A characterization of the time-rescaled gamma process as a model for spike trains
A characterization of the time-rescaled gamma process as a model for spike trains
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DOI：
10.1007/s10827-009-0194-y
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发表时间：
2010-08-01
影响因子：
1.2
通讯作者：
Shinomoto, Shigeru
中科院分区：
文献类型：
--
作者：
Shimokawa, Takeaki;Koyama, Shinsuke;Shinomoto, Shigeru
神经元锋电位的发生不仅可以由放电的速率而且可以由放电的不规则性来表征。我们最近开发了一种贝叶斯方法，用于表征一系列的尖峰在瞬时率和不规则性，假设间尖峰间隔（IS IS）是从分布，其形状可能会随时间而变化。虽然ISI分布的任何参数化族都可以安装在贝叶斯方法中，但是检测点火特性的能力可能取决于分布族的选择。在这里，我们选择一组ISI指标，可以有效地表征尖峰模式，并确定可以提取这些特征的分布。基于统计正交性唯一地选择平均ISI和平均对数ISI的集合，并且相应地，对应的分布是伽马分布。通过对不同ISI分布（如对数正态分布和逆高斯分布）下的脉冲序列应用带有Gamma分布的Bayes方法，我们证实了Gamma分布能有效地提取脉冲序列的速率和形状因子
The occurrence of neuronal spikes may be characterized by not only the rate but also the irregularity of firing. We have recently developed a Bayes method for characterizing a sequence of spikes in terms of instantaneous rate and irregularity, assuming that mterspike intervals (IS Is) are drawn from a distribution whose shape may vary in time. Though any parameterized family of ISI distribution can be installed in the Bayes method, the ability to detect firing characteristics may depend on the choice of a family of distribution. Here, we select a set of ISI metrics that may effectively characterize spike patterns and determine the distribution that may extract these characteristics. The set of the mean ISI and the mean log ISI are uniquely selected based on the statistical orthogonality, and accordingly the corresponding distribution is the gamma distribution. By applying the Bayes method equipped with the gamma distribution to spike sequences derived from different ISI distributions such as the log-normal and Inverse-Gaussian distribution, we confirm that the gamma distribution effectively extracts the rate and the shape factor