Neuroanatomical algorithms for dendritic modelling
Neuroanatomical algorithms for dendritic modelling
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DOI：
10.1088/0954-898x/13/3/301
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发表时间：
2002-08-01
影响因子：
7.8
通讯作者：
Ascoli, GA
中科院分区：
文献类型：
--
作者：
Ascoli, GA
树突形态的复杂性和多变性对神经系统结构-活性-功能关系的研究构成了巨大的挑战。计算建模最近已成为一个强大的方法，树突的定量解剖表征。其关键思想是设计一个随机算法来生成数字结构，这些数字结构在统计上与给定形态学类的真实的神经元的结构无法区分。该算法所使用的参数集将构成该形态类别的完整和准确的描述。我们回顾了主要类型的算法的优点和缺点，用于模型树状图的属性，包括那些基于分支直径和距离的索马。我们还描述了一些方法来模拟树枝状取向和三维几何形状。最后，我们讨论了环境对树突形态的影响（特别是轴突，其他神经元和解剖边界的存在），因此需要包括模型的组织体积的算法描述树突。
The complexity and variability of dendritic morphology constitutes a fascinating challenge to the investigation of the structure-activity-function relationship in the nervous system. Computational modelling has recently emerged as a powerful approach for the quantitative anatomical characterization of dendrites. The key idea is to design a stochastic algorithm to generate digital structures that are statistically indistinguishable from those of real neurons of a given morphological class. The set of parameters used by this algorithm would then constitute a complete and accurate description of that morphological class. We review the strengths and weaknesses of the major types of algorithms used to model dendrogram properties, including those based on branch diameter and on distance from the soma. We also describe some approaches to the simulation of dendritic orientation and three-dimensional geometry. Finally, we discuss the environmental influences on dendritic morphology (especially the presence of axons, other neurons, and anatomical boundaries) and thus the need to include models of the tissue volume in the algorithmic description of dendrites.