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首页> 外文期刊>Brain research >Parsimonious modelling allows generation of the dendrograms of primate striatal medium spiny and pallidal type II neurons using a stochastic algorithm.
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Parsimonious modelling allows generation of the dendrograms of primate striatal medium spiny and pallidal type II neurons using a stochastic algorithm.

机译:简约建模允许使用随机算法生成灵长类纹状体中等棘和苍白II型神经元的树状图。

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摘要

Data from quantitative three-dimensional analysis of primate striatal medium spiny neurons (MSNs) and pallidal type I and type II neurons were used to search for possible rules underlying the dendritic architecture of these cells. Branching and terminating probabilities per unit length of dendrite were computed from all available measurement points. In the three neuronal groups, terminating probabilities were found to be exponentially increasing functions of the path distance to soma. MSNs and type II branching probabilities could be accurately modelled with decreasing functions of both the metrical (exponential functions) and topological (power functions of the centrifugal branch order) distances to soma. Additionally, type II branching also slightly depended on the distance to the proximal tip of the supporting branches. Type I branching probabilities did not follow these rules accurately. Embedding the modelled probability functions in a stochastic algorithm allowed generation of dendrograms close to those of the real MSNs and pallidal type II neurons, while the algorithm failed to simulate type I dendrites. MSN and pallidal type II neuron branching and terminating probabilities are thus highly dependent on the position in the dendritic arbor. This relationship can be modelled with simple functions and has a strong incidence on the dendrogram structure of the cells concerned. The additional dependence of the branching probability on the within-branch position led us to propose an extension of a previous modelling study by Nowakowski and co-workers which could account for a large range of topological and metrical (length) dendritic tree structures.
机译:来自灵长类动物纹状体中棘神经元(MSN)和苍白I型和II型神经元的定量三维分析数据被用于寻找这些细胞树突结构基础的可能规则。从所有可用的测量点计算出枝晶每单位长度的分支和终止概率。在这三个神经元组中,发现终止概率与到达体细胞的距离呈指数函数增长。 MSN和II型分支概率可以通过与躯体距离的度量(指数函数)和拓扑(离心分支阶幂函数)的递减函数来精确建模。另外,II型分支也略微取决于到支撑分支近端尖端的距离。 I类分支概率未正确遵循这些规则。将模型化的概率函数嵌入到随机算法中,可以生成与真实MSN和II型苍白神经元神经元相近的树状图,而该算法无法模拟I型树突。因此,MSN和II型苍白神经元的分支和终止概率高度依赖于树突状乔木中的位置。这种关系可以用简单的函数建模,并且在有关细胞的树状图结构上具有很高的关联性。分支概率对分支内位置的额外依赖性使我们提出了对Nowakowski及其同事的先前建模研究的扩展,该模型研究可以解释大范围的拓扑和度量(长度)树状树结构。

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