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Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input

Overview of attention for article published in PLoS Computational Biology, August 2014
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3 X users

Citations

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22 Dimensions

Readers on

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70 Mendeley
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1 CiteULike
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Title
Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003775
Pubmed ID
Authors

Jonathan Laudanski, Benjamin Torben-Nielsen, Idan Segev, Shihab Shamma

Abstract

An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.

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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
Uruguay 1 1%
Sweden 1 1%
France 1 1%
India 1 1%
Belarus 1 1%
Japan 1 1%
United States 1 1%
Unknown 60 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 37%
Student > Master 10 14%
Researcher 8 11%
Student > Bachelor 8 11%
Professor 4 6%
Other 8 11%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 30%
Neuroscience 16 23%
Engineering 8 11%
Mathematics 3 4%
Computer Science 3 4%
Other 10 14%
Unknown 9 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 August 2014.
All research outputs
#17,450,897
of 25,604,262 outputs
Outputs from PLoS Computational Biology
#7,527
of 9,014 outputs
Outputs of similar age
#149,029
of 247,962 outputs
Outputs of similar age from PLoS Computational Biology
#122
of 159 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,014 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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 247,962 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.