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Workshop on Models of Thought Processes: Insights Toward Chemical Systems; June 20-23, 2005; Arlington, VA

Workshop on Models of Thought Processes: Insights Toward Chemical Systems; June 20-23, 2005; Arlington, VA
思维过程模型研讨会:对化学系统的见解;
批准号:
0403126
负责人:
Irving Epstein
金额:
$9.96万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-04-01 至 2006-03-31

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中文摘要
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英文摘要
This award by the Division of Chemistry in the Directorate for Mathematical and Physical Sciences, the Division of Integrative Biology and Neuroscience in the Directorate for Biological Sciences, the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral and Economic Sciences, the Division of Computer and Network Systems and the Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering, and the Division of Chemical and Transport Systems in the Directorate for Engineering, supports a Workshop on "Models of Thought Processes: Insights toward Chemical Systems" to be held at the National Science Foundation in June 20-22, 2004.This workshop will bring together scientists from several disciplines to explore how chemistry may offer insights into and models for such key processes as learning, memory, motor control, integration of sensors, signaling, computation and how knowledge gained about these processes from other areas of science may open up new and exciting areas for chemists. Invited participants from a range of fields, including chemistry, biology, neuroscience, cognitive science, computer science, mathematics, physics, and engineering will engage in half-day sessions devoted to different aspects of learning and other processes and in a final session to summarize and synthesize the questions and insights that have arisen. Participants will be asked to prepare materials before the workshop that will highlight key issues for cross-disciplinary attack. A workshop summary and final report will be prepared that will survey particularly promising directions and approaches. Specific areas to be addressed are: 1) chemically and biologically based approaches to computation; 2) models of learning in chemical systems; 3) transmission of information at the local (synaptic) and global (network) levels; 4) synthesis and analysis of key molecules and structural elements important in learning, memory, computation, sensing and signaling. A professional science writer will be employed to assist with preparing pre-workshop materials and the final report, and to craft materials for press releases and other forms of public dissemination. The broader impact of this workshop includes its exploration of the diversity of scientific thought in this inherently interdisciplinary area, with strong prospects for generating new collaborations across fields. A small number of postdoctoral and/or advanced graduate students will be invited to attend including individuals from underrepresented groups. The results of the workshop will be made available on a publicly accessible website. Other goals include the identification of areas in which a multidisciplinary, multilevel approach is likely to yield significant new insights, and initiation of interdisciplinary collaborations, which may be relevant to several interdisciplinary NSF programs.
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Nonlinear Chemical Dynamics
  • 批准号:
    1856484
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $54.0万
  • 财政年份:
    2019
  • 负责人:
    Irving Epstein
  • 依托单位:
Nonlinear Chemical Dynamics
  • 批准号:
    1362477
  • 项目类别:
    Standard Grant
  • 资助金额:
    $60.89万
  • 财政年份:
    2014
  • 负责人:
    Irving Epstein
  • 依托单位:
Nonlinear Chemical Dynamics
  • 批准号:
    1012428
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $53.6万
  • 财政年份:
    2011
  • 负责人:
    Irving Epstein
  • 依托单位:
REU Site: Investigations in Catalysis at Brandeis University
  • 批准号:
    0552767
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2006
  • 负责人:
    Irving Epstein
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
新型手性NAD(P)H Models合成及生化模拟