Collaborative Research: Neural computational rules of robust and generalizable learning

生物可以从一些例子中学习，并将这些知识应用到新的情况中。例如，人类可以从几个实例中了解树木，并识别不同形状和大小的树木，以及可以在不同的季节、时间和场景中识别树木。这种快速学习和推广到更广泛情况的能力是生物系统所独有的。相比之下，如果当前的人工智能模型在每个用例中遇到的所有可能场景中进行训练，它们只能达到接近人类水平的性能。这种级别的培训是不切实际的、低效的和不现实的。该项目旨在直接从生物大脑中学习，以识别新的学习规则并将其应用于人工智能。研究人员训练活昆虫，记录来自它们大脑的神经元信号，并使用计算模型来识别快速、可靠和有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新的强大算法提供了信息，这些算法可以快速学习，将知识转移到新的任务中，并且稳健高效。人工智能对现代社会至关重要，从医疗保健到国家安全。这个项目不仅带来了基础科学的发现，也产生了重大的社会影响。此外，研究人员还为高中生、本科生和研究生建立了暑期讲习班，提供实践研究经验。两位研究者都致力于通过这个项目培训少数族裔学生。联想学习是影响人类和动物行为结果的重要适应机制。生物系统表现出将学习到的刺激推广到不同环境的能力，即使是从少量的例子中。然而，生物系统中快速、稳健和可推广学习的基本神经计算尚未完全理解。关于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对开发新的人工智能（AI）算法的贡献存在知识缺口。无脊椎动物嗅觉系统在组织和功能上与人类嗅觉系统相似，这使其成为研究可推广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电路中泛化联想学习的神经计算规则及其与行为结果的联系。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相关物的变化；(b)使用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导出的学习规则。这些目标的发现增强了对可泛化联想学习基本原理的理解，并确定了用于鲁棒和可泛化学习的规范人工智能算法。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。

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.