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Specific excitatory connectivity for feature integration in mouse primary visual cortex

Overview of attention for article published in PLoS Computational Biology, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
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Title
Specific excitatory connectivity for feature integration in mouse primary visual cortex
Published in
PLoS Computational Biology, December 2017
DOI 10.1371/journal.pcbi.1005888
Pubmed ID
Authors

Dylan R. Muir, Patricia Molina-Luna, Morgane M. Roth, Fritjof Helmchen, Björn M. Kampa

Abstract

Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a 'like-to-like' scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a 'feature binding' scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming 'feature binding' connectivity. Unlike under the 'like-to-like' scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.

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The data shown below were collected from the profiles of 21 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 30%
Researcher 17 18%
Student > Bachelor 8 9%
Student > Master 8 9%
Other 5 5%
Other 11 12%
Unknown 15 16%
Readers by discipline Count As %
Neuroscience 30 33%
Agricultural and Biological Sciences 22 24%
Computer Science 9 10%
Biochemistry, Genetics and Molecular Biology 3 3%
Engineering 3 3%
Other 9 10%
Unknown 16 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 07 May 2018.
All research outputs
#3,623,572
of 25,382,440 outputs
Outputs from PLoS Computational Biology
#3,167
of 8,960 outputs
Outputs of similar age
#74,894
of 443,583 outputs
Outputs of similar age from PLoS Computational Biology
#67
of 132 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 64% 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 443,583 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.