Integrated information in discrete dynamical systems: motivation and theoretical framework.
Integrated information in discrete dynamical systems: motivation and theoretical framework.
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离散动态系统中的集成信息：动机和理论框架。
DOI：
10.1371/journal.pcbi.1000091
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发表时间：
2008-06-13
影响因子：
4.3
通讯作者：
Tononi G
中科院分区：
文献类型：
--
作者：
Balduzzi D;Tononi G
本文介绍了一种时间和状态相关的综合信息度量，φ，它捕获了系统作为一个整体可用的因果状态库。具体而言，φ量化了当系统通过其元素之间的因果相互作用进入特定状态时，除了其部分独立生成的信息之外，还生成了多少信息（不确定性降低）。这样的数学表征是由这样的观察所激发的，即整合的信息捕获了意识的两个关键现象学性质：（i）有大量的意识经验，因此，当一个特定的经验发生时，它通过排除所有其他经验而产生大量的信息;（ii）这些信息是综合的，因为每一种经验都是一个整体，不能分解成独立的部分。本文扩展了以前的工作平稳系统和应用集成信息离散网络作为其动态和因果结构的功能。对基本例子的分析表明：（1）φ根据网络进入的状态而变化，如果活跃和不活跃的元素平衡，φ就较高，如果网络不活跃或过度活跃，φ就较低。(ii)对于具有相同或相似的表面动态的系统，φ根据潜在的因果结构而变化，对于仅仅复制或重放活动状态的系统，φ较低。(iii)φ作为网络架构的函数而变化。高φ值可以通过将功能专门化与功能集成结合起来的架构来获得。严格的模块化和同构系统不能产生高φ，因为前者缺乏集成，而后者缺乏信息。前馈和网格架构能够产生高φ，但效率低下。(iv)在Hopfield网络中，对于吸引子状态和中性状态，φ很低，但如果网络被优化以实现局部和全局相互作用之间的张力，φ就会增加。这些基本的例子似乎与有关意识的神经基质的神经生物学证据相吻合。更一般地说，φ似乎是表征任何物理系统整合信息的能力的有用度量。我们已经提出，意识与系统产生整合信息的能力有关。这一建议源于对意识的两个基本属性的考虑：（1）通过排除其他经验，每个意识经验都会产生大量信息;（2）信息是整合的，这意味着它不能被分解为独立的部分。我们引入了一个措施，量化有多少集成的信息是由一个离散的动态系统从一个状态过渡到下一个的过程中产生的。该度量捕获了系统元素之间的因果相互作用所产生的信息，这些信息超出了系统各部分独立产生的信息。我们提出了基本的例子，这与神经生物学证据相匹配的数值分析有关的神经基板的consciousness. The框架建立了一个独立的信息的观点，采取内在的相互作用的角度。
This paper introduces a time- and state-dependent measure of integrated information, φ, which captures the repertoire of causal states available to a system as a whole. Specifically, φ quantifies how much information is generated (uncertainty is reduced) when a system enters a particular state through causal interactions among its elements, above and beyond the information generated independently by its parts. Such mathematical characterization is motivated by the observation that integrated information captures two key phenomenological properties of consciousness: (i) there is a large repertoire of conscious experiences so that, when one particular experience occurs, it generates a large amount of information by ruling out all the others; and (ii) this information is integrated, in that each experience appears as a whole that cannot be decomposed into independent parts. This paper extends previous work on stationary systems and applies integrated information to discrete networks as a function of their dynamics and causal architecture. An analysis of basic examples indicates the following: (i) φ varies depending on the state entered by a network, being higher if active and inactive elements are balanced and lower if the network is inactive or hyperactive. (ii) φ varies for systems with identical or similar surface dynamics depending on the underlying causal architecture, being low for systems that merely copy or replay activity states. (iii) φ varies as a function of network architecture. High φ values can be obtained by architectures that conjoin functional specialization with functional integration. Strictly modular and homogeneous systems cannot generate high φ because the former lack integration, whereas the latter lack information. Feedforward and lattice architectures are capable of generating high φ but are inefficient. (iv) In Hopfield networks, φ is low for attractor states and neutral states, but increases if the networks are optimized to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, φ appears to be a useful metric to characterize the capacity of any physical system to integrate information. We have suggested that consciousness has to do with a system's capacity to generate integrated information. This suggestion stems from considering two basic properties of consciousness: (i) each conscious experience generates a large amount of information, by ruling out alternative experiences; and (ii) the information is integrated, meaning that it cannot be decomposed into independent parts. We introduce a measure that quantifies how much integrated information is generated by a discrete dynamical system in the process of transitioning from one state to the next. The measure captures the information generated by the causal interactions among the elements of the system, above and beyond the information generated independently by its parts. We present numerical analyses of basic examples, which match well against neurobiological evidence concerning the neural substrates of consciousness. The framework establishes an observer-independent view of information by taking an intrinsic perspective on interactions.
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