Analytical estimates of limited sampling biases in different information measures
Analytical estimates of limited sampling biases in different information measures
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DOI：
10.1088/0954-898x/7/1/006
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发表时间：
1996-02-01
影响因子：
7.8
通讯作者：
Treves, A
中科院分区：
文献类型：
--
作者：
Panzeri, S;Treves, A
由于通常可获得的数据量有限，导致系统误差，测量神经元活动携带的信息变得困难，特别是在从哺乳动物细胞进行记录时。虽然经验特别程序已被用来校正这种误差，但我们最近提出了一种直接程序，包括对平均误差的分析计算，从数据中估计它(至多至子载项)，并从原始信息度量中减去它，以产生无偏度量。我们在这里计算平均传输信息和条件信息的前导校正项，并且由于通常必须首先正则化数据，所以我们指定适合于不同正则化的表达式。计算机模拟表明了分析结果的广泛有效性，表明了通过简单的入库来正则化的有效性，并说明了这种方法相对于以前使用的‘bootstrap’过程的优势。
Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. While empirical ad hoc procedures have been used to correct for such error, we have recently proposed a direct procedure consisting of the analytical calculation of the average error, its estimation (up to subleading terms) from the data, and its subtraction from raw information measures to yield unbiased measures. We calculate here the leading correction terms for both the average transmitted information and the conditional information and, since usually one must first regularize the data, we specify the expressions appropriate to different regularizations. Computer simulations indicate a broad range of validity of the analytical results, suggest the effectiveness of regularizing by simple binning and illustrate the advantage of this over the previously used 'bootstrap' procedure.