Large-scale optimization of neuron arbors
Large-scale optimization of neuron arbors
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DOI：
10.1103/physreve.59.6001
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发表时间：
1999-05-01
影响因子：
2.4
通讯作者：
Kang, DW
中科院分区：
文献类型：
--
作者：
Cherniak, C;Changizi, M;Kang, DW
在全局和局部尺度上，神经元乔木类型的某些几何形状（包括树突和轴突）似乎是自组织的：它们的形态发生表现得像流水，即流体动态；分支网络中的水流又像一棵由受拉力的绳索组成的树一样，即机械矢量。分支直径和角度以及连接位置与该模型显着一致。结果是，此类神经元树样本全局最小化其总体积，而不是表面积或分支长度。此外，心轴在生成互连终端的最便宜拓扑方面表现良好：它们的大规模布局是所有此类可能连接模式中最好的，接近最佳布局的 5%。该模型也同样适用于干道和河流网络。 [S1063-651X(99)16205-6]。
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. [S1063-651X(99)16205-6].