NSF Convergence Accelerator - Track D: A Standardized Model Description Format for Accelerating Convergence in Neuroscience, Cognitive Science, Machine Learning and Beyond

NSF 融合加速器 - 轨道 D：用于加速神经科学、认知科学、机器学习等领域融合的标准化模型描述格式

• 批准号: 2040682
• 负责人: Jonathan Cohen
• 金额: $ 99.31万
• 依托单位: Princeton University
• 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: 2020-09-15 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040682&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Standardized

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. Accelerating convergence in science and technology depends on the ability to represent and share not only data, but also theories and models in the most objective, transparent, and reproducible way possible. This project will develop a Model Description Format (MDF) that can be used for computational models that span from neuroscience and psychology to machine learning, and that can serve as the foundation for extensions that serve an even broader scope of models in population biology and the social sciences. Such an MDF would have numerous benefits, both scientific and technological, including: dissemination and validation of model reproducibility; migration of models across domains (e.g., use of models of brain function in machine learning applications); integration of models at different levels of analysis (e.g., biophysically-realistic neural models into models of cognitive function, cognitive models as agents in population level models); exploitation of complementary strengths of existing packages (e.g., design in a familiar environment but execute in one with better tools for parameter tuning and/or data-fitting); and more efficient development of new tools, by providing developers with a representative diversity of models, all in a common format.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF 融合加速器支持以使用为基础的、基于团队的多学科努力，以解决国家重大挑战，并将在不久的将来为社会提供有价值的成果。科学技术的加速融合不仅取决于以最客观、透明和可复制的方式表示和共享数据的能力，还取决于理论和模型的能力。该项目将开发一种模型描述格式（MDF），可用于从神经科学、心理学到机器学习的计算模型，并且可以作为扩展的基础，为人口生物学和社会科学中更广泛的模型提供服务。这样的MDF将有许多科学和技术上的好处，包括：模型再现性的传播和验证；模型跨领域迁移（例如，在机器学习应用中使用大脑功能模型）；不同分析层面的模型整合（例如，生物物理现实神经模型到认知功能模型中，认知模型作为群体层面模型中的主体）；利用现有软件包的互补优势（例如，在熟悉的环境中进行设计，但在具有更好的参数调整和/或数据拟合工具的环境中执行）；通过为开发人员提供具有代表性的多样性模型（全部采用通用格式），更有效地开发新工具。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。