Synergy, redundancy, and multivariate information measures: an experimentalist's perspective
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective
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DOI：
10.1007/s10827-013-0458-4
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发表时间：
2014-04-01
影响因子：
1.2
通讯作者：
Beggs, John M.
中科院分区：
文献类型：
--
作者：
Timme, Nicholas;Alford, Wesley;Beggs, John M.
信息理论长期以来一直被用来量化两个变量之间的相互作用。随着复杂系统研究的兴起，多变量信息测度越来越多地用于研究三个或更多变量的组之间的相互作用，通常强调所谓的协同和冗余相互作用。虽然双变量信息度量通常是一致同意的，但今天使用的多变量信息度量已经由许多不同的团体开发，并且在细微但重要的方面有所不同。在这里，我们将审查这些多元信息的措施，特别强调他们的关系，协同和冗余，以及检查这些措施之间的差异，通过将它们应用到几个简单的模型系统。除了这些系统，我们将说明有用的信息措施，通过分析神经尖峰数据从一个分离的文化，通过其发展的早期阶段。我们的目标是，这项工作将有助于其他研究人员，因为他们寻求最好的多元信息措施，为他们的具体研究目标和系统。最后，我们已经在线提供软件，允许用户计算本文讨论的所有信息措施。
Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussedwithin this paper.