Stochastic transitions into silence cause noise correlations in cortical circuits
Stochastic transitions into silence cause noise correlations in cortical circuits
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DOI：
10.1073/pnas.1410509112
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发表时间：
2015-03-17
影响因子：
11.1
通讯作者：
de la Rocha, Jaime
中科院分区：
文献类型：
--
作者：
Mochol, Gabriela;Hermoso-Mendizabal, Ainhoa;de la Rocha, Jaime
皮层神经元的尖峰活动是高度可变的。这种变异性通常与附近的神经元相关，这种效应通常被解释为反映了由于解剖学上共享的输入而引起的神经元的共激活。然而，最近的研究结果表明，相关性可以动态调制，这表明其潜在的机制还没有得到很好的理解。在这里，我们调查的假设，相关性占主导地位的神经元coinactivation：短暂的沉默期期间，在本地网络中的所有神经元停止发射的发生。我们记录了不同大脑状态下麻醉大鼠听觉皮层中大量神经元的尖峰活动。在自发活动期间，伴随大脑状态去同步的相关性的降低在很大程度上被解释为沉默期密度的降低。一个刺激的呈现会导致相关性的最初下降，然后反弹，这是一个由瞬时沉默密度模仿的时间过程。我们建立了一个速率网络模型，在一个沉默的和一个活跃的吸引子之间具有波动驱动的转换，并假设神经元发射泊松尖峰序列，其速率遵循模型动力学。网络外部输入的变化改变了到无声吸引子的过渡率，并再现了在自发和诱发条件下数据中发现的相关性和沉默密度之间的关系。这表明，所观察到的相关性变化，随着大脑状态的变化而逐渐发生，或者随着感觉刺激而突然发生，是由于微电路暂时停止放电的可能性的变化。
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 de-synchronization 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.