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A self-organizing model of perisaccadic visual receptive field dynamics in primate visual and oculomotor system

Overview of attention for article published in Frontiers in Computational Neuroscience, February 2015
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Title
A self-organizing model of perisaccadic visual receptive field dynamics in primate visual and oculomotor system
Published in
Frontiers in Computational Neuroscience, February 2015
DOI 10.3389/fncom.2015.00017
Pubmed ID
Authors

Bedeho M. W. Mender, Simon M. Stringer

Abstract

We propose and examine a model for how perisaccadic visual receptive field dynamics, observed in a range of primate brain areas such as LIP, FEF, SC, V3, V3A, V2, and V1, may develop through a biologically plausible process of unsupervised visually guided learning. These dynamics are associated with remapping, which is the phenomenon where receptive fields anticipate the consequences of saccadic eye movements. We find that a neural network model using a local associative synaptic learning rule, when exposed to visual scenes in conjunction with saccades, can account for a range of associated phenomena. In particular, our model demonstrates predictive and pre-saccadic remapping, responsiveness shifts around the time of saccades, and remapping from multiple directions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 4%
Germany 1 4%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 25%
Student > Ph. D. Student 4 17%
Professor 3 13%
Researcher 3 13%
Other 2 8%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 21%
Nursing and Health Professions 3 13%
Medicine and Dentistry 3 13%
Neuroscience 3 13%
Computer Science 2 8%
Other 4 17%
Unknown 4 17%
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 28 February 2015.
All research outputs
#17,749,774
of 22,793,427 outputs
Outputs from Frontiers in Computational Neuroscience
#959
of 1,341 outputs
Outputs of similar age
#246,187
of 357,821 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#23
of 33 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,341 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 21st percentile – i.e., 21% 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 357,821 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.