Generation and Description of Neuronal Morphology and Connectivity

树突和轴突形态在生理性脑功能和病理性脑损伤中起着重要作用。 通过影响突触整合、锋电位序列传输和电路连接性来治疗功能障碍。结合 将已发表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， 这对于定量地桥接亚细胞和系统水平是重要的。我们成功地设计了， 实施，并免费分发给社区的计算机软件和数据库，以重建， 分析，可视化，模拟和共享从任何标签收集的神经元的3D树状形状， 可视化技术，动物种类，大脑区域，发育阶段和实验条件。 我们通过光学显微镜成像，数字化追踪，并分享新的数据，我们为同行提供了 电子方式自由地做同样的事情。此外，我们将这些数据与计算模型相结合， 膜生物物理学研究神经元的结构与活动的关系，特别侧重于 海马和内嗅皮层，因为它们在空间表征和情景记忆中起着中心作用。我们 在一个开源的基于网络的神经元门户网站上额外注释了大量的细胞特性， 啮齿类动物海马结构的类型。我们现在建议用三种方法来扩展这种研究方法 具体目标。第一个是增强NeuroMorpho.xml存储库的功能、范围和可用性， 数字追踪我们计划将共享重建的数量增加一倍以上，同时加强 通过添加“搜索相似项”和摘要报告功能，最 对于长期可持续性而言，重要的是，我们将大幅实现信息技术基础设施的现代化 此资源的使用，以支持作者直接主动提交，持续的敏捷发布，以及 社区众包。第二个目标是通过添加以下内容来完善Hippocampome.org知识库： 突触信息，包括连接概率，生理学和可塑性，并将它们与 突触前和突触后神经元的现有形态学、生理学和分子特性。这将 实现真实规模的尖峰神经网络模型，以运行活动的预测模拟 动力学和计算功能。第三个目标是开发一种创新的方法来分类神经元 直接从网络连接，验证它与海马电路和部署它在开放访问 来自流行模式生物的高通量数据。总之，这三个目标将使我们（和其他人） 一方面，测试将神经元形态与分子和发育决定因素相关的假设， 另一方面连接到功能电路。这一应用的重点是结构塑性是特别相关的 主要涉及海马结构的致残性神经系统疾病，包括癫痫， 老年痴呆我们提出的数据驱动的，生物现实的网络模拟可能揭示的作用， 特定的神经元类型及其在记忆形成和提取障碍中的相互作用。

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 published 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 collected from any labeling and visualization techniques, animal species, brain regions, 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 with a special focus on the hippocampus and entorhinal cortex due to their central role in spatial representation and episodic memory. We additionally annotated a massive amount of cellular properties in an open-source web-based portal of neuron types in the rodent hippocampal formation. We now propose to expand this research approach with three specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to more than double the number of shared reconstructions while enhancing the human- and machine-accessible utility by adding ‘search similar’ and summary reporting functionalities. Most importantly for long-term sustainability, we will dramatically modernize the information technology infrastructure of this resource to enable unsolicited submissions directly from authors, continuous agile releases, and community crowdsourcing. The second aim is to complete the Hippocampome.org knowledge base by adding synaptic information, including connection probabilities, physiology, and plasticity, and linking them to the existing morphological, physiological, and molecular properties of pre- and post-synaptic neurons. This will enable the implementation of a real-scale spiking neural network model to run predictive simulations of activity dynamics and computational functions. The third aim is to develop an innovative approach to classify neurons directly from network connectivity, validating it with the hippocampal circuit and deploying it on open-access high-throughput data from a popular model organism. Together, these three aims will allow us (and others) to test hypotheses relating neuronal morphology to molecular and developmental determinants on the one hand, and to functional circuits on the other. The focus of this application on structural plasticity is especially relevant to disabling neurological diseases prominently involving the hippocampal formation, including epilepsy and Alzheimer’s. Our proposed data-driven, biologically realistic network simulations may shed light on the role of specific neuron types and of their interactions in impairments of memory formation and retrieval.