Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation
Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation
批准号:
0321388
负责人:
Allen Malony
金额:
$103.75万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-09-01 至 2007-08-31
中文摘要
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英文摘要
Future progress in cognitive neuroscience research will rely increasingly on the application of systems for high-performance computation and high-volume data management to address the challenges of integrated neuroimaging, multi-modality sensor fusion, and cognitive modeling. With a Major Research Instrumentation award from the National Science Foundation, the University of Oregon will establish the Integrated COgnitive Neuroscience, Informatics, and Computation (ICONIC) Grid, composed of parallel computing clusters, large-scale data servers, workstations, and interactive visualization devices. Connected by a high-bandwidth campus network linking the Department of Psychology, the Center for Neuroimaging , the Neuroinformatics Center, the Department of Computer and Information Science, and the Computational Science Institute, the ICONIC Grid will enhance Oregon's excellence in cognitive neuroscience with needed computing power to solve neuroimaging problems of tissue/feature segmentation, dense-array EEG source localization, multi-modal MRI integration, and functional components analysis. The ICONIC Grid will be organized as a distributed computing environment to promote grid-style collaboration among cognitive neuroscience research groups. Computer science research in high-performance parallel and distributed computing, scientific databases, informatics, and interactive visualization will enhance the ICONIC Grid for highly productive use as a computational science tool.The interchange between cognitive neuroscience and computational science is now important at both theoretical and empirical levels. For several decades, cognitive psychology has drawn from concepts of cybernetics and information processing in the development of models of human mental function. However, it is in the integration of psychological with neural evidence that the methodological demands for computational advances have become particularly intense. Many investigators in cognitive neuroscience now recognize the limitations of individual brain imaging methods, such as in the temporal or spatial resolution, or practical implementation of the technology. The result is an increasing demand for integrated imaging and analysis, in which convergent methods are brought to bear on a particular issue of brain mechanisms. The University of Oregon began the decade with a bold Brain, Biology, and Machine Initiative (BBMI) to promote interdisciplinary research between neuroscience, cognitive science, molecular biology, genomics, and computational science. The establishment of the Center for Neuroimaging , which houses a new Siemans Allegra 3-Tesla fMRI machine, and the Neuroinformatics Center, were Oregon's first steps towards integrative cognitive neuroscience. The ICONIC Grid is the next critical piece of the puzzle providing an essential resource to further advancements in cognitive neuroscience research, collaboration, education, and outreach.The broader impact of the ICONIC Grid will be important for the University's educational goals, for minority recruitment and retention, and for extending advances in computation to medical advances in society. With on-campus access to both advanced imaging facilities and the computational and visualization infrastructure that processes and presents the experimental data, students in Psychology will be exposed to a state-of-the-art problem-solving environment for cognitive neursocience education. New Psychology curricula are planned for providing students training in the use of such tools. Similarly, the CIS department's academic objectives in parallel and distributed computing, computational science, networking, human-computer interaction, and visualization will benefit greatly from hands-on access to parallel cluster and distributed grid technology.
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SDCI HPC Improvement: High-Productivity Performance Engineering (Tools, Methods, Training) for NSF HPC Applications
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ST-HEC: Collaborative Research: Scalable, Interoperable Tools to Support Autonomic Optimization of High-End Applications
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依托单位:
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依托单位:
海外基金