Retinal Circuitry

视网膜电路

• 批准号: 10593874
• 负责人: Bryan William Jones
• 金额: $ 29.88万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593874
• 关键词: Address; Age related macular degeneration; Client; Collaborations; Communities; Consumption; Data; Data Set; Databases; Dependence; Development; Discrimination; Disease; Electronic Medical Records and Genomics Network; Environment; Funding; Gap Junctions; Generations; Hand; Human; Image; Infrastructure; Internet; Mining; Modeling; Modernization; Mosaicism; Neural Retina; Neurons; Organelles; Pathologic; Prize; Research; Research Personnel; Resolution; Resources; Retina; Retinitis Pigmentosa; Services; Ships; Side; Structure; Synapses; System; Testing; Training; Update; Work; annotation system; application programming interface; connectome; feature detection; migration; neural network; nonhuman primate; open source; retinal prosthesis; tool; tool development; web app

EY028927 Project/Supplement Summary This supplement for EY028927 responds to NOT-OD-22-067 and is crafted around assembly, annotation and analysis of ultrastructural data from non-human primate, and human retina, providing a normal circuit topology framework as well as disease frameworks allowing comparison of normal to pathological retinal networks that emerge in disease. Connectomes are Rosetta Stones for discovering how retinas are wired and reveal how retinal structure and function are altered by remodeling in retinitis pigmentosa (RP) and age-related macular degeneration (AMD). Prior research efforts have unmasked unexpected, pervasive complexities in mammalian retinal networks, informing neuronal modeling and modeling of retinal prosthetics. This supplement will fund tool development to enable sharing of specific aspects of our datasets in great demand from the AI/ML community. Our work has built not only connectomics infrastructure for datasets, but the annotation work within the datasets themselves provides a valuable ground truth to feed AI/ML approaches as training data. While our annotated databases have been the highest resolution connectomics databases yet available, allowing discrimination of synapses and gap junctions as well as organelle data, we do not have good tools to subset these connectomes to feed AI/ML approaches allowing feature detection of synaptic features or sub-cellular features desired by the AI/ML community. The tools deriving from this supplement will interface with our open-source datasets, providing the entire connectomics community access to rich, validated, ground-truth data for AI/ML training and mining.

EY028927项目/补充摘要 EY028927的这种补充剂对NOT-OD-22-067响应，并围绕组装，注释和 分析非人类灵长类动物和人类视网膜的超微结构数据，提供正常电路 拓扑框架以及疾病框架，允许比较正常视网膜 疾病中出现的网络。连接组是Rosetta石头，用于发现视网膜的连线方式和 揭示视网膜结构和功能如何通过色素性视网膜炎（RP）中的重塑和年龄相关的方式改变 黄斑变性（AMD）。先前的研究效果具有未掩盖的意外，普遍的复杂性 哺乳动物的视网膜网络，告知视网膜假体的神经元建模和建模。 该补充剂将资助工具开发，以使我们的数据集的特定方面在很大程度上共享 AI/ML社区的需求。我们的工作不仅为数据集建立了连接基础架构，而且还建立了 数据集本身内的注释工作为喂养AI/ML提供了宝贵的基础真理 作为培训数据的方法。虽然我们的注释数据库是最高的分辨率连接组学 数据库尚未可用，可以歧视突触和间隙连接以及细胞器数据，我们 没有良好的工具来将这些连接组纳入以供应AI/ML方法以允许特征检测 AI/ML社区期望的突触特征或亚细胞特征。从中得出的工具 补充将与我们的开源数据集接口，提供整个连接社区 访问AI/ML培训和采矿的丰富，经过验证，基础真相数据。