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Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection

Overview of attention for article published in Frontiers in Computational Neuroscience, September 2018
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  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users
facebook
1 Facebook page

Citations

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20 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection
Published in
Frontiers in Computational Neuroscience, September 2018
DOI 10.3389/fncom.2018.00074
Pubmed ID
Authors

Timothée Masquelier, Saeed R. Kheradpisheh

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 27%
Researcher 8 17%
Student > Master 8 17%
Professor 2 4%
Student > Doctoral Student 1 2%
Other 2 4%
Unknown 14 29%
Readers by discipline Count As %
Neuroscience 12 25%
Computer Science 8 17%
Engineering 7 15%
Physics and Astronomy 1 2%
Agricultural and Biological Sciences 1 2%
Other 2 4%
Unknown 17 35%
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 29 October 2018.
All research outputs
#15,268,318
of 24,226,848 outputs
Outputs from Frontiers in Computational Neuroscience
#720
of 1,406 outputs
Outputs of similar age
#195,552
of 345,611 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#16
of 25 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,406 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 45th percentile – i.e., 45% 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 345,611 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.