↓ Skip to main content

PLOS

Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains

Overview of attention for article published in PLoS Computational Biology, December 2011
Altmetric Badge

Mentioned by

googleplus
1 Google+ user

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
142 Mendeley
citeulike
1 CiteULike
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 142 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 127 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 33%
Researcher 28 20%
Student > Master 18 13%
Professor 8 6%
Professor > Associate Professor 7 5%
Other 16 11%
Unknown 18 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 31%
Neuroscience 19 13%
Physics and Astronomy 12 8%
Computer Science 10 7%
Engineering 10 7%
Other 26 18%
Unknown 21 15%