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

人类和动物的行为，如学习、记忆和决定，需要大脑各区域神经元和回路的相互作用。然而，尽管这些相互作用很重要，但人们对调节这些全脑通信的过程知之甚少。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大脑不同区域如何沟通和协同工作以产生行为。这项工作将确定全脑通信的共享和独特特征，以指导新的实验研究，并使新的计算机模型更好地定义大脑功能。此外，该项目还通过两个互补项目促进社区参与、多样性和包容性：“漫画书神经科学”，将计算神经科学的研究成果通过漫画这种没有行话和视觉吸引力的媒介引入纽约市缺乏服务的教室；以及与CS （SONiC）集成的神经科学学生拓展计划（Student Outreach for Neuroscience with CS (SONiC) program），这是一个以实验室为基础的年度暑期学校，为纽约地区的高级学院和研究生提供大脑数据可视化和建模的实践经验。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，但这些区域通常不是孤立地运作的。然而，对于许多行为输出背后的调节全脑交流的过程，我们知之甚少。为了揭示全脑通信的基本原理，该项目将产生(1)具有区域间通信的新型，可扩展，鲁棒和灵活的多区域递归神经网络（RNN）模型；(2)推断区域内部和区域之间相互作用的方向和程度的分析方法。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，大脑区域内和区域间神经元的合作活动如何产生决策等复杂行为。对这些模型进行逆向工程将揭示多区域脑回路如何利用生物可塑性来获得一项新技能。最后，人类电生理数据的RNN建模将有助于识别在多个物种中保守或分散的区域间通信过程。新模型和工具的广泛采用将改变对相互作用的大脑区域如何协调复杂行为的理解，并为未来的实验范式提供信息。该研究还将促进神经科学与人工智能/机器学习社区之间的交叉受精，并为更广泛的神经科学社区提供定量技术共享。此外，该项目将为多元化的新一代计算神经科学家营造一个包容、友好的环境。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。

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.