ar X iv : 1 50 5 . 04 36 8 v 1 [ q-bi o . N C ] 1 7 M ay 2 01 5 1 Measuring integrated information from the decoding perspective
ar X iv : 1 50 5 . 04 36 8 v 1 [ q-bi o . N C ] 1 7 M ay 2 01 5 1 Measuring integrated information from the decoding perspective
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ar Xiv：1 50 5。
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
N. Tsuchiya
中科院分区：
文献类型：
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作者：
Masafumi Oizumi;S. Amari;T. Yanagawa;N. Fujii;N. Tsuchiya
越来越多的证据表明，大脑整合信息的能力是意识的先决条件。意识的综合信息理论提供了一种数学方法来量化系统中综合的信息，称为综合信息，Φ。从理论上讲，综合信息的定义是系统作为一个整体产生的信息量，超过其各部分独立产生的信息量的总和。IIT预测，大脑中整合的信息量应该反映意识的水平。对这一理论的经验评估需要从实验获得的神经数据计算综合信息，尽管使用原始测量Φ的困难排除了这种计算。虽然之前已经提出了一些实际的措施，但我们发现这些措施并不能满足作为综合信息衡量标准的理论要求。集成信息的度量应满足如下上下界：当系统不生成信息(无信息)或当系统包含独立部分(无集成)时，集成信息的下界应为0。综合信息量的上界是整个系统产生的信息量，当其各部分独立产生的信息量等于0时实现。在这里，我们通过引入信息论中的失配译码的概念，推导出一种新的实用度量Φ。我们证明，作为综合信息的一种度量，Φ从下到上都有适当的界限。我们在高斯假设下导出了Φ的解析表达式，这使得它很容易适用于实验数据。我们的新测量Φ通常可以用作意识研究中的综合信息的测量，也可以作为生物学不同领域研究中的网络分析工具。作者摘要意识的综合信息理论(IIT)吸引了研究意识的科学家，因为它对理解意识的神经属性具有解释和预测能力。IIT预测，意识水平与大脑中整合的信息量有关，这被称为整合信息Φ。综合信息衡量的是系统作为一个整体产生的超出其各部分独立产生的信息量的过剩信息。尽管IIT预测得到了大量实验的间接支持，但需要通过直接从实验神经数据中量化综合信息来进行验证。实际困难是缺乏直接的、定量的支持的原因。为了解决这些困难，提出了几项切实可行的信息集成措施。然而，我们发现，这些措施并不满足集成信息的理论要求：第一，集成信息不应低于0；第二，集成信息不应超过整个系统产生的信息量。在这里，我们提出了一种新的实用的综合信息度量，称为Φ，它通过引入从信息论发展而来的失配解码的概念来满足这些理论要求。Φ创造了对个人所得税进行实证和定量验证的可能性，以获得新颖性
Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the sum of the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system and is realized when the amount of information generated independently by its parts equals to 0. Here we derive the novel practical measure Φ by introducing a concept of mismatched decoding developed from information theory. We show that Φ is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression Φ under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ can be generally used as a measure of integrated information in research on consciousness, and also as a tool for network analysis in research on diverse areas of biology. Author Summary Integrated Information Theory (IIT) of consciousness attracts scientists who investigate consciousness owing to its explanatory and predictive powers for understanding the neural properties of consciousness. IIT predicts that the levels of consciousness are related to the quantity of information integrated in the brain, which is called integrated information Φ. Integrated information measures excess information generated by a system as a whole above and beyond the amount of information independently generated by its parts. Although IIT predictions are indirectly supported by numerous experiments, validation is required through quantifying integrated information directly from experimental neural data. Practical difficulties account for the absence of direct, quantitative support. To resolve these difficulties, several practical measures of integrated information have been proposed. However, we found that these measures do not satisfy the theoretical requirements of integrated information: first, integrated information should not be below 0; and second, integrated information should not exceed the quantity of information generated by the whole system. Here, we propose a novel practical measure of integrated information, designated as Φ that satisfies these theoretical requirements by introducing the concept of mismatched decoding developed from information theory. Φ creates the possibility of empirical and quantitative validations of IIT to gain novel