Correlation transfer in stochastically driven neural oscillators over long and short time scales
Correlation transfer in stochastically driven neural oscillators over long and short time scales
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DOI：
10.1103/physreve.84.061914
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发表时间：
2011-12-20
影响因子：
2.4
通讯作者：
Ermentrout, Bard
中科院分区：
文献类型：
--
作者：
Abouzeid, Aushra;Ermentrout, Bard
在没有突触耦合的情况下，两个或更多个神经振荡器可以凭借其噪声输入流中的统计相关性而变得同步。最近的工作表明，从输入电流到输出尖峰的相关性转移的程度不仅取决于固有的振荡器动态，而且还取决于计算相关性的观察窗口的长度。在本文中，我们使用随机相位减少和经常扰动，推导出的相关性的总相位经过了很长的时间尺度，数量，提供了一个方便的代理的尖峰计数相关性。在短时间尺度上，我们推导出的尖峰计数相关直接使用简单的概率推理适用于密度的相位差。我们的近似表明，输出相关尺度与相位重置曲线的自相关性在长时间尺度上。我们还找到了一个简洁的表达的影响的相位复位曲线的形状上的输出相关的初始斜率在短的时间尺度。这些分析结果与数值模拟提供了新的直觉，最近的反直觉的发现，I型振荡器传输相关性更忠实地比II型在长时间尺度，而相反的情况下，更好地理解短的时间尺度。
In the absence of synaptic coupling, two or more neural oscillators may become synchronized by virtue of the statistical correlations in their noisy input streams. Recent work has shown that the degree of correlation transfer from input currents to output spikes depends not only on intrinsic oscillator dynamics, but also on the length of the observation window over which the correlation is calculated. In this paper we use stochastic phase reduction and regular perturbations to derive the correlation of the total phase elapsed over long time scales, a quantity that provides a convenient proxy for the spike count correlation. Over short time scales, we derive the spike count correlation directly using straightforward probabilistic reasoning applied to the density of the phase difference. Our approximations show that output correlation scales with the autocorrelation of the phase resetting curve over long time scales. We also find a concise expression for the influence of the shape of the phase resetting curve on the initial slope of the output correlation over short time scales. These analytic results together with numerical simulations provide new intuitions for the recent counterintuitive finding that type I oscillators transfer correlations more faithfully than do type II over long time scales, while the reverse holds true for the better understood case of short time scales.