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Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains

Overview of attention for article published in PLoS Computational Biology, December 2011
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139 Mendeley
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Title
Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002305
Pubmed ID
Authors

Ashok Litwin-Kumar, Anne-Marie M. Oswald, Nathaniel N. Urban, Brent Doiron

Abstract

Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Germany 3 2%
Chile 1 <1%
France 1 <1%
Switzerland 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Belgium 1 <1%
Other 0 0%
Unknown 124 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 34%
Researcher 25 18%
Student > Master 18 13%
Professor 8 6%
Professor > Associate Professor 7 5%
Other 16 12%
Unknown 18 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 31%
Neuroscience 19 14%
Physics and Astronomy 12 9%
Computer Science 10 7%
Engineering 10 7%
Other 24 17%
Unknown 21 15%
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 23 December 2011.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#171,928
of 248,979 outputs
Outputs of similar age from PLoS Computational Biology
#76
of 118 outputs
Altmetric has tracked 25,394,764 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 8,964 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.
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 248,979 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.