The relative efficiency of modular and non-modular networks of different size.
The relative efficiency of modular and non-modular networks of different size.
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DOI：
10.1098/rspb.2014.2568
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发表时间：
2015-03-07
期刊：
影响因子：
--
通讯作者：
McNally L
中科院分区：
文献类型：
--
作者：
Tosh CR;McNally L
大多数生物网络是模块化的，但先前对小型模型网络的研究表明，模块化并不一定能提高功能效率。然而，大多数生物网络规模较大，在此我们研究了不同规模下模块化和非模块化神经网络的相对功能效率。我们对两种规模类别（“小”和“大”）的网络效率进行了详细分析，并对一系列网络规模进行了不太详细的分析。前者的分析表明，当网络较小时，模块化网络的效率低于所考虑的两种非模块化网络中的一种，但当网络较大时，它通常与两种非模块化网络效率相同或更高。后者的分析表明，在中小规模的网络中，模块化网络比相同（低）连接密度的非模块化网络效率高得多。例如，如果为了降低能量需求必须保持较低的连接密度，这可能会促进模块化。我们已经展示了相对功能/性能如何随网络规模而变化，但网络规模和模块化普遍性之间进化关系的确切性质将取决于连接的成本。
Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.
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发表时间：
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期刊：
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影响因子：
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