GENERATION AND DESCRIPTION OF DENDRITIC MORPHOLOGY

树枝状形态的产生和描述

• 批准号: 6529430
• 负责人: GIORGIO A ASCOLI
• 金额: $ 10.93万
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6529430
• 关键词: computational neuroscience; computer simulation; dendrites; electrophysiology; informatics; motor neurons; single cell analysis

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.

描述：（申请人摘要） 尽管神经科学家普遍认为树突形态 在塑造细胞生理和网络中起着重要作用 连接，用于详细神经形态学建模的计算工具是 到目前为止，缺乏。这样的差距是特别令人惊讶的， 大量关于许多神经元的三维形状的实验数据 在文学中可用的类，和越来越强大的 计算机图形和虚拟现实的复杂性。本研究项目 旨在填补这一空白。卡哈尔设想神经元的形状是由一个 有限数量的内在现象，调制的外在效应的 环境基于这一概念，一些地方规则 形态参数（例如分支直径和长度）已被证明是 对树突状细胞的特定方面的强大和简约的描述 topology.我们正在使用这些成功的相关性，与全球 几何约束，实现枝晶的描述算法 形态学这些算法将被组装成一个软件包，名为 L-神经元，用于产生和研究解剖学上合理的神经元 类似物我们的实现是基于L系统，一个著名的数学 形式主义特别适合于描述分支和递归结构， 并在计算机图形学中得到了广泛的发展。L-Neuron将使用实验性的 来自真实细胞解剖数据的参数分布， 各种形态的神经元。在每一类中， 算法的受约束的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 生物学相关，因为它区分了重要的形态学 参数和新出现的副产品，它们代表冗余。如果 算法实际上产生准确和现实的结构，它必须包含 所有必要的信息，从而完整地描述了原始 形态学家族如果虚拟和 真实的神经元，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相关性。 在虚拟现实中生成树枝状几何形状的完整模型， 刺激分析策略的发展，以测试虚拟 神经元在形态上等同于真实的神经元。L-Neuron将输出 神经元结构转换成各种格式，包括虚拟现实，标准 图形和解剖文件，也用于房室建模程序 如GENESIS。这种多样的选项将允许显示，动态 数据的绘制和定量分析以及数据的有效交换 在研究小组中。L-神经元的局限性在于 单细胞分析，从而使其不太适合研究 神经元形态对网络连通性的影响。然而，简单性 该系统还具有一个重要的优点，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，并因此对神经元电生理学。我们认为 该软件包可移植到所有主要平台并免费分发， 进一步的神经解剖学、计算建模和科学教育。