Integrated Information - Theory, Estimation, and Application

传统的信息理论关注的是信息通过一个通道从发送者到接收者的流动。然而，在交互元素的分布式网络中，任何元素（或元素集合）都可以被认为是发送者或接收者。在这种情况下，当没有特定的感兴趣的渠道，也就没有主要的接收者或接收者时，我们如何应用信息论的原理来研究网络呢？综合信息试图捕捉一个直观的概念，一个物理系统集成信息，如果它指定更多的信息作为一个整体，而不是当它的部分被单独考虑。 整合信息最初是在神经科学中发展起来的，它试图捕捉大脑中观察到的分离和整合之间的平衡。各个大脑区域似乎执行专门的功能（分离），但共同工作以实现统一的反应（整合）。除了在神经科学中的应用，集成信息已被用作复杂性的一般度量，在自然科学中找到应用，包括生物学，计算机科学和物理学。 在今后五年中，我将同时寻求三种途径来推进综合信息领域： 发展对整合信息的理论理解。具体地说，我们使用信息几何的工具，以更好地理解物理系统中的信息结构。 改进估计综合信息的统计和计算技术。目前的方法将应用局限于小型系统，但更好的工具将开辟许多令人兴奋的可能应用。 展示集成信息在应用程序中的价值。申请将主要基于神经科学，但也探索其他学科的机会。 我的研究是高度跨学科和协作，并将在自然科学和工程的几个领域产生直接影响。预期的结果将提供一个新的（几何）的综合信息结构的角度。研究结果还将确定连续变量综合信息的定义，将现有的综合信息方法推广到新的应用领域。对综合信息的准确估计将为分析更大的模型提供新的机会。虽然最初将大脑视为相互作用的神经元系统，但集成信息已在其他自然科学领域（从生物学到物理学）中得到应用。估计综合信息的有效技术将促进因果模型和图形模型在这些领域的应用。

Traditional information theory is concerned with the flow of information through a channel, from sender to receiver. However, in distributed networks of interacting elements, any element (or set of elements) can be considered as a sender or a receiver. In such cases, when there is no specific channel of interest, and so no primary senders or receivers, how can we apply the principles of information theory to study the network? Integrated information attempts to capture an intuitive notion a physical system integrates information if it specifies more information as a whole than when its parts are considered independently. Originally developed in neuroscience, integrated information is an attempt to capture the balance between segregation and integration observed in the brain. Individual brain regions appear to perform specialized functions (segregation) yet work together to achieve a unified response (integration). In addition to applications in neuroscience, integrated information has been employed as a general measure of complexity, finding applications across the natural sciences, including biology, computer science, and physics. During the next five years I will concurrently pursue three avenues for advancing the field of integrated information: Develop the theoretical understand of integrated information. Specifically, we use the tools of information geometry to better understand the structure' of information in physical systems. Improve on statistical and computational techniques for estimating integrated information. Current methods limit applications to small systems, but better tools will open up many exciting possible applications. Demonstrate the value of integrated information in applications. Applications will be primarily based in neuroscience, but also exploring opportunities in other disciplines. My research is highly interdisciplinary and collaborative, and will have a direct impact on several fields within the natural sciences and engineering. The expected results will provide a new (geometric) perspective on the structure of integrated information. The results will also establish a definition of integrated information for continuous variables, exposing the existing methods of integrated information to new domains of application. Accurate estimates of integrated information will provide new opportunities to analyse larger models. While originally developed by considering the brain as a system of interacting neurons, integrated information has found applications in other natural science fields, from biology to physics. Validated techniques for estimating integrated information will stimulate the application of causal and graphical models in these fields.