Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
合作研究:ABI 创新:大规模神经形态数据集的计算探索
基本信息
- 批准号:2028361
- 负责人:
- 金额:$ 14.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-02-27 至 2021-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Analyzing single neuron's property is a fundamental task to understand the nervous system and brain working mechanism. Investigating neuron morphology is an effective way to analyze neurons, since it plays a major role in determining neurons' properties. Recently, the ever-increasing neuron databases have greatly facilitated the research of neuron morphology. However, the sheer volume and complexity of these data pose significant challenges for computational analysis, preventing the realization of the full potential of such data. This interdisciplinary project will seek for new avenue to assemble the massive neuron morphologies and provide a unified framework for neuroscientists to explore and analyze different types of neurons. The research is able to tackle many challenges in neuroscience which are hard to solve with previous methods, including fine-grained neuron identification, latent pattern discovery and exploration, etc. The large-scale methods being developed will be particularly beneficial in the future of neuroscience, since more and more neurons are reconstructed and added to the databases. The computational methods and tools developed are very likely to be applicable for solving other bioinformatics problems, especially those dealing with large-scale datasets. The broader impact of this project not only includes educational support for undergraduate researchers and high school students, particularly women and those underrepresented groups, but also contributes to the research of neuroscience and other STEM fields.The long-term goal of this project is to develop effective computational methods and tools for neuroscientists to interactively explore large-scale neuron databases with ultra-fine-grained accuracy, in real-time. This research has a strong multidisciplinary component that involves a nexus ideas from machine learning, information retrieval, and neuroinformatics. Particularly, novel ideas will be implemented in three inter-related components through the whole framework. The first one addresses the accurate and efficient neuron reconstruction and tracing based on deep learning models. The second addresses the efficient discovery of relevant instances among large-size neuron databases via multi-modal and online binary coding methods. The third part addresses intelligent visualization and interaction schemes for knowledge discovery and mining, equipped with interactive coding that can incorporate domain experts' feedback to enhance the query algorithms for fine-tuned results. Compared with previous methods and systems, this project will open a new avenue to assist neuroscientists analyzing and exploring large-scale neuron databases with high efficiency, accuracy, and robustness. The performance of proposed methods will be validated using public neuro-morphological databases (e.g., NeuroMorpho, BigNeuron) and compared with several benchmarks. The effectiveness of the tools to be developed will be evaluated by neuroscientists on domain-specific hypothesis-driven applications. The outcome of the project will be made available at the following websites: http://webpages.uncc.edu/~szhang16/ and https://github.com/divelab/.
分析单个神经元的特性是理解神经系统和大脑工作机制的基础性工作。研究神经元形态是分析神经元的一种有效方法,因为它在确定神经元的性质方面起着重要作用。近年来,不断增加的神经元数据库极大地促进了神经元形态学的研究。然而,这些数据的庞大数量和复杂性对计算分析构成了重大挑战,阻碍了这些数据的全部潜力的实现。这个跨学科的项目将寻求新的途径来组装大量的神经元形态,并为神经科学家探索和分析不同类型的神经元提供一个统一的框架。该研究能够解决神经科学中的许多挑战,这些挑战是以前的方法难以解决的,包括细粒度神经元识别,潜在模式发现和探索等,正在开发的大规模方法将在神经科学的未来特别有益,因为越来越多的神经元被重建并添加到数据库中。所开发的计算方法和工具很可能适用于解决其他生物信息学问题,特别是那些处理大规模数据集的问题。该项目的更广泛影响不仅包括对本科研究人员和高中生,特别是女性和那些代表性不足的群体的教育支持,还有助于神经科学和其他STEM领域的研究。该项目的长期目标是开发有效的计算方法和工具,供神经科学家实时交互式地探索大规模神经元数据库,具有超细粒度的准确性。这项研究具有强大的多学科组成部分,涉及机器学习,信息检索和神经信息学的联系思想。特别是,新的想法将在整个框架的三个相互关联的组成部分实施。第一个解决了基于深度学习模型的准确有效的神经元重建和跟踪。第二个解决了通过多模态和在线二进制编码方法在大规模神经元数据库中有效地发现相关实例。第三部分讨论了知识发现和挖掘的智能可视化和交互方案,配备了交互式编码,可以结合领域专家的反馈,以增强查询算法,以获得微调的结果。与以往的方法和系统相比,该项目将开辟一条新的途径,以帮助神经科学家分析和探索大规模神经元数据库,具有高效率,准确性和鲁棒性。所提出的方法的性能将使用公共神经形态学数据库(例如,NeuroMorpho、BigNeuron),并与几个基准进行比较。待开发的工具的有效性将由神经科学家在特定领域的假设驱动的应用程序进行评估。该项目的成果将在以下网站上公布:http://webpages.uncc.edu/~szhang16/和https://github.com/divelab/。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An Efficient Policy Gradient Method for Conditional Dialogue Generation
