Malleability of gamma rhythms enhances population-level correlations
Malleability of gamma rhythms enhances population-level correlations
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DOI：
10.1007/s10827-021-00779-4
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发表时间：
2021-04-05
影响因子：
1.2
通讯作者：
Young，Lai-Sang
中科院分区：
文献类型：
--
作者：
Saraf，Sonica;Young，Lai-Sang
系统神经科学中的一个重要问题是了解信息是如何在大脑区域之间进行交流的，并且已经提出交流是由神经元振荡介导的，例如伽马波段的节律。我们试图通过使用具有两个组件的网络模型来研究这个想法，源（发送）和目标（接收）组件，两者都被构建为类似于大脑皮层中的局部人群。为了衡量沟通的有效性，我们使用了源和目标之间尖峰时间的群体水平相关性。我们发现，校正后的响应时间是独立的初始条件，尖峰时间之间的源和目标的相关性是显着的，由于在很大程度上对齐的发射事件在其伽马节律。但是，我们也发现，规则振荡不能产生我们在皮层神经元模型模拟中观察到的结果。令人惊讶的是，正是他们伽马节律的不规则性，内部时钟的缺失，以及这些节律的易变性和它们与外部脉冲一致的趋势--这些特征已知存在于真实的皮层的伽马节律中--产生了观察到的结果。这些发现和我们提供的机理解释是我们的主要结果。我们的第二个结果是相关性和用于计算的样本大小之间的数学关系。随着技术的改进，可以同时记录越来越多的神经元，这种关系可能有助于解释实验记录的结果。
An important problem in systems neuroscience is to understand how information is communicated among brain regions, and it has been proposed that communication is mediated by neuronal oscillations, such as rhythms in the gamma band. We sought to investigate this idea by using a network model with two components, a source (sending) and a target (receiving) component, both built to resemble local populations in the cerebral cortex. To measure the effectiveness of communication, we used population-level correlations in spike times between the source and target. We found that after correcting for a response time that is independent of initial conditions, spike-time correlations between the source and target are significant, due in large measure to the alignment of firing events in their gamma rhythms. But, we also found that regular oscillations cannot produce the results observed in our model simulations of cortical neurons. Surprisingly, it is theirregularityof gamma rhythms, the absence of internal clocks, together with themalleabilityof these rhythms and their tendency to align with external pulses — features that are known to be present in gamma rhythms in the real cortex — that produced the results observed. These findings and the mechanistic explanations we offered are our primary results. Our secondary result is a mathematical relationship between correlations and the sizes of the samples used for their calculation. As improving technology enables recording simultaneously from increasing numbers of neurons, this relationship could be useful for interpreting results from experimental recordings.