Measuring Integrated Information from the Decoding Perspective.
Measuring Integrated Information from the Decoding Perspective.
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DOI：
10.1371/journal.pcbi.1004654
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发表时间：
2016-01
影响因子：
4.3
通讯作者：
Tsuchiya N
中科院分区：
文献类型：
--
作者：
Oizumi M;Amari S;Yanagawa T;Fujii N;Tsuchiya N
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。意识的整合信息理论（IIT）提供了一种数学方法来量化整合在系统中的信息，称为整合信息Φ。综合信息在理论上被定义为一个系统作为一个整体产生的信息量，超过了它的各个部分独立产生的信息量。IIT预测，大脑中整合信息的数量应该反映意识水平。该理论的实证评估需要根据实验获得的神经数据计算综合信息，尽管使用原始测量值Φ的困难排除了此类计算。虽然以前已经提出了一些实际的措施，我们发现，这些措施不能满足理论要求的综合信息的措施。综合信息的度量应满足以下下限和上限：综合信息的下限应为0，并且当系统不产生信息（无信息）或系统包含独立部分（无综合）时等于0。综合信息的上界是整个系统产生的信息量。在这里，我们通过引入从信息论发展而来的失配解码的概念来推导新的实用度量Φ*。我们表明，Φ* 是适当的有界从下面和上面，作为一个综合信息的措施。我们在高斯假设下推导了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综合信息的测量，也可以用作生物学不同领域网络分析的工具。意识的整合信息理论（IIT）吸引了研究意识的科学家，因为它对理解意识的神经特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，称为整合信息Φ。集成信息度量的是系统作为一个整体所产生的超出其各部分独立产生的信息量的多余信息。虽然IIT预测间接支持了大量的实验，验证需要通过量化直接从实验神经数据的综合信息。实际困难是缺乏直接的数量支助的原因。针对这些困难，提出了几种实用的信息集成措施。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措施的综合信息，指定为Φ*，满足这些理论要求，通过引入不匹配的解码从信息理论的概念。Φ* 创造了IIT的经验和定量验证的可能性，以获得对意识神经基础的新见解。
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 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 and is equal to 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. 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 of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology. 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 insights into the neural basis of consciousness.