Stochastic transitions into silence cause noise correlations in cortical circuits

Proceedings of the National Academy of Sciences of the United States of America
Gabriela MocholJaime de la Rocha

Abstract

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamic...Continue Reading

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Citations

Feb 3, 2016·Frontiers in Neural Circuits·Garrett T Neske
Feb 26, 2016·Nature Neuroscience·Brent DoironKrešimir Josić
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Dec 24, 2021·PLoS Computational Biology·Zhuo-Cheng XiaoLai-Sang Young

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