CRCNS Research Proposal: Network models of cortical and subcortical interactions for dynamical control of decision making

CRCNS 研究提案：用于决策动态控制的皮质和皮质下相互作用的网络模型

• 批准号: 2207895
• 负责人: Rishidev Chaudhuri
• 金额: $ 66.01万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207895&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Network; models

Decisions in the brain are collectively made by a network of interacting brain regions that each play different roles in the decision-making process. These distributed networks are flexible, able to respond effectively to changing circumstances, while also highly robust, and able to preserve functionality even when partially disrupted or damaged. This project seeks to understand how these brain regions interact with each other during decision making and how these interactions confer their remarkable flexibility and robustness. To do this, the research team will combine cutting edge technologies for recording and modifying the activity of neurons while rats make decisions, with machine learning techniques for modeling the data generated. The project will improve understanding of brain systems that support decision making and cognition more generally, while also providing critical insight for the next generation of brain-inspired artificial intelligence systems.The proposal combines large-scale multi-region recordings in awake, behaving rats and data-driven recurrent neural network modeling to investigate the role of association cortex during decision-making through its impact on interacting subcortical areas. The first part of the project will use Neuropixels recordings along with multi-area recurrent neural network modeling to identify whether and how association cortex plays a role in controlling interconnected subcortical dynamics during decision making. The recordings will be targeted to two areas of association cortex and two subcortical regions while rats perform decision tasks that require flexible integration of noisy sensory information over time. The network modeling will be used to disambiguate competing hypotheses for the specific roles of each brain region in shaping neural dynamics during decision formation. The second part of the project will combine similar network modeling with experimental perturbation of brain activity through optogenetics to identify mechanisms that underlie the robustness of neural decision making. The investigators will identify mechanisms of structural robustness, which arise from the architecture of the distributed network itself, and mechanisms of dynamic robustness, which involve active compensation for perturbation. In this manner, the project will synergize recent exciting advances in both machine learning and tools for systems neuroscience to create a tight experiment-theory loop to address questions with broad interdisciplinary importance, with implications at the core of developing principled treatments for cognitive disorders.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.

大脑中的决策是由相互作用的大脑区域网络共同做出的，每个区域在决策过程中发挥不同的作用。这些分布式网络非常灵活，能够有效地响应不断变化的环境，同时也非常稳健，即使部分中断或损坏也能够保留功能。该项目旨在了解这些大脑区域在决策过程中如何相互作用，以及这些相互作用如何赋予它们非凡的灵活性和鲁棒性。为此，研究团队将结合用于记录和修改大鼠做出决策时神经元活动的尖端技术，以及用于对生成的数据进行建模的机器学习技术。该项目将增进对更广泛地支持决策和认知的大脑系统的理解，同时也为下一代受大脑启发的人工智能系统提供重要的见解。该提案结合了清醒、行为老鼠的大规模多区域记录和数据驱动的循环神经网络模型，通过其对相互作用的皮层下区域的影响来研究关联皮层在决策过程中的作用。该项目的第一部分将使用 Neuropixels 记录以及多区域循环神经网络建模来确定关联皮层是否以及如何在决策过程中控制相互关联的皮层下动力学中发挥作用。录音将针对关联皮层的两个区域和两个皮层下区域，而大鼠执行需要随着时间的推移灵活整合噪声感觉信息的决策任务。网络建模将用于消除关于每个大脑区域在决策形成过程中塑造神经动力学的特定作用的竞争假设的歧义。该项目的第二部分将通过光遗传学将类似的网络模型与大脑活动的实验扰动结合起来，以确定神经决策稳健性的机制。研究人员将确定由分布式网络本身的架构产生的结构鲁棒性机制，以及涉及扰动主动补偿的动态鲁棒性机制。通过这种方式，该项目将协同机器学习和系统神经科学工具方面最近令人兴奋的进展，创建一个紧密的实验理论循环，以解决具有广泛跨学科重要性的问题，其影响是开发认知障碍原则性治疗的核心。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。