CAREER: Untangling Inter-Area Communication in the Brain Using Multi-Region Neural Networks

人类和动物的行为，如学习、记忆和决策，需要跨大脑区域的神经元和电路的相互作用。然而，尽管这些相互作用很重要，但人们对调控这些全脑交流的过程知之甚少。这项研究基于对人类和动物行为的测量，建立了大脑的计算机模型，并使用这些模型来识别不同的大脑区域如何沟通和合作来产生行为。这项工作将确定全脑交流的共同和独特的特征，以指导新的实验研究，并使新的计算机模型能够更好地定义大脑功能。此外，这个项目通过两个相辅相成的项目来促进社区的参与度、多样性和包容性：“对比书本神经科学”，它通过无术语和视觉吸引力的漫画媒介，将计算神经科学的研究成果带到纽约市服务不足的课堂上；以及神经科学学生外展与CS(SONIC)计划，这是一个以实验室为基础的年度暑期班，为纽约市地区的高中生和研究生提供可视化和建模大脑数据的动手体验。虽然神经科学的快速发展促进了对单个大脑区域及其功能的更深入了解，但这些区域通常并不是孤立运行的。然而，人们对调控许多行为输出背后的全脑沟通的过程知之甚少。为了揭示全脑通信的基本原理，该项目将产生(1)一种新的、可扩展的、健壮的、灵活的具有区域间通信的多区域递归神经网络(RNN)模型；以及(2)推断区域内和区域之间交互的方向和大小的分析方法。多区域RNN将受到真实神经数据的约束，以揭示真实生物系统的机制，例如，大脑区域内和跨大脑区域的神经元的合作活动如何引起决策等复杂行为。反向工程这些模型将揭示多区域大脑回路如何利用生物可塑性来获得一项新技能。最后，对人类电生理学数据的RNN建模将有助于识别跨多个物种的保守或不同的区域间通信过程。新模型和工具的更广泛采用将改变对相互作用的大脑区域如何凝聚功能以协调复杂行为和为未来的实验范式提供信息的理解。这项研究还将促进神经科学和人工智能/机器学习社区之间的交叉受精，并提供更广泛的神经科学界共享的量化技术。此外，该项目将为多样化的新一代计算神经科学家营造一个包容、欢迎的环境。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。

Human and animal behaviors like learning, remembering, and deciding require the interactions of neurons and circuits across regions of the brain. However, despite the importance of these interactions, remarkably little is known about the processes regulating these brain-wide communications. This research builds computer models of the brain based on measurements taken from humans and animals performing behaviors, and uses those models to identify how different brain regions communicate and work together to produce behaviors. This work will identify shared and distinct features of brain-wide communication to guide new experimental studies and enable new computer models to better define brain functions. Additionally, this project promotes community engagement, diversity, and inclusion through two complementary programs: "Comp-ic Book Neuroscience," which brings research findings from computational neuroscience into under-served classrooms in New York City through the jargon-free and visually appealing medium of comics; and the Student Outreach for Neuroscience Integrated with CS (SONiC) program, an annual lab-based summer school to give NYC-area senior college and graduate students hands-on experience with visualizing and modeling brain data.While rapid advances in neuroscience have catalyzed a deeper understanding of individual brain regions and their functions, these regions generally do not operate in isolation. Yet, little is known about processes regulating the brain-wide communication underlying many behavioral outputs. To reveal fundamental principles of brain-wide communication, this project will produce (1) a new, scalable, robust, and flexible class of multi-region recurrent-neural network (RNN) models with inter-area communication; and (2) analysis methods to infer the direction and magnitude of interactions within and between areas. Multi-region RNNs will be constrained with real neural data to uncover mechanisms of the real biological system, for instance, how the cooperative activity of neurons within and across brain regions gives rise to complex behaviors like decision-making. Reverse-engineering these models will reveal how multi-area brain circuits use biological plasticity to acquire a new skill. Finally, RNN modeling of human electrophysiology data will help identify inter-area communication processes that are conserved or divergent across multiple species. Wider adoption of the new models and tools will transform the understanding of how interacting brain areas function cohesively to orchestrate complex behaviors and inform future experimental paradigms. The research will also promote cross-fertilization between neuroscience and artificial intelligence/machine learning communities, and provide quantitative techniques shared in the broader neuroscience community. Furthermore, the project will foster an inclusive, welcoming environment for a diverse new generation of computational neuroscientists.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.