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

职业：使用多区域神经网络理清大脑中的区域间通信

• 批准号: 2046583
• 负责人: Kanaka Rajan
• 金额: $ 54.93万
• 依托单位: Icahn School of Medicine at Mount Sinai
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046583&HistoricalAwards=false
• 关键词: CAREER; Untangling; Inter; Area; Communication

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.

人类和动物的行为，如学习、记忆和决策，需要大脑各区域的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不同的大脑区域如何交流和共同工作以产生行为。这项工作将识别全脑通信的共同和独特特征，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，该项目通过两个互补方案促进社区参与、多样性和包容性：“计算机神经科学”，通过无行话和视觉吸引力的漫画媒介，将计算神经科学的研究成果带入纽约市服务不足的教室;和学生外展神经科学与CS集成（SONiC）计划，一年一度的实验室暑期学校，让纽约地区的高年级大学生和研究生的手-虽然神经科学的快速发展促进了对个体大脑的更深入理解，区域及其职能，这些区域一般不是孤立运作的。然而，人们对调节许多行为输出背后的全脑通信的过程知之甚少。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，鲁棒的，灵活的多区域递归神经网络（RNN）模型，具有区域间通信;和（2）分析方法，以推断区域内和区域间相互作用的方向和幅度。多区域RNN将受到真实的神经数据的约束，以揭示真实的生物系统的机制，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大脑回路如何利用生物可塑性来获得新技能。最后，人类电生理数据的RNN建模将有助于识别跨多个物种保守或发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和人工智能/机器学习社区之间的交叉，并提供在更广泛的神经科学社区中共享的定量技术。此外，该项目将为新一代的计算神经科学家创造一个包容、友好的环境。该奖项反映了NSF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。

项目成果

Curriculum learning as a tool to uncover learning principles in the brain

课程学习作为揭示大脑学习原理的工具

• 发表时间: 2022
• 期刊: International Conference on Learning Representations
• 作者: Kepple, D.;Engelken, R.;Rajan, K
• 通讯作者: Rajan, K