- DOI:10.1109/icdm.2019.00013
- 发表时间:2019-11
- 期刊:
- 影响因子:0
- 作者:Lei Cai;Shuiwang Ji
- 通讯作者:Lei Cai;Shuiwang Ji
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
- DOI:10.24963/ijcai.2019/401
- 发表时间:2019-08
- 期刊:
- 影响因子:0
- 作者:Jun Li;Yongjun Chen;Lei Cai;I. Davidson;Shuiwang Ji
- 通讯作者:Jun Li;Yongjun Chen;Lei Cai;I. Davidson;Shuiwang Ji
Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction
- DOI:10.1109/icdm.2019.00091
- 发表时间:2019-11
- 期刊:
- 影响因子:0
- 作者:Hao Yuan;Na Zou;Shaoting Zhang;Hanchuan Peng;Shuiwang Ji
- 通讯作者:Hao Yuan;Na Zou;Shaoting Zhang;Hanchuan Peng;Shuiwang Ji
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
- DOI:
- 发表时间:2020-10
- 期刊:
- 影响因子:0
- 作者:Yaochen Xie;Zhengyang Wang;Shuiwang Ji
- 通讯作者:Yaochen Xie;Zhengyang Wang;Shuiwang Ji
Line Graph Neural Networks for Link Prediction
- DOI:10.1109/tpami.2021.3080635
- 发表时间:2022-09-01
- 期刊:
- 影响因子:23.6
- 作者:Cai, Lei;Li, Jundong;Ji, Shuiwang
- 通讯作者:Ji, Shuiwang
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Shuiwang Ji其他文献
A Mathematical View of Attention Models in Deep Learning
深度学习中注意力模型的数学观点
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Shuiwang Ji;Yaochen Xie - 通讯作者:
Yaochen Xie
Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments
降维判别分析:近期发展概述
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Jieping Ye;Shuiwang Ji - 通讯作者:
Shuiwang Ji
An Interpretable Neural Model with Interactive Stepwise Influence
具有交互式逐步影响的可解释神经模型
- DOI:
10.1007/978-3-030-16142-2_41 - 发表时间:
2019 - 期刊:
- 影响因子:2.3
- 作者:
Yin Zhang;Ninghao Liu;Shuiwang Ji;James Caverlee;Xia Hu - 通讯作者:
Xia Hu
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
高保真流体流动重建的半监督学习
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Cong Fu;Jacob Helwig;Shuiwang Ji - 通讯作者:
Shuiwang Ji
Eliminating Position Bias of Language Models: A Mechanistic Approach
消除语言模型的位置偏差:一种机械方法
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Ziqi Wang;Hanlin Zhang;Xiner Li;Kuan;Chi Han;Shuiwang Ji;S. Kakade;Hao Peng;Heng Ji - 通讯作者:
Heng Ji
Shuiwang Ji的其他文献
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{{ truncateString('Shuiwang Ji', 18)}}的其他基金
III: Small: 3D Graph Neural Networks: Completeness, Efficiency, and Applications
III:小:3D 图神经网络:完整性、效率和应用
- 批准号:
2243850 - 财政年份:2023
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications
III:小:协作研究:揭秘图深度学习:从基本操作到应用
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2006861 - 财政年份:2020
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks
III:媒介:协作研究:迈向可扩展和可解释的图神经网络
- 批准号:
1955189 - 财政年份:2020
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
III: Small: Collaborative Research: Structured Methods for Multi-Task Learning
III:小:协作研究:多任务学习的结构化方法
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1908166 - 财政年份:2018
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
III: Small: Deep Learning for Gene Expression Pattern Image Analysis
III:小:深度学习用于基因表达模式图像分析
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1908220 - 财政年份:2018
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
CAREER: Towards the Next Generation of Data-Driven
职业:迈向下一代数据驱动
- 批准号:
1922969 - 财政年份:2018
- 资助金额:
$ 14.65万 - 项目类别:
Continuing Grant
BIGDATA: Collaborative Research: F: Efficient and Exact Methods for Big Data Reduction
BIGDATA:协作研究:F:大数据缩减的高效且精确的方法
- 批准号:
1908198 - 财政年份:2018
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
III: Small: Deep Learning for Gene Expression Pattern Image Analysis
III:小:深度学习用于基因表达模式图像分析
- 批准号:
1811675 - 财政年份:2018
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
合作研究:ABI 创新:大规模神经形态数据集的计算探索
- 批准号:
1661289 - 财政年份:2017
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
BIGDATA: Collaborative Research: F: Efficient and Exact Methods for Big Data Reduction
BIGDATA:协作研究:F:大数据缩减的高效且精确的方法
- 批准号:
1633359 - 财政年份:2016
- 资助金额:
$ 14.65万 - 项目类别:
Standard Grant
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