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

不同的主体以不同的方式完成决策等认知任务。这种受试者之间的变异性的一个重要表现是通过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移动眼睛;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会调节皮层活动，因此在决策过程中建模和解释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并量化其神经起源是理解认知行为神经基础的关键第一步。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。在认知任务期间，由于神经活动受到任务期间运动的强烈调制，因此无法完全理解大规模神经和行为数据，并且自发运动的影响，未经指导的动作对学习和认知的影响还不太清楚。将运动相关的神经动力学与认知任务相关的神经动力学分离的一个重大挑战是受试者之间的变异性的存在。传统上，通过关注大量主题中存在的表征来解决可变性。相比之下，本研究将通过将其与任务的跨学科部分解耦来明确地模拟自发运动和受试者间的变异性。具体来说，该项目将通过学习独立于受试者的动力学模型，同时单独量化特定于受试者的动力学，来利用小鼠神经活动的跨受试者相似性。此外，下一代神经刺激算法不应要求对每个受试者进行广泛的培训。为了实现这一目标，该项目将使用经过训练的跨学科模型作为新学科的初始模型，同时使用个体数据进一步完善它。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。在一个综合的计算和实验的方法，这些模型将被用来调查的贡献，高维皮层活动的自发运动和认知tasks.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

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.