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Noise Suppression and Surplus Synchrony by Coincidence Detection

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
Noise Suppression and Surplus Synchrony by Coincidence Detection
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1002904
Pubmed ID
Authors

Matthias Schultze-Kraft, Markus Diesmann, Sonja Grün, Moritz Helias

Abstract

The functional significance of correlations between action potentials of neurons is still a matter of vivid debate. In particular, it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high input correlation, in the presence of synchrony, a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks.

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Geographical breakdown

Country Count As %
United States 4 5%
Germany 2 3%
France 1 1%
United Kingdom 1 1%
Sweden 1 1%
Unknown 71 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 38%
Researcher 18 23%
Student > Master 7 9%
Professor > Associate Professor 5 6%
Professor 5 6%
Other 10 13%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 26%
Neuroscience 20 25%
Physics and Astronomy 14 18%
Computer Science 7 9%
Medicine and Dentistry 5 6%
Other 5 6%
Unknown 8 10%