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

总结，项目4(确定皮层下信号影响的图式 额叶皮质动力学和认知行为) 这项研究项目集中在皮质下区域和额叶皮质之间的动态相互作用。 丘脑在灵活的行为中。额叶皮质，包括运动皮质和医学前额叶皮质，显示 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 与自愿行动相关的快速调节的活动。复杂的行为对应于 大脑皮层活动模式序列。额叶皮质与高级(无感觉)丘脑紧密相连 来调解他们的行为。丘脑继而接受来自基底节和其他皮质下的驱动输入 结构。我们检验了这样的假设，即不同的皮质下对特定丘脑区域的输入控制着不同的 大脑皮质活动的各个方面，包括设置维持短期记忆的时间段， 从运动规划到运动执行的过渡(目标1)，以及更新和维护 觅食过程中的特定动作。我们的建模框架(总体上，项目5)将动态系统链接在一起 用实际的多区域神经电路展望神经计算，并做出预测，可以 进行了神经生理学测试。 我们在小鼠身上使用了两种行为任务，这些任务涉及明确但不同的皮质活动模式。在一个 在记忆引导反应任务中，前外侧运动皮质(ALM)的神经元显示出准备活动， 它可以预测未来的具体动向。就在运动开始之前，准备活动崩溃在 偏爱推动运动的活动模式。在动态觅食任务中，内侧前额叶皮质中的神经元 (MPFC)显示出缓慢变化的活动模式，这些模式与一个动作相对于另一个动作的价值相关。 此活动基于新信息进行更新，例如奖励的大小。项目1-3提供资料 关于连接皮质下区域、丘脑和ALM/mPFC的回路。建立在这些电路映射的基础上 实验中，我们将从连接的皮层下部→丘脑→皮层回路进行同步录音 使用新的具有5120个记录点的多柄神经像素探头(项目3)。我们将把这些结合起来 皮层下的光遗传操作和现代多变量分析方法的记录(数据 科学核心)，以追踪皮质下信号如何通过丘脑传播到皮质。在记忆引导下 我们将探讨黑质网状核通过腹内侧核的作用。 维持运动计划，以及中脑运动中心的影响(例如桥小脑 核团)，通过后背内侧核起作用，从运动规划切换到运动 启蒙(目标1)。在觅食任务中，我们将探索错误预测信号，例如来自腹侧的错误预测信号 苍白球(PALv)，通过前内侧丘脑更新mPFC中的记忆相关活动。这些 测量将测试关键模型预测，进而为认知的多区域电路模型提供信息 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.