Information theory has been used as an organizing principle in neuroscience for several decades. Estimates of the mutual information (MI) between signals acquired in neurophysiological experiments are believed to yield insights into the structure of the underlying information processing architectures. With the pervasive availability of recordings from many neurons, several information and redundancy measures have been proposed in the recent literature. A typical scenario is that only a small number of stimuli can be tested, while ample response data may be available for each of the tested stimuli. The resulting asymmetric information estimation problem is considered. It is shown that the direct plug-in information estimate has a negative bias. An anthropic correction is introduced that has a positive bias. These two complementary estimators and their combinations are natural candidates for information estimation in neuroscience. Tail and variance bounds are given for both estimates. The proposed information estimates are applied to the analysis of neural discrimination and redundancy in the avian auditory system.

中文翻译：

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信息估计的人为校正及其在神经编码中的应用

几十年来，信息论一直被用作神经科学的组织原则。在神经生理学实验中获得的信号之间的互信息 (MI) 估计被认为可以深入了解底层信息处理架构的结构。随着许多神经元记录的普遍可用性，最近的文献中提出了几种信息和冗余措施。一个典型的场景是只能测试少量的刺激，而对于每个被测试的刺激可能有充足的响应数据可用。考虑了由此产生的非对称信息估计问题。结果表明，直接插件信息估计具有负偏差。引入了具有正偏差的人为校正。这两个互补的估计量及其组合是神经科学中信息估计的自然候选者。两个估计都给出了尾部和方差界限。建议的信息估计应用于分析鸟类听觉系统中的神经辨别力和冗余度。