Collaborative Research: Neural computational rules of robust and generalizable learning

生物有机体可以从几个例子中学习，并将这些知识应用于新的情况。例如，人类可以通过几个实例来了解树，识别不同形状和大小的树，以及可以在不同的季节、时间和场景中识别树。这种快速学习和推广到更广泛情况的能力是生物系统独有的。相比之下，目前的人工智能模型只有在对每个用例中可能遇到的所有可能场景进行训练的情况下，才能获得接近人类水平的性能。这种水平的培训是不切实际、低效和不切实际的。该项目旨在直接从生物大脑学习，以识别新的学习规则，并将其应用于人工智能。研究人员训练活的昆虫，记录它们大脑中的神经元信号，并使用计算模型来确定快速、可靠和高效学习的关键学习规则。这些生物学原理为人工智能系统开发了新的强大算法，这些算法可以快速学习，将知识转移到新的任务，并且是健壮和高效的。从医疗保健到国家安全，人工智能在现代社会中都是必不可少的。该项目导致基础科学发现以及重大的社会影响。此外，研究人员还为高中生、本科生和研究生开设暑期工作坊，提供实践研究经验。两位研究人员都致力于通过这个项目培训代表不足的少数族裔学生。联想学习是影响人类和动物生活中行为结果的关键适应机制。生物系统表现出将习得的刺激概括到不同背景下的能力，即使是从少量的例子。然而，生物系统中快速、稳健和可推广的学习背后的基本神经计算还没有完全被理解。在上游神经回路对系统级学习的贡献以及生物物理学习规则的扩展方面存在知识差距，以开发新的人工智能(AI)算法。无脊椎动物嗅觉系统在组织和功能上与人类嗅觉系统有相似之处，使其成为研究可概括的联想学习规则的理想模型。在这项研究中，研究人员揭示了控制蝗虫嗅觉通路中央回路中概括性联想学习的神经计算规则，以及它们与行为结果的联系。具体目标如下：(A)确定联想学习引起的蝗虫触角叶泛化学习的神经反应和神经关联的变化；(B)使用一种新的计算机器学习方法确定潜在的泛化学习规则；(C)通过行为实验验证从计算机模型中导出的学习规则。这些目标的发现增强了对概括性联想学习基本原理的理解，并确定了用于健壮和概括性学习的规范人工智能算法。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。

Living organisms can learn from a few examples and apply that knowledge to new situations. For instance, humans can learn about trees from a few instances and recognize trees of different shapes and sizes, as well as can identify trees during different seasons, times of day, and scenes. This ability to learn fast and generalize to broader situations is unique to biological systems. In contrast, current artificial intelligence models can only attain close to human-level performance if they are trained on all possible scenarios they would encounter in each use case. This level of training is impractical, inefficient, and unrealistic. This project aims to learn directly from biological brains to identify new learning rules and apply them to artificial intelligence. The investigators train live insects, record neuronal signals from their brains, and employ computational models to identify key learning rules for fast, reliable and efficient learning. These biological principles inform the development of novel powerful algorithms for AI systems that can learn quickly, transfer knowledge to new tasks, and be robust and efficient. AI is essential in modern society, from healthcare to national security. This project leads to fundamental basic science discoveries as well as significant societal impacts. Additionally, the researchers establish summer workshops for high school, undergraduate, and graduate students, providing hands-on research experience. Both investigators are committed to training underrepresented minority students through this project.Associative learning is a crucial adaptive mechanism that influences behavioral outcomes in human and animal life. Biological systems exhibit the ability to generalize learned stimuli to diverse contexts, even from a small number of examples. However, the fundamental neural computations underlying fast, robust, and generalizable learning in biological systems are not fully understood. There exists a knowledge gap regarding the contribution of upstream neural circuits to system-level learning and the extension of biophysical learning rules for the development of new artificial intelligence (AI) algorithms. The invertebrate olfactory system shares organizational and functional similarities with the human olfactory system, making it an ideal model for investigating generalizable associative learning rules. In this study, the investigators uncover the neural computational rules governing generalizable associative learning in the central circuitry of the locust olfactory pathway and their connection to behavioral outcomes. Specific objectives are as follows: (a) Determine changes in neural responses and neural correlates of generalizable learning in the locust antennal lobe induced by associative learning; (b) Identify potential learning rules for generalization using a novel computational machine learning approach; (c) Validate the derived learning rules from the computer model through behavioral experiments. Findings from these objectives enhance understanding of the fundamental principles underlying generalizable associative learning and identify canonical AI algorithms for robust and generalizable learning.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.