CRCNS Research Proposal: Collaborative Research: Evaluating Machine Learning Architectures Using a Massive Benchmark Dataset of Brain Responses to Natural Scenes

机器学习技术有可能从根本上改变对人脑的研究，但需要比传统神经科学实验中通常收集的数据多得多的数据。该项目的目标是通过从人类视觉系统生成大量的大脑反应数据集，推动ML技术在神经科学研究中的应用。由此产生的数据集将免费提供给科学家、教育工作者和学生。通过一年一度的建模比赛，神经科学家将获得应用先进计算方法的经验，ML研究人员将对人脑的挑战和复杂性有更深入的了解。建模比赛的结果将在由机器学习、神经科学研究人员和学生参加的年度会议上公布，为两个小组提供互动和讨论方法的机会。该项目将促进神经科学家和人工智能研究人员之间的开放合作，以及分享数据、想法和进展的文化。这项工作的长期目标是产生数据，这些数据将导致开发经过实验验证和计算能力强大的人类视觉系统模型。项目负责人将使用高场(7特斯拉)功能磁共振成像(FMRI)来测量人类观察者对广泛采样的自然图像的大脑反应。具体目标如下：(1)获取、前处理和分发利用最先进的成像技术的海量高分辨率功能磁共振数据集。该数据集将包括从ML社区广泛使用的图像集合中提取的大约8万张照片的大脑反应的多个样本。(2)在认知计算神经科学会议上建立并主办一年一度的模拟这一丰富数据集的比赛。(3)通过测试受ML启发的新架构作为视觉系统的模型，在ML架构和人脑之间架起一座桥梁。项目负责人将特别关注ML的最新发展，这些进展提出了关于背部视觉流的新假设。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning technologies have the potential to radically transform the study of the human brain, but require far more data than is typically collected during conventional neuroscience experiments. The goal of this project is to drive the application of ML techniques to neuroscience research by generating a massive dataset of brain responses from the human visual system. The resulting dataset will be freely available to scientists, educators, and students. Through a yearly modeling competition, neuroscientists will gain experience in the application of advanced computational methods and ML researchers will gain a deeper understanding of the challenges and complexities of the human brain. Results of the modeling competition will be presented at an annual conference attended by both machine learning and neuroscience researchers and students, providing an opportunity for the two groups to interact and discuss approaches. This project will foster open collaboration between neuroscientists and artificial intelligence researchers and a culture of sharing data, ideas, and progress. The long-term goal of this work is to generate data that will lead to the development of experimentally validated and computationally powerful models of the human visual system. The project leaders will use high-field (7 Tesla) functional magnetic resonance imaging (fMRI) to measure brain responses to a broad sampling of natural images in human observers. The specific objectives are as follows: (1) Acquire, pre-process, and distribute a massive, high-resolution fMRI dataset that exploits state-of-the-art imaging techniques. The dataset will include multiple samples of brain responses to roughly eighty thousand photographs drawn from an image collection that is widely used by the ML community. (2) Establish and host an annual competition for modeling this rich dataset at the conference on Cognitive Computational Neuroscience. (3) Bridge the gap between ML architectures and the human brain by testing new ML-inspired architectures as models of the visual system. The project leaders will focus specifically on recent developments in ML that suggest new hypotheses about the dorsal visual stream.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.