Zhou Pengcheng, Burton Shawn D, Snyder Adam C, Smith Matthew A, Urban Nathaniel N, Kass Robert E
Program for Neural Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.
Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
PLoS Comput Biol. 2015 Oct 14;11(10):e1004549. doi: 10.1371/journal.pcbi.1004549. eCollection 2015 Oct.
Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.
成对的活跃神经元经常几乎同步地发放动作电位或“尖峰”（即彼此之间在5毫秒内）。这种尖峰同步可能是偶然发生的，仅仅基于神经元波动的发放模式，或者它可能发生得过于频繁，无法仅用偶然性来解释。当出现高于偶然水平的尖峰同步时，它可能有助于特定认知过程的计算，或者可能是这种计算的无关副产品。无论哪种方式，尖峰同步都是神经数据的一个特征，应该得到解释。以前已经为此目的开发了一个点过程回归框架，使用广义线性模型（GLM）。在这个框架中，将观察到的同步尖峰数量与在关于影响每个单个神经元发放率函数的因素的不同假设下偶然预测的数量进行比较。尖峰同步的一个重要可能来源是全网络振荡，它可能提供网络信息流的基本机制。为了建立尖峰同步与全网络振荡之间的统计联系，我们将振荡场电位整合到我们的点过程回归框架中。我们首先扩展了一个先前发表的尖峰-场关联模型，并表明我们可以恢复振荡场电位与发放率之间的相位关系。然后，我们使用这个新框架来证明振荡场电位与尖峰同步在以下方面的统计关系：1）模拟神经元，2）海马CA1锥体细胞的体外记录，以及3）新皮层V4神经元的体内记录。我们的结果提供了一种严格的方法来建立网络振荡与神经同步之间的统计联系。
