Drawing inferences from Fano factor calculations
Drawing inferences from Fano factor calculations
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DOI：
10.1016/j.jneumeth.2010.04.012
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发表时间：
2010-06-30
影响因子：
3
通讯作者：
Kramer, Mark A.
中科院分区：
文献类型：
--
作者：
Eden, Uri T.;Kramer, Mark A.
神经放电的一个重要特征是一组区间内放电计数的方差与均值之比--Fano因子。对于泊松过程，理论上的Fano因子正好是1。对于模拟或实验神经数据，样本Fano因子从来都不是1，但经常接近1。在这个简短的交流中，我们描述了泊松过程的Fano因子的分布，使我们能够计算概率界限并对记录的神经尖峰计数的分布进行假设检验。我们表明，对于泊松过程的Fano因子渐近遵循伽玛分布与尖峰计数的观测数的依赖，收敛到这个渐近分布是快速的。该分析提供了一种简单的方法来确定计算的Fano因子应接近1的程度，并正式测试观察到的尖峰变化是否可能出现在泊松过程生成的数据中。(C)2010 Elsevier B. V.保留所有权利。
An important characterization of neural spiking is the ratio of the variance to the mean of the spike counts in a set of intervals-the Fano factor. For a Poisson process, the theoretical Fano factor is exactly one. For simulated or experimental neural data, the sample Fano factor is never exactly one, but often appears close to one. In this short communication, we characterize the distribution of the Fano factor for a Poisson process, allowing us to compute probability bounds and perform hypothesis tests on the distribution of recorded neural spike counts. We show that for a Poisson process the Fano factor asymptotically follows a gamma distribution with dependence on the number of observations of spike counts, and that convergence to this asymptotic distribution is fast. The analysis provides a simple method to determine how close to 1 the computed Fano factor should be and to formally test whether the observed variability in the spiking is likely to arise in data generated by a Poisson process. (C) 2010 Elsevier B.V. All rights reserved.