↓ Skip to main content

Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field

Overview of attention for article published in Frontiers in Computational Neuroscience, December 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
53 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field
Published in
Frontiers in Computational Neuroscience, December 2016
DOI 10.3389/fncom.2016.00141
Pubmed ID
Authors

Kang Li, Vladislav Kozyrev, Søren Kyllingsbæk, Stefan Treue, Susanne Ditlevsen, Claus Bundesen

Abstract

A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
United States 1 2%
Unknown 50 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 10 19%
Student > Master 8 15%
Student > Bachelor 5 9%
Professor 4 8%
Other 8 15%
Unknown 5 9%
Readers by discipline Count As %
Neuroscience 15 28%
Psychology 14 26%
Agricultural and Biological Sciences 6 11%
Computer Science 3 6%
Mathematics 2 4%
Other 8 15%
Unknown 5 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 May 2020.
All research outputs
#12,990,663
of 22,925,760 outputs
Outputs from Frontiers in Computational Neuroscience
#479
of 1,347 outputs
Outputs of similar age
#200,780
of 420,925 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#13
of 36 outputs
Altmetric has tracked 22,925,760 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,347 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 62% 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 420,925 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.