Identify the schemata by which subcortical signals influence frontal cortical dynamics and cognitive behaviors

总结，项目4（识别皮层下信号影响的图式 额叶皮质动力学和认知行为） 该研究项目的重点是通过以下方式研究皮层下区域和额叶皮层之间的动态相互作用： 丘脑在灵活的行为。额叶皮层，包括运动皮层和医学前额叶皮层， 与行为相关的复杂的神经活动模式。这些模式可以分解为活动 模式（即活动空间中的子空间），例如与短期记忆相关的持续活动，以及 与随意运动相关的快速调节的活动。复杂的行为对应于 皮层活动模式的序列。额叶皮层与高级（非感觉）丘脑紧密耦合 to mediate调解behavior行为.丘脑依次接受来自基底神经节和其他皮质下神经节的驱动输入 结构.我们检验了这样一个假设，即不同的皮层下输入到特定的丘脑区域控制着不同的 皮质活动的各个方面，包括设定短期记忆维持的时间段， 从运动规划到运动执行的过渡（目标1），以及更新和维护 在觅食过程中的具体行动。我们的建模框架（总体而言，项目5）将动力系统 神经计算的角度与实际的多区域神经电路，并作出预测，可以 进行神经生理学测试 我们在小鼠中采用了两种行为任务，这些任务涉及定义明确但不同的皮质活动模式。中 在记忆引导的反应任务中，前外侧运动皮层（ALM）中的神经元显示出准备活动， 它能预测未来的具体动向就在运动开始之前，准备活动崩溃了， 有利于驱动运动的活动模式。在动态觅食任务中，内侧前额叶皮层的神经元 （mPFC）显示出缓慢变化的活动模式，与一个动作的价值相比，另一个。 此活动将根据新信息（例如奖励的大小）进行更新。项目1 - 3提供信息 关于连接皮层下区域、丘脑和ALM / mPFC的回路。基于这些电路映射 实验中，我们将从连接的皮层下→丘脑→皮层电路进行同步记录， 使用新的多柄Neuropixels探针，具有5120个记录位点（项目3）。我们将联合收割机 用皮层下的光遗传学操作和现代多变量分析方法记录（数据 科学核心）跟踪皮层下信号如何通过丘脑传播到皮层。在记忆引导的 反应任务，我们将探讨黑质网状的作用，通过腹内侧核， 运动计划的维持，以及中脑运动中心（例如脚桥脑）的影响 核），通过后内侧背核起作用，从运动计划转换到运动 启动（目标1）。在觅食任务中，我们将探讨错误预测信号，例如从腹侧 苍白球（PALv），通过前内侧背丘脑更新mPFC中的记忆相关活动。这些 测量将测试关键模型的预测，并反过来告知认知的多区域电路模型 行为

Summary, Project 4 (Identify the schemata by which subcortical signals influence frontal cortical dynamics and cognitive behaviors) This research project is focused on dynamic interactions between subcortical areas and frontal cortex via thalamus during flexible behavior. Frontal cortex, including motor cortex and medical prefrontal cortex, displays complex patterns of neural activity that correlate with behavior. These patterns can be decomposed into activity modes (i.e. subspaces in activity space), such as the persistent activity correlated with short-term memory, and the rapidly modulated activity associated with voluntary movements. Complex behaviors correspond to sequences of cortical activity modes. Frontal cortex is tightly coupled with higher-order (non-sensory) thalamus to mediate behavior. Thalamus in turn receives driving input from the basal ganglia the and other subcortical structures. We test the hypothesis that different subcortex inputs to specific thalamic regions control different aspects of cortical activity, including setting the time periods when short-term memories are maintained, the transitions from motor planning to movement execution (Aim 1), and updating and maintaining the values of specific actions during foraging. Our modeling framework (Overall, Project 5) links the dynamical systems perspective of neural computation with actual multi-regional neural circuits and makes predictions that can be tested with neurophysiology. We employ two behavioral tasks in mice that engage well-defined but distinct cortical activity modes. In a memory-guided response task, neurons in the anterior lateral motor cortex (ALM) show preparatory activity, which predicts specific future movements. Just before the onset of movement, preparatory activity collapses in favor of activity modes that drive movement. In a dynamic foraging task, neurons in the medial prefrontal cortex (mPFC) show slowly varying activity patterns that correlate with the value of one action compared to another. This activity is updated based on new information, such as the size of a reward. Projects 1 - 3 provide information about the circuits linking subcortical areas, thalamus, and ALM / mPFC. Building on these circuit mapping experiments we will perform simultaneous recordings from connected subcortex → thalamus → cortex circuits using new multi-shank Neuropixels probes with 5120 recording sites (Project 3). We will combine these recordings with optogenetic manipulations of subcortex and modern multi-variate analysis methods (Data Science Core) to track how subcortical signals propagate through the thalamus into cortex. In the memory-guided response task we will probe the role of the substantia nigra reticulata, acting via the ventromedial nucleus, on maintenance of movement planning, and the impact of midbrain movement centers (e.g. pedunculopontine nucleus), acting via the posterior mediodorsal nucleus, on switching from movement planning to movement initiation (Aim 1). In the foraging task, we will probe how error prediction signals, for example from ventral pallidum (PALv), update memory-related activity in the mPFC via anterior mediodorsal thalamus. These measurements will test key model predictions and in turn inform multi-regional circuit models of cognitive behavior.