Luczak Artur
Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave., Newark, NJ 07102, USA.
J Neurosci Methods. 2006 Oct 15;157(1):132-41. doi: 10.1016/j.jneumeth.2006.03.024. Epub 2006 May 11.
The relative importance of the intrinsic and extrinsic factors determining the variety of geometric shapes exhibited by dendritic trees remains unclear. This question was addressed by developing a model of the growth of dendritic trees based on diffusion-limited aggregation process. The model reproduces diverse neuronal shapes (i.e., granule cells, Purkinje cells, the basal and apical dendrites of pyramidal cells, and the axonal trees of interneurons) by changing only the size of the growth area, the time span of pruning, and the spatial concentration of 'neurotrophic particles'. Moreover, the presented model shows how competition between neurons can affect the shape of the dendritic trees. The model reveals that the creation of complex (but reproducible) dendrite-like trees does not require precise guidance or an intrinsic plan of the dendrite geometry. Instead, basic environmental factors and the simple rules of diffusive growth adequately account for the spatial embedding of different types of dendrites observed in the cortex. An example demonstrating the broad applicability of the algorithm to model diverse types of tree structures is also presented.
决定树突状树呈现出各种几何形状的内在和外在因素的相对重要性仍不清楚。通过基于扩散限制聚集过程开发一个树突状树生长模型来解决这个问题。该模型仅通过改变生长区域的大小、修剪的时间跨度和“神经营养颗粒”的空间浓度，就能再现多种神经元形状（即颗粒细胞、浦肯野细胞、锥体细胞的基底和顶端树突以及中间神经元的轴突树）。此外，所提出的模型展示了神经元之间的竞争如何影响树突状树的形状。该模型表明，复杂（但可重复）的树突状树的形成并不需要精确的引导或树突几何形状的内在规划。相反，基本的环境因素和扩散生长的简单规则足以解释在皮层中观察到的不同类型树突的空间嵌入。还给出了一个示例，展示了该算法对多种类型树结构建模的广泛适用性。
