RUI: A Proposal for Research on Computing with Neural Models of Computation

RUI：神经计算模型计算研究提案

• 批准号: 9800086
• 负责人: Bhaskar DasGupta
• 金额: $ 12.15万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-15 至 2001-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9800086&HistoricalAwards=false
• 关键词: RUI; Proposal; Research; Computing; Neural

The last two decades have seen a resurgence in the interest of investigating various neural models of computation. Researchers from various fields such as mathematics, computer science, cognitive science and biology are either trying to analyze the capabilities and limitations of various neural models, or proposing extensions or modifications of current neural models to make these models more consistent with applications in biology, control theory, pattern recognition and other related areas. Recent advanced mathematical techniques using results on semi- algebraic sets, model theory, statistical methods and theory of real numbers have enabled researchers to answer many theoretical questions about neural models which were previously open. This project continues further investigation of the capabilities and limitations of various neural models, either from a theoretical point of view using some of the advanced mathematical techniques mentioned in the references, or from an experimental point of view. The ultimate objective is to be able to provide further insight into the working mechanisms of these models, and to modify or extend existing models whenever appropriate. The theoretical investigations to be carried out in this project will also be valuable in various practical application of neural models. This project involves interaction with undergraduate students which should prepare them for possible graduate studies in mathematics, computer science and theoretical biology.

在过去的二十年里，人们对研究各种神经计算模型的兴趣重新兴起。 来自数学、计算机科学、认知科学和生物学等各个领域的研究人员要么试图分析各种神经模型的能力和局限性，要么提出对当前神经模型的扩展或修改，以使这些模型更符合生物学、控制等领域的应用。理论、模式识别等相关领域。 最近使用半代数集、模型理论、统计方法和实数理论结果的先进数学技术使研究人员能够回答许多以前未解决的有关神经模型的理论问题。该项目继续进一步研究各种神经模型的功能和局限性，无论是从理论角度使用参考文献中提到的一些高级数学技术，还是从实验角度。 最终目标是能够进一步深入了解这些模型的工作机制，并在适当的时候修改或扩展现有模型。 该项目中要进行的理论研究对于神经模型的各种实际应用也很有价值。 该项目涉及与本科生的互动，帮助他们为可能的数学、计算机科学和理论生物学研究生学习做好准备。