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Inferring neural circuit structure from datasets of heterogeneous tuning curves

Overview of attention for article published in PLoS Computational Biology, April 2019
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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3 X users
facebook
1 Facebook page

Citations

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2 Dimensions

Readers on

mendeley
34 Mendeley
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Title
Inferring neural circuit structure from datasets of heterogeneous tuning curves
Published in
PLoS Computational Biology, April 2019
DOI 10.1371/journal.pcbi.1006816
Pubmed ID
Authors

Takafumi Arakaki, G. Barello, Yashar Ahmadian

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

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 21%
Student > Ph. D. Student 7 21%
Student > Master 4 12%
Student > Bachelor 3 9%
Other 2 6%
Other 4 12%
Unknown 7 21%
Readers by discipline Count As %
Neuroscience 12 35%
Agricultural and Biological Sciences 4 12%
Computer Science 3 9%
Physics and Astronomy 3 9%
Engineering 2 6%
Other 3 9%
Unknown 7 21%
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 22 April 2019.
All research outputs
#16,728,456
of 25,385,509 outputs
Outputs from PLoS Computational Biology
#7,219
of 8,961 outputs
Outputs of similar age
#221,707
of 364,188 outputs
Outputs of similar age from PLoS Computational Biology
#178
of 198 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,961 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 16th percentile – i.e., 16% 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 364,188 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 198 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.