Generation and description of neuronal morphology and connectivity

树突和轴突的形态在生理脑功能和 影响突触整合、棘波传递和回路的病理性功能障碍 连通性。将现有和即将到来的实验数据整合到准确的、全面的、 生物学上看似合理的神经网络模拟对于定量地连接 亚细胞级和系统级。我们成功地设计、实现并免费分发 社区计算机软件和数据库，以重建、分析、可视化、模拟、 并共享来自许多标记和可视化技术的神经元的3D树状形状， 发育阶段和实验条件。我们用光学显微镜进行了数字化成像 跟踪和共享新数据，我们为同行提供了免费的电子手段 做着同样的事。此外，我们还将这些数据与膜的计算模型相结合 生物物理学，研究神经元的结构-活性关系。我们建议扩大这一范围 研究方法有两个具体目的。一是加大力度，扩大范围，扩大 神经形态组织数字痕迹资料库的可用性。我们计划将数量增加两倍 共享重建，添加新物种、大脑区域和神经元类型。此外，我们还将 使用最先进的本体论，通过“语义”引擎增强搜索功能。我们 还将扩展分布式数据的域和格式，以包括电路、多通道 信息和时间序列。第二个目标是开发一个新的知识库 通过量化海马区和内嗅区皮质的形态， 来自已发表报告的生理和分子特性。海马区结构是 研究最多的大脑区域之一，是自传记忆存储和空间记忆的基础 代表，并显著参与毁灭性的神经疾病，包括 癫痫和阿尔茨海默氏症。然而，我们对海马体如何工作的概念性理解是 与关于其神经元的丰富可用知识相比，这是有限的，因为很难 找到并整合分散在数千篇论文中的所有相关数据。我们将确定所有 发布信息，并使用指向同级中的源文档的特定指针对其进行注释- 回顾了相关文献。由此产生的开源门户(Hippocampome.Org)将支持 潜在电路连通性的推导和全网尖峰的预测模拟 活动。我们将通过链接特定的特定应用程序，使此应用程序与神经病理学特别相关 神经元类型对涉及海马体的疾病，并展示其潜力与一种新的 基于结构可塑性受损的学习障碍模型。

Dendritic and axonal morphologies play fundamental roles in physiological brain function and pathological dysfunction by affecting synaptic integration, spike train transmission, and circuit connectivity. Incorporating existing and forthcoming experimental data into accurate, full-scale, and biologically plausible neural network simulations is important for quantitatively bridging the sub-cellular and systems-levels. We successfully designed, implemented, and freely distributed to the community computer software and databases to reconstruct, analyze, visualize, simulate, and share the 3D tree-like shape of neurons from many labeling and visualization techniques, developmental stages, and experimental conditions. We imaged by light microscopy, digitally traced, and shared new data, and we provided our peers with the electronic means of freely doing the same. Moreover, we combined those data with computational models of membrane biophysics to investigate the neuronal structure-activity relationship. We propose to expand this research approach with two specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to triple the number of shared reconstructions, adding new species, brain regions, and neuron types. Moreover, we will enhance the search functionality with a "semantic" engine using state-of-the-art ontologies. We will also extend the domain and format of distributed data to include circuitry, multi-channel information, and temporal sequences. The second aim is to develop a new knowledge base of neuron types in the hippocampus and entorhinal cortex by quantifying their morphological, physiological, and molecular properties from published reports. The hippocampal formation is one of the most studied brain regions, underlies autobiographic memory storage and spatial representation, and is prominently involved in devastating neurological disease, including epilepsy and Alzheimer's. Yet, our conceptual understanding of how the hippocampus works is limited compared to the wealth of available knowledge about its neurons, because it is difficult to find and integrate all relevant data scattered in thousands of papers. We will identify all published information and annotate it with specific pointers to the source documents in the peer- reviewed literature. The resulting open-source portal (Hippocampome.Org) will enable the derivation of potential circuit connectivity and the predictive simulation of network-wide spiking activity. We will make this application especially relevant to neuropathology by linking specific neuron types to diseases involving the hippocampus, and demonstrate its potential with a new model of learning disabilities based on impaired structural plasticity.