Computational Infrastructure for Brain Research: Workshops on Methods and Infrastructure for Scalable Computing in Neuroscience

脑研究的计算基础设施：神经科学可扩展计算方法和基础设施研讨会

• 批准号: 1649759
• 负责人: Lauren Michael
• 金额: $ 5万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649759&HistoricalAwards=false
• 关键词: Computational; Infrastructure; Brain; Research; Workshops

Computational and data-intensive neuroscience has been growing rapidly. However, many neuroscientists are not familiar with scalable computational approaches and the diverse, widely available shared computational resources that can be used to accelerate neuroscientific data analysis and modeling, such as the advanced computing resources of the Open Science Grid and the XSEDE network, among others. Many computing infrastructure experts also know little about the computational and data challenges in contemporary neuroscience. This project will address these issues through a pair of multidisciplinary workshops and development of training materials designed to bring together and educate both the neuroscientists and computational infrastructure communities. In the first workshop, leading neuroscientists and computing infrastructure experts will share computational practices to identify critical theory and best practices for matching neuroscience computational approaches with appropriate infrastructure configurations. The second workshop will be timed to coincide with the 2018 annual meeting of the Cognitive Science Society, to be held in Madison, Wisconsin. The overall goal is dissemination of these best practices and accompanying practical training on advanced computing resources to accelerate neuroscience discovery. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.At the Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison, breakthrough progress in neuroscience has been enabled by integrating computational expertise and domain expertise via Research Computing Facilitators. This model has demonstrated that scientific paradigm shifts can be achieved by human-driven matchmaking to pair research programs, computational methods experts, and appropriate computational infrastructure configurations. The workshops will foster replication of this matchmaking approach to impact the broader neuroscience community. The overall goal is to empower brain researchers to select the scalable computational resources that are most appropriate for their specific research programs, to achieve dramatic rapid progress in the field.This award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the CISE Division of Information and Intelligent Systems, with funds associated with the NSF Understanding the Brain and BRAIN Initiative activities, and for developing national research infrastructure for neuroscience. This project also aligns with NSF objectives under the National Strategic Computing Initiative.

计算和数据密集型神经科学正在迅速发展。然而，许多神经科学家并不熟悉可扩展的计算方法和可用于加速神经科学数据分析和建模的各种广泛可用的共享计算资源，例如开放科学网格和XSEDE网络的高级计算资源等。许多计算基础设施专家也对当代神经科学中的计算和数据挑战知之甚少。该项目将通过一对多学科研讨会和旨在汇集和教育神经科学家和计算基础设施社区的培训材料的开发来解决这些问题。在第一个研讨会上，领先的神经科学家和计算基础设施专家将分享计算实践，以确定将神经科学计算方法与适当的基础设施配置相匹配的关键理论和最佳实践。第二次研讨会将与认知科学学会2018年年会在威斯康星州的麦迪逊举行。总体目标是传播这些最佳实践，并伴随先进计算资源的实践培训，以加速神经科学的发现。因此，该项目符合NSF促进科学进步和促进国家健康、繁荣和福利的使命。在威斯康星大学麦迪逊分校的高吞吐量计算中心（CHTC），通过研究计算促进者整合计算专业知识和领域专业知识，实现了神经科学的突破性进展。该模型表明，科学范式的转变可以通过人为匹配来实现，以配对研究计划，计算方法专家和适当的计算基础设施配置。研讨会将促进这种配对方法的复制，以影响更广泛的神经科学界。总体目标是使大脑研究人员能够选择最适合其特定研究项目的可扩展计算资源，以实现该领域的快速发展。该奖项由CISE高级网络基础设施部门颁发，由CISE信息和智能系统部门共同支持，与NSF理解大脑和大脑倡议活动相关的资金，以及发展神经科学的国家研究基础设施。该项目也符合国家战略计算计划下的NSF目标。