers by discipline Count As %
Neuroscience 15 29%
Agricultural and Biological Sciences 10 19%
Computer Science 4 8%
Physics and Astronomy 4 8%
Engineering 4 8%
Other 7 13%
Unknown 8 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 January 2019.
All research outputs
#6,722,043
of 22,760,687 outputs
Outputs from Frontiers in Computational Neuroscience
#349
of 1,339 outputs
Outputs of similar age
#66,531
of 237,913 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#8
of 29 outputs
Altmetric has tracked 22,760,687 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,339 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 73% 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 237,913 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 29 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 72% of its contemporaries.