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

大脑中的决策是由相互作用的大脑区域组成的网络共同做出的，每个区域在决策过程中扮演着不同的角色。这些分布式网络很灵活，能够有效地应对不断变化的环境，同时也非常健壮，即使在部分中断或损坏的情况下也能够保留功能。这个项目试图了解这些大脑区域在决策过程中是如何相互作用的，以及这些相互作用如何赋予它们非凡的灵活性和健壮性。为了做到这一点，研究团队将结合尖端技术来记录和修改大鼠做出决策时神经元的活动，以及机器学习技术来对产生的数据进行建模。该项目将提高对大脑系统的理解，这些系统更广泛地支持决策和认知，同时也为下一代大脑启发的人工智能系统提供关键的洞察力。该提议结合大鼠清醒、行为的大规模多区域记录和数据驱动的递归神经网络建模，通过关联皮质对相互作用的皮质下区域的影响，研究关联皮质在决策过程中的作用。该项目的第一部分将使用神经像素记录和多区域递归神经网络建模，以确定关联皮质在决策过程中是否以及如何在控制相互关联的皮质下动态方面发挥作用。当老鼠执行决策任务时，记录的目标将是两个关联皮质区域和两个皮质下区域，这些任务需要随着时间的推移灵活整合嘈杂的感觉信息。网络建模将被用来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角色的相互矛盾的假设。该项目的第二部分将通过光遗传学将类似的网络建模与大脑活动的实验扰动相结合，以确定神经决策健壮性的基础机制。研究人员将确定由分布式网络本身的体系结构产生的结构健壮性机制，以及涉及对扰动的主动补偿的动态健壮性机制。以这种方式，该项目将协同机器学习和系统神经科学工具方面的最新令人兴奋的进展，以创建一个紧密的实验-理论环路，以解决具有广泛跨学科重要性的问题，并涉及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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.