GENERATION AND DESCRIPTION OF DENDRITIC MORPHOLOGY

描述：(申请人摘要) 尽管神经学家普遍认为树突形态 在形成细胞生理和网络方面起着重要作用 连通性、用于详细神经形态建模的计算工具是 到目前为止还不够。这样的差距尤其令人惊讶，因为 多个神经元三维形态的大量实验数据 可在文学中使用的课程，以及日益强大的 计算机图形学和虚拟现实的复杂性。这项研究项目 旨在填补这一空白。卡哈尔设想神经元的形状由一个 有限数量的内在现象，受外部效应的调节 环境。基于这一概念，几条与之相关的地方规则 形态参数(例如树枝直径和长度)已被证明是 对树枝状结构特定方面的有力而简明的描述 拓扑学。我们正在利用这些成功的相关性，以及全球 几何约束，实现树枝的描述性算法 形态学。这些算法将被组装成一个软件包，名为 L-神经元，用于解剖学上可能的神经元的产生和研究 类比。我们的实现是基于著名的数学专家L系统 形式主义特别适合描述分支和递归结构， 并在计算机图形学中得到广泛发展。L-神经元将使用实验 来自真实细胞解剖数据的参数分布以生成虚拟 各种形态类型的神经元。在每个班级内，统计上的 该算法的约束随机实现将产生多个， 不同的神经元。虚拟神经元集合的生成是 生物学上相关，因为它区分了重要的形态 参数和紧急副产品，它们代表冗余。如果 算法实际上产生了准确和逼真的结构，它必须包含 所有必需的信息，从而完整地描述了原件 形态家族。如果在虚拟和虚拟之间存在残余差异 真正的神经元，他们的分析可能会导致新的几何发现 枝晶参数之间的约束和定量关联。 在虚拟现实中生成完整的枝晶几何模型 刺激分析策略的发展，以测试虚拟 神经元在形态上与真实的神经元是等同的。L-神经元将输出 将神经元结构转换为各种格式，包括虚拟现实、标准 图形和解剖文件，也由分区建模程序使用 比如《创世纪》。这种多种多样的选项将允许显示、动态 数据的呈现和量化分析及其高效交换 在研究小组中。L神经元的局限性在于定向性 转向单细胞分析，从而使其不太适合于研究 神经元形态对网络连通性的影响。然而，简单性 也代表了一个重要的优点，因为它允许 分析特定的内在和外在决定因素对 神经元的形状，因此对神经元电生理学的影响。我们相信 此包可移植到所有主要平台并免费分发，将 进一步的神经解剖学、计算建模和科学教育。

DESCRIPTION: (Applicant's Abstract) Despite a general agreement among neuroscientists that dendritic morphology plays an important role in shaping cellular physiology and network connectivity, computational tools for detailed neuromorphological modeling are so far lacking. Such a gap is particularly surprising considering the vast amount of experimental data on the three-dimensional shape of many neuronal classes available in the literature, and the increasingly powerful sophistication of computer graphics and virtual reality. This research project aims at filling this gap. Cajal envisioned neuronal shape as determined by a finite number of intrinsic phenomena, modulated by the extrinsic effect of the environment. Based on this notion, several local rules correlating morphological parameters (e.g. branch diameter and length) have proved to be powerful and parsimonious descriptors of specific aspects of dendritic topology. We are using these successful correlations, together with global geometrical constraints, to implement descriptive algorithms for dendritic morphology. These algorithms will be assembled into a software package, named L-Neuron, for the generation and study of anatomically plausible neuronal analogs. Our implementation is based on L-system, a well-known mathematical formalism particularly suitable to describe branching and recursive structures, and extensively developed in computer graphics. L-Neuron will use experimental distributions of parameters from real-cell anatomical data to generate virtual neurons of various morphological classes. Within each class, the statistically constrained stochastic implementation of the algorithm will produce multiple, non-identical neurons. The generation of sets of virtual neurons is biologically relevant because it discriminates between important morphological parameters and emergent byproducts, which represent redundancies. If the algorithm actually produces accurate and realistic structures, it must contain all the required information and thus completely describes the original morphological family. If there are residual discrepancies between virtual and real neurons, their analysis may lead to the discovery of new geometric constraints and quantitative correlations between dendritic parameters. Generating complete models of dendritic geometry in virtual reality thus stimulates the development of analytical strategies to test whether the virtual neurons are morphologically equivalent to the real ones. L-Neuron will output neuronal structures into various formats, including virtual reality, standard graphic, and anatomical files, also used by compartmental modeling programs such as GENESIS. This variety of options will allow the display, dynamical rendering and quantitative analysis of data as well as their efficient exchange among research groups. The limitation of L-Neuron consists in being oriented toward single-cell analysis, thus making it less suitable for studying the effect of neuronal morphology on network connectivity. However, the simplicity of this system also represents an important advantage because it allows the analysis of the influence of specific intrinsic and extrinsic determinants on neuronal shape, and consequently on neuronal electrophysiology. We believe that this package, portable to all major platforms and freely distributed, will further neuroanatomy, computational modeling, and scientific education.