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，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。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（Model Description Format，简称EML），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。这样一个模型将有许多科学和技术上的好处，包括：模型可重复性的传播和验证;模型跨领域的迁移（例如，在机器学习应用中使用脑功能模型）;在不同分析级别的模型的集成（例如，将生物药理学上现实的神经模型转化为认知功能模型，认知模型作为群体水平模型中的代理）;利用现有包的互补优势（例如，在熟悉的环境中设计，但在一个更好的工具进行参数调整和/或数据拟合）;和更有效地开发新的工具，通过为开发人员提供具有代表性的多样性的模型，所有在一个共同的格式。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.