期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2015
卷号：112
期号：11
页码：3529-3534
DOI：10.1073/pnas.1410509112
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceNeurons in the cerebral cortex emit action potentials in a seemingly random manner. One puzzling aspect of this neuronal "noise" is that it is correlated among neighboring neurons, something thought to reflect the tendency of neurons to fire together. Here, we recorded the activity from populations of cortical neurons in rats and found that correlations could be largely explained by the tendency of cortical neurons to stop firing together. A computational network model whose activity alternated between periods of activity and silence was able to reproduce the pattern of correlations found in the experiments. Our findings shed light on the mechanisms causing neuronal variability and may contribute to elucidate its role in a neural code. 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 dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.
