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

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。加速科学和技术的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），可用于从神经科学和心理学到机器学习的计算模型，并可作为扩展的基础，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方面的好处，包括：传播和验证模型的可重复性；跨领域的模型迁移（例如，在机器学习应用中使用大脑功能模型）；整合不同分析层次的模型（例如，将生物物理现实的神经模型纳入认知功能模型，将认知模型作为主体纳入人口水平模型）；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）；通过向开发人员提供具有代表性的模型多样性，所有这些模型都采用通用格式，从而更有效地开发新工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.