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神经元树突的大规模优化

Large-scale optimization of neuron arbors.

作者信息

Cherniak C, Changizi M, Kang D

机构信息

Committee on History and Philosophy of Science, Department of Philosophy, University of Maryland, College Park, Maryland 20742, USA.

出版信息

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1999 May;59(5 Pt B):6001-9. doi: 10.1103/physreve.59.6001.

Abstract

At the global as well as local scales, some of the geometry of types of neuron arbors-both dendrites and axons-appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform significantly to this model. The result is that such neuron tree samples globally minimize their total volume-rather than, for example, surface area or branch length. In addition, the arbors perform well at generating the cheapest topology interconnecting their terminals: their large-scale layouts are among the best of all such possible connecting patterns, approaching 5% of optimum. This model also applies comparably to arterial and river networks.

摘要

在全球和局部尺度上,某些类型的神经元树突(包括树突和轴突)的几何形状似乎是自组织的:它们的形态发生表现得像流水一样,也就是说,具有流体动力学特性;分支网络中的水流又像由处于张力下的绳索组成的树一样,即具有矢量机械特性。分支直径、角度和连接点都与该模型高度吻合。结果是,这样的神经元树样本在整体上使它们的总体积最小化——而不是例如表面积或分支长度。此外,这些树突在生成连接其末端的最便宜拓扑结构方面表现出色:它们的大规模布局是所有此类可能连接模式中最好的之一,接近最优值的5%。该模型同样适用于动脉网络和河流网络。

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