Measuring information integration.

Measuring information integration.
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DOI:
10.1186/1471-2202-4-31
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发表时间:
2003-12-02
期刊:
影响因子:
2.4
通讯作者:
Sporns O
Sporns O
中科院分区:
医学4区
文献类型:
--
作者:
Tononi G;Sporns O

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为了理解大脑等分布式网络的功能,表征它们整合信息的能力非常重要。本文考虑了一种基于有效信息的度量,即捕获系统两个部分之间可能发生的所有因果相互作用的量。集成信息的能力(或 Φ)由子集的两个互补部分之间可以交换的最小有效信息量给出。结果表明,该度量可用于识别可以集成信息的系统子集或复合体。该分析适用于连接组织不同的理想化神经系统。结果表明,通过让每个元素与复合体的其余部分形成不同的连接模式(功能专业化),同时确保可以在网络的任何二部分之间交换大量信息(功能集成),可以最大化 Φ。根据这一分析,某些神经结构(例如丘脑皮质系统)的连接组织非常适合信息整合,而其他神经结构(例如小脑)的连接组织则不适合,从而产生显着的功能后果。所提出的信息集成分析应适用于其他系统和网络。
To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. The capacity to integrate information, or Φ, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Φ is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.
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