EFRI BRAID: Principles of sleep-dependent memory consolidation for adaptive and continual learning in artificial intelligence

人工神经网络（ann）是人工智能（AI）的一种形式，用于从自动驾驶汽车到医学再到机器人系统的应用。尽管它们在某些学习任务上可以匹配甚至超过人类的表现，但它们无法再现人类思维的重要特征，例如快速和持续的学习，将知识转移到新任务中，以及能源效率。事实上，人工神经网络在学习新信息时通常会忘记它们所知道的东西，因此需要从头开始教它们重新学习。在不断变化和不可预测的环境中的现实应用中，如果在生活中可能发生的所有场景中进行训练，人工神经网络只能达到接近人类水平的性能。这种水平的培训既低效又不现实。在自然的大脑中，睡眠被认为对智力很重要。在睡眠期间，大脑会重复和回放白天学到的东西，这有助于防止遗忘，将其推广到新的情况，并创造新的知识。在这个项目中，从睡眠生物学中学到的原理将被用来为人工智能系统开发强大的新算法，这些算法可以持续学习，从很少的例子中学习，将从旧任务中学到的知识转移到新任务中，并且稳健高效。由于人工智能和人工神经网络对现代世界至关重要，从医疗保健到电子产品再到国防，这个项目有可能产生重大的社会影响。该项目采用多学科方法，通过一系列以高中、本科生（包括社区学院）和研究生为重点的教育活动，支持代表性不足的群体更广泛地参与STEM研究。该项目旨在将睡眠研究的见解转化为人工智能（AI）中持续学习、泛化和知识转移所必需的深度学习系统的改进。利用人工神经网络和蜜蜂大脑的信息处理在架构上的相似性，这个项目的主要目标是：(a)在体内和生物物理硅模型中描述蜜蜂大脑中的多相睡眠的精细时空细节，以揭示睡眠在记忆巩固中作用的关键原理；(b)应用这些结果来支持开发新的机器学习算法，用于在复杂和动态环境中进行自适应和持续学习。该研究将建立一个基于经验的多相睡眠理论，然后将其应用于人工神经网络，而开发“人工智能睡眠”的过程将有助于加强工程、计算神经科学和神经行为学之间的联系，为处于各种职业阶段的研究人员提供帮助。为了实现这一目标，项目团队还计划采用四层教育方法，针对高中、社区大学、学士学位课程和研究生课程的学生，向更广泛的学生介绍人工智能、睡眠生物学和计算神经科学的主题。该项目由美国工程局的“研究与创新前沿脑激发动力学工程节能电路和人工智能项目”和生物科学局的“神经系统/调制项目”共同资助。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。

Artificial Neural Networks (ANNs) are a form of Artificial Intelligence (AI) used in applications from self-driving cars to medicine to robotic systems. Although they can match and even exceed human performance on some learning tasks, they fail to reproduce important characteristics of the human mind, such as quick and continual learning, transfer of knowledge to the new tasks, and energy efficiency. Indeed, ANNs commonly forget what they knew when new information is learned, and so they need to be taught from scratch to re-learn. In real-life applications in changing and unpredictable environments, ANNs can only reach near human-level performance if they are trained on all possible scenarios that could happen in life. This level of training is inefficient and unrealistic. In natural brains, sleep is thought to be important for intelligence. During sleep, the brain repeats and replays what was learned during the day, and this helps to prevent forgetting, to generalize to new situations, and to create new emerging knowledge. In this project, principles learned from the biology of sleep will be used to develop powerful new algorithms for AI systems that can learn continuously and from few examples, transfer knowledge learned from old tasks to new tasks, and be robust and efficient. Because AI and ANNs are so fundamental to the modern world, from healthcare to electronics to national defense, this project has the potential to make a significant societal impact. The project takes a multi-disciplinary approach and supports broader participation of underrepresented groups in STEM research through a range of educational activities focused on high school, undergraduate, including community college, and graduate students.This project aims to translate insights from the study of sleep to improvements in deep-learning systems necessary for continual learning, generalization, and transfer of knowledge in artificial intelligence (AI). Taking advantage of the architectural similarities between information processing in ANNs and the honeybee brain, the main goals of this project are: (a) to characterize multi-phasic sleep in the honeybee brain in vivo and in biophysical in silico models in fine spatio-temporal detail to reveal the critical principles of the role of sleep in memory consolidation, and (b) to apply these results to support the development of novel machine-learning algorithms for adaptive and continual learning in complex and dynamic environments. The study will develop an empirically grounded theory of multi-phasic sleep that will be then applied to artificial neural networks, and the process of developing “sleep for AI” will help to strengthen connections between engineering, computational neuroscience, and neuroethology for researchers at a range of career stages. To accomplish this goal, the project team also plans a four-tiered educational approach targeting students in high schools, community colleges, bachelor’s degree programs, and graduate-level programs to introduce a wider range of students to the topics in AI, sleep biology, and computational neuroscience. This project is funded jointly by the Emerging Frontiers in Research and Innovation Brain-Inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence Program of the Engineering Directorate and the Neural Systems/Modulation Program of the Biological Sciences Directorate.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.