Information processing in dendrites - II. Information theoretic complexity
Information processing in dendrites - II. Information theoretic complexity
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DOI：
10.1016/s0893-6080(01)00085-5
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发表时间：
2001-10-01
期刊：
影响因子：
7.8
通讯作者：
Gurney, KN
中科院分区：
文献类型：
--
作者：
Gurney, KN
在配套文件中，我们建立了一个基本原理，探索树突状处理使用一类布尔函数的多立方体单位（MCU）。在这里，我们使用这种方法来进一步使用信息论和复杂性研究的思想来进行树突状处理。起点是布尔函数的总互信息（输入变量和输出之间）的一种新的分解。分解的每个分量是相对于单个输入的互信息度量，以剩余输入的子集为条件。我们称这种分解为信息谱，并将其视为信息域中函数的重新表示。此外，布尔函数的信息谱可以使用由Pincus（Pincus，S. M.（1991年）。近似熵作为系统复杂性的度量。美国国家科学院院刊Sci. USA，88，2297-2301）。使用蒙特卡罗方法，我们提供的证据表明，微控制器的信息谱复杂性是大于任何其他类型的布尔函数。我们解释这种现象的信息流通过2级MCU架构。在我们的建模假设下，对生物神经处理的影响是，树突实现的函数相对于从中提取它们的多元函数类具有最大信息谱复杂度。(C)2001爱思唯尔科技有限公司版权所有。
In the companion paper, we established a rationale for exploring the general principles of dendritic processing using a class of Boolean functions-the Multi-Cube Units (MCUs). Here, we use this approach to further characterise dendritic processing using ideas from information theory and studies in complexity. The starting point is a novel decomposition of a Boolean function's total mutual information (between input variables and the output). Each component of the decomposition is a mutual information measure with respect to a single input, conditioned on a subset of the remaining inputs. We call this decomposition the information spectrum and conceive of it as a re-representation of the function in the information domain. Furthermore, the information spectrum of a Boolean function may be assigned a complexity value using the approximate entropy introduced by Pincus (Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proc. Natl, Acad. Sci. USA, 88, 2297-2301). Using Monte Carlo methods, we provide evidence that the information spectral complexity of MCUs is larger than that of any other class of Boolean function. We explain this phenomenon in terms of information flow through the 2-stage MCU architecture. Under our modelling assumptions, the implication for biological neural processing is that dendrites implement functions that have maximal information spectral complexity with respect to the class of multivariate functions from which they are drawn. (C) 2001 Elsevier Science Ltd. All rights reserved.