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Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis

Overview of attention for article published in PLoS Computational Biology, February 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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20 X users
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1 Facebook page
wikipedia
1 Wikipedia page
googleplus
1 Google+ user
reddit
1 Redditor

Citations

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69 Dimensions

Readers on

mendeley
200 Mendeley
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5 CiteULike
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Title
Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis
Published in
PLoS Computational Biology, February 2013
DOI 10.1371/journal.pcbi.1002890
Pubmed ID
Authors

Eduardo J. Izquierdo, Randall D. Beer

Abstract

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 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 200 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 5%
Portugal 2 1%
Netherlands 1 <1%
France 1 <1%
Hungary 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 182 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 32%
Researcher 40 20%
Student > Bachelor 28 14%
Student > Master 15 8%
Professor 12 6%
Other 27 14%
Unknown 14 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 28%
Neuroscience 31 16%
Computer Science 22 11%
Engineering 17 9%
Biochemistry, Genetics and Molecular Biology 14 7%
Other 39 20%
Unknown 20 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 19 June 2023.
All research outputs
#2,220,733
of 25,874,560 outputs
Outputs from PLoS Computational Biology
#1,946
of 9,062 outputs
Outputs of similar age
#21,087
of 293,225 outputs
Outputs of similar age from PLoS Computational Biology
#25
of 160 outputs
Altmetric has tracked 25,874,560 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,062 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done well, scoring higher than 78% 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 293,225 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 160 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.