Costa Luciano da Fontoura, Manoel Edson Tadeu Monteiro
Cybernetic Vision Research Group, IFSC-USP, Caixa Postal 369, 13560-970, São Carlos, SP, Brazil.
Neuroinformatics. 2003;1(1):65-80. doi: 10.1385/ni:1:1:065.
This article addresses the issues of neural shape characterization and analysis from the perspective of one of the main roles played by neural shapes, namely, connectivity. This study is oriented toward the geometry at the individual cell level and involves the use of the percolation concept from statistical mechanics, which is reviewed in an accessible fashion. The characterization of the neural cell geometry with respect to connectivity is performed in terms of critical percolation probability obtained experimentally while considering several types of geometrical interactions between cells, therefore directly expressing the potential for connections defined by each situation. Two basic situations are considered: dendrite-dendrite and dendrite-axon interactions. The obtained results corroborate the potential of the critical percolation probability as a valuable resource for characterizing, classifying, and analyzing the morphology of neural cells.
本文从神经形状所起的主要作用之一，即连通性的角度，探讨神经形状表征与分析的问题。本研究针对单个细胞水平的几何学，并涉及使用统计力学中的渗流概念，以通俗易懂的方式对其进行了综述。关于连通性的神经细胞几何特征是根据实验获得的临界渗流概率来进行的，同时考虑了细胞之间几种类型的几何相互作用，从而直接表达了每种情况所定义的连接潜力。考虑了两种基本情况：树突 - 树突和树突 - 轴突相互作用。所得结果证实了临界渗流概率作为表征、分类和分析神经细胞形态的宝贵资源的潜力。
