Torben-Nielsen Benjamin, Vanderlooy Stijn, Postma Eric O
TENU, Okinawa Institute of Science and Technology, Okinawa, Japan.
Neuroinformatics. 2008 Winter;6(4):257-77. doi: 10.1007/s12021-008-9026-x. Epub 2008 Sep 17.
Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-NEURON: and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.
生成算法允许从一小组形态学属性生成虚拟神经元（VN）。该集合根据统计描述符（如分支数量和节段长度等）描述真实神经元的形态学属性。大多数重建算法使用观察到的属性来估计先验固定概率分布的参数，以便构建与观察到的数据拟合良好的统计描述符。在本文中，我们提出了一种基于核密度估计器（KDE）的非参数生成算法。新算法称为KDE-NEURON，与参数重建算法相比有三个优点：（1）无需对真实数据背后的分布进行先验规定，（2）生物数据中的特性将反映在虚拟神经元中，（3）能够重建不同的细胞类型。我们通过实验生成了运动神经元和颗粒细胞，并对所得结果进行了统计验证。此外，我们评估了原型数据集的质量，观察到就所使用的统计描述符而言，我们生成的神经元与原型数据一样好。还讨论了数据驱动的神经元算法重建的机遇和局限性。
