Greenwood Priscilla E, McDonnell Mark D, Ward Lawrence M
Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Neural Comput. 2015 Jan;27(1):74-103. doi: 10.1162/NECO_a_00688.
In this letter, we provide a stochastic analysis of, and supporting simulation data for, a stochastic model of the generation of gamma bursts in local field potential (LFP) recordings by interacting populations of excitatory and inhibitory neurons. Our interest is in behavior near a fixed point of the stochastic dynamics of the model. We apply a recent limit theorem of stochastic dynamics to probe into details of this local behavior, obtaining several new results. We show that the stochastic model can be written in terms of a rotation multiplied by a two-dimensional standard Ornstein-Uhlenbeck (OU) process. Viewing the rewritten process in terms of phase and amplitude processes, we are able to proceed further in analysis. We demonstrate that gamma bursts arise in the model as excursions of the modulus of the OU process. The associated pair of stochastic phase and amplitude processes satisfies their own pair of stochastic differential equations, which indicates that large phase slips occur between gamma bursts. This behavior is mirrored in LFP data simulated from the original model. These results suggest that the rewritten model is a valid representation of the behavior near the fixed point for a wide class of models of oscillatory neural processes.
在这封信中,我们对由兴奋性和抑制性神经元相互作用群体在局部场电位(LFP)记录中产生伽马暴的随机模型进行了随机分析,并提供了支持性模拟数据。我们关注的是该模型随机动力学固定点附近的行为。我们应用随机动力学的一个最新极限定理来探究这种局部行为的细节,从而获得了几个新结果。我们表明,该随机模型可以表示为一个旋转乘以一个二维标准奥恩斯坦 - 乌伦贝克(OU)过程。从相位和幅度过程的角度来看待重新表述后的过程,我们能够进一步进行分析。我们证明,在该模型中,伽马暴作为OU过程模的偏移出现。相关的一对随机相位和幅度过程满足它们自己的一对随机微分方程,这表明在伽马暴之间会发生大的相位跳跃。从原始模型模拟得到的LFP数据也反映了这种行为。这些结果表明,对于一大类振荡神经过程模型,重新表述后的模型是固定点附近行为的有效表示。