Practical measures of integrated information for time-series data.
Practical measures of integrated information for time-series data.
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DOI：
10.1371/journal.pcbi.1001052
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发表时间：
2011-01-20
影响因子：
4.3
通讯作者：
Seth AK
中科院分区：
文献类型：
--
作者：
Barrett AB;Seth AK
最近的一项“综合信息”测量，ΦDM，量化了系统在状态之间转换时产生的信息超过其部分总和的程度，可能反映了神经系统产生的意识水平。然而，ΦDM仅定义为离散马尔可夫系统，这在生物学中是不寻常的;因此，ΦDM在实践中很少可以测量。在这里，我们描述了两个新的措施，ΦE和ΦAR，克服了这些限制，很容易应用到时间序列数据。我们使用模拟来证明我们的措施在实践中的适用性，并探讨其属性。我们的研究结果为研究真实的和模型系统中的信息整合提供了新的机会，并对整合信息、意识和其他神经认知过程之间的关系产生了影响。然而，我们的研究结果对将物理意义归因于测量量的理论提出了挑战。人类大脑的一个关键特征是它能够代表大量的信息，并整合这些信息以产生特定的和选择性的行为，以及一系列统一的意识场景。人们试图通过数学形式化一个系统作为一个整体产生的信息比其各部分之和多的程度来量化所谓的“综合信息”。然而，到目前为止，由此产生的措施已被证明是不适用于真实的神经系统。在本文中，我们介绍了两个新的措施，可以应用到现实的神经模型和时间序列数据从广泛的神经成像和电生理方法。我们的工作为研究整合信息在认知和意识中的作用，以及在任何复杂生物系统的功能中的作用提供了新的机会。然而，我们的研究结果也提出了挑战的理论，归因于一个直接的物理意义到目前为止描述的任何版本的综合信息。
A recent measure of ‘integrated information’, ΦDM, quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, ΦDM is defined only for discrete Markov systems, which are unusual in biology; as a result, ΦDM can rarely be measured in practice. Here, we describe two new measures, ΦE and ΦAR, that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities. A key feature of the human brain is its ability to represent a vast amount of information, and to integrate this information in order to produce specific and selective behaviour, as well as a stream of unified conscious scenes. Attempts have been made to quantify so-called ‘integrated information’ by formalizing in mathematics the extent to which a system as a whole generates more information than the sum of its parts. However, so far, the resulting measures have turned out to be inapplicable to real neural systems. In this paper we introduce two new measures that can be applied to both realistic neural models and to time-series data garnered from a broad range of neuroimaging and electrophysiological methods. Our work provides new opportunities for examining the role of integrated information in cognition and consciousness, and indeed in the function of any complex biological system. However, our results also pose challenges for theories that ascribe a direct physical meaning to any version of integrated information so far described.
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影响因子：
2.4
作者：
Barnett, L.;Buckley, C. L.;Bullock, S.
通讯作者：
Bullock, S.
DOI：
10.1073/pnas.0931349100
发表时间：
2003-04-29
影响因子：
11.1
作者：
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通讯作者：
Edelman, GM
影响因子：
4.3
作者：
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通讯作者：
Sporns O
DOI：
10.1073/pnas.0701519104
发表时间：
2007-06-12
影响因子：
11.1
作者：
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通讯作者：
Sporns, Olaf
影响因子：
14.5
作者：
Arthuis, Marie;Valton, Luc;Bartolomei, Fabrice
通讯作者：
Bartolomei, Fabrice