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Fast Coding of Orientation in Primary Visual Cortex

Overview of attention for article published in PLoS Computational Biology, June 2012
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Title
Fast Coding of Orientation in Primary Visual Cortex
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002536
Pubmed ID
Authors

Oren Shriki, Adam Kohn, Maoz Shamir

Abstract

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple 'race to threshold' readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 3%
France 2 2%
United States 2 2%
Switzerland 1 1%
Canada 1 1%
United Kingdom 1 1%
Russia 1 1%
Belarus 1 1%
Unknown 79 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 32%
Researcher 24 26%
Student > Master 10 11%
Professor 5 5%
Professor > Associate Professor 5 5%
Other 10 11%
Unknown 8 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 31%
Neuroscience 14 15%
Engineering 11 12%
Psychology 9 10%
Computer Science 6 7%
Other 14 15%
Unknown 9 10%
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 31 July 2012.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#7,479
of 8,958 outputs
Outputs of similar age
#119,055
of 181,090 outputs
Outputs of similar age from PLoS Computational Biology
#88
of 105 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,958 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 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 105 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.