Collaborative Research: NCS-FO: A model-based approach to probe the role of spontaneous movements during decision-making

合作研究：NCS-FO：一种基于模型的方法，探讨自发运动在决策过程中的作用

• 批准号: 2219946
• 负责人: Anne Churchland
• 金额: $ 44.73万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219946&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FO; model

Cognitive tasks such as decision-making are performed by different subjects in different ways. An important manifestation of this subject-to-subject variability is through spontaneous movements during task performance. For example, people may tap the floor or move their eyes while learning a cognitive task; similarly, mice display idiosyncratic whisker, facial, or other movements. Since both spontaneous movements and cognitive tasks modulate cortical activity, modeling and interpreting neural activity during decision-making is a major challenge. Deciphering the function and quantifying the neural origin of spontaneous movements while explicitly modeling individual neural dynamics is a crucial first step in understanding the neural basis of cognitive behaviors. Elucidating the role of spontaneous movements in cognition will also be important for assessing and developing novel therapies for neurobehavioral disorders such as attention deficit hyperactivity disorder.It has not been possible to fully understand large-scale neural and behavioral data during cognitive tasks because neural activity is strongly modulated by movements during the task, and the effect of spontaneous, uninstructed movements on learning and cognition is not well understood. A significant challenge in disentangling the neural dynamics related to movements from those related to cognitive tasks is the presence of subject-to-subject variability. Traditionally, the variability is resolved by focusing on the representations that are present across a large number of subjects. In contrast, this study will explicitly model spontaneous movements and subject-to-subject variability by decoupling it from the across-subject part of the task. Specifically, this project will leverage across-subject similarity in neural activity in mice by learning subject-independent dynamical models, while separately quantifying subject-specific dynamics. Furthermore, the next generation of neurostimulation algorithms should not require extensive training on each subject. For this goal, this project will use a trained across-subject model as the initial model for new subjects, while refining it further with individual data. This transfer learning approach will lead to effective model-based control strategies to design closed-loop neurostimulation. In an integrative computational and experimental approach, these models will be used to investigate the contribution of high-dimensional cortical activity to spontaneous movements and cognitive tasks.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.

像决策这样的认知任务是由不同的主体以不同的方式完成的。这种主体间可变性的一个重要表现是在任务执行过程中的自发运动。例如，人们在学习认知任务时可能会轻拍地板或转动眼睛；同样，老鼠也会表现出特殊的胡须、面部或其他动作。由于自发运动和认知任务都调节皮层活动，建模和解释决策过程中的神经活动是一个重大挑战。在明确建模个体神经动力学的同时，破译自发运动的功能和量化神经起源是理解认知行为的神经基础的关键的第一步。阐明自发运动在认知中的作用对于评估和开发神经行为障碍（如注意缺陷多动障碍）的新疗法也很重要。由于神经活动在任务过程中受到运动的强烈调节，自发的、无指导的运动对学习和认知的影响尚不清楚，因此不可能完全理解认知任务期间的大规模神经和行为数据。将与运动相关的神经动力学与与认知任务相关的神经动力学分离开来的一个重大挑战是主体间可变性的存在。传统上，可变性是通过关注在大量主题中出现的表示来解决的。相比之下，本研究将通过将其与任务的跨主题部分分离，明确地模拟自发运动和主体之间的可变性。具体来说，该项目将通过学习与学科无关的动力学模型来利用小鼠神经活动的跨学科相似性，同时分别量化学科特定的动力学。此外，下一代神经刺激算法不需要对每个主题进行广泛的训练。为了实现这个目标，这个项目将使用一个训练好的跨主题模型作为新主题的初始模型，同时使用单个数据进一步对其进行细化。这种迁移学习方法将导致有效的基于模型的控制策略来设计闭环神经刺激。在综合计算和实验方法中，这些模型将用于研究高维皮层活动对自发运动和认知任务的贡献。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。