The role of the cerebello-parietal pathway in state estimation

小脑顶叶通路在状态估计中的作用

• 批准号: 464398405
• 负责人: Professor Dr. Cornelius Schwarz
• 金额: --
• 依托单位: Werner Reichardt-Centrum für integrative Neurowissenschaften (CIN)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/464398405?language=en
• 关键词: role; cerebello; parietal; pathway; state

A dominant theory in the field of sensorimotor integration is that the sensory signal flow is integrated with internal predictions about sensory consequences of ongoing movements and other contextual parameters. Such predictions are thought to be generated by an internal model based on an efference copy and other contextual signals. The site of the internal model is assumed to be the cerebellum. The optimal integration of predictive signals with sensory feedback to yield the sensory prediction error has been called state estimation. Although state estimation is a classical concept, its site of computation is not known. One problem has been the anatomical complexities of cerebellar output pathways. Another one is that state estimation may not be unique to cerebellar circuits. For instance, mammalian neocortex is almost certain to provide predicative signals as well. Such non-cerebellar predictive signals may realize different types of behavioral control, perhaps playing out on different time scales and/or related to different learning systems. Little is known about the interplay of different types of predictive signals. In this project, we will focus on cerebellar state estimation, which is most directly related to movement generation. The outputs of the cerebellum massively reach the sensorimotor cortex and we will systematically disentangle cerebellar predictive signals from assumed neocortical ones. We will track prediction signals on the complex cerebello-parietal pathway using multi-neuron extracellular electrophysiology, identify their cerebellar origin using causal, optogenetic analysis, and study their interplay with known parietal predictive signals of presumed cortical provenience. We established a novel behavioral tool to realize such analysis in extensive preliminary work. It translates a classic approach, the opening of the reafferent loop pioneered by Curtis Bell working in weakly electric fish in the 1980ies, to the use in behaviorally trained mice that perform defined whisker movements. This analysis will allow us to disentangle sensory prediction, and state estimation signals and localize them within the branched output stream of signals originating in the cerebellum and projecting to the neocortex. Starting from the cerebellar output we will parse the signals in motor thalamus, sensorimotor higher order thalamus and tactile thalamus. Finally we will monitor signals in primary/secondary somatosensory cortex and neighboring posterior parietal area.

感觉运动整合领域的一个主导理论是，感觉信号流与对正在进行的运动和其他环境参数的感觉后果的内部预测相结合。这样的预测被认为是由一个基于参考副本和其他上下文信号的内部模型产生的。内部模型的位置假定是小脑。将预测信号与感官反馈的最优集成以产生感官预测误差称为状态估计。状态估计是一个经典的概念，但它的计算位置是未知的。一个问题是小脑输出通路的解剖学复杂性。另一个是状态估计可能不是小脑回路所独有的。例如，哺乳动物的新皮层几乎肯定也能提供预测信号。这种非小脑预测信号可能实现不同类型的行为控制，可能在不同的时间尺度上发挥作用和/或与不同的学习系统有关。人们对不同类型的预测信号之间的相互作用知之甚少。在这个项目中，我们将专注于小脑状态估计，这与运动生成最直接相关。小脑的输出大量到达感觉运动皮层，我们将系统地从假设的新皮层信号中分离小脑预测信号。我们将利用多神经元细胞外电生理学追踪复杂的小脑-顶叶通路上的预测信号，利用因果光遗传学分析确定它们的小脑起源，并研究它们与假定的皮层起源的已知顶叶预测信号的相互作用。我们在广泛的前期工作中建立了一种新的行为工具来实现这种分析。它将柯蒂斯·贝尔（Curtis Bell）在上世纪80年代在弱电鱼身上首创的打开再传入回路（reaffent loop）的经典方法，应用于经过行为训练的老鼠身上，让它们完成明确的胡须运动。这种分析将使我们能够解开感觉预测和状态估计信号，并将它们定位在源自小脑并投射到新皮层的分支输出流信号中。从小脑输出开始，我们将分析运动丘脑、感觉运动高阶丘脑和触觉丘脑的信号。最后，我们将监测初级/次级体感觉皮层和邻近的后顶叶区的信号。