Generation and description of neuronal morphology and connectivity

树突和轴突形态在生理脑功能中起着重要作用， 通过影响突触整合、锋电位串传递和回路而导致病理性功能障碍 连通性。将现有的和即将到来的实验数据转化为准确的，全面的， 生物学上合理的神经网络模拟对于定量桥接 亚细胞和系统水平。我们成功地设计、实现并免费分发了 社区计算机软件和数据库来重建，分析，可视化，模拟， 并从许多标记和可视化技术中共享神经元的3D树状形状， 发展阶段和实验条件。我们用光学显微镜进行数字成像 追踪和分享新的数据，我们为同行提供免费的电子手段， 做同样的事情。此外，我们将这些数据与膜的计算模型相结合， 生物物理学来研究神经元结构-活性关系。我们建议扩大这一点， 研究方法有两个具体目标。第一是扩大权力，范围， 数字描记的NeuroMorpho. xml 3存储库的可用性。我们计划将 共享重建，添加新的物种，大脑区域和神经元类型。而且还要 使用最先进的本体，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。第二个目标是建立一个新的知识库， 海马和内嗅皮层中的神经元类型通过量化它们的形态， 生理学和分子特性。海马结构是 这是研究最多的大脑区域之一，是自传体记忆存储和空间记忆的基础。 代表性，并显着参与毁灭性的神经系统疾病，包括 癫痫和老年痴呆症然而，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，它是有限的，因为它很难 找到并整合分散在数千篇论文中的所有相关数据。我们将确认所有 发布的信息，并用指向对等体中的源文档的特定指针对其进行注释- 审查文献。由此产生的开源门户网站（Hippocampome.org）将使 潜在电路连通性的推导和全网络尖峰的预测模拟 活动我们将使这种应用特别与神经病理学相关， 神经元类型的疾病涉及海马，并证明其潜力与一个新的 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.