Top-down control of cortical visual sensory information processing in spatial navigation

对环境的行为适应对生存至关重要，依赖于选择性地整合相关感官信息的能力。这种能力依赖于神经网络通过经验改变的潜力；例如，通过学习特定感觉刺激与奖励的关联。最近的研究表明，初级视觉皮质(V1)的感觉加工是由高阶皮质区域的轴突反馈投射调节的。这些投影传达了编码环境中与任务相关的特征的预测信号。反馈调制产生于经验，被认为是根据任务需求来获取感觉信息。例如，研究表明，从高阶前扣带回(ACC)到V1的反馈传达了关于空间预期和自我运动的信息，这对空间导航非常重要。反馈如何整合到V1局部回路，以及它如何改变感觉处理，在很大程度上仍然难以捉摸。此外，在学习过程中这种背景反馈回路是如何建立的还是个未知数。被认为参与这一过程的一个关键结构是基底节：它们通过背内侧纹状体(DMS)接受来自V1和ACC的输入，并编码动作-结果偶发事件。本方案的目的是确定视觉输入和自主相关输入如何在V1建立整合，以编码对导航至关重要的空间信息。具体地说，来自ACC的上下文信息如何与V1中的可视信息集成？在学习奖赏导航任务的过程中，这个过程依赖于从V1到DMS的神经元投射吗？我将使用最先进的双光子钙成像和电生理记录，结合光遗传学和化学遗传学方法，以投射特有的方式记录和调节神经元的活动。该项目将围绕两个目标组织：1)描述学习奖赏视觉引导任务期间和之后从ACC到V1的功能输入的特征。2)确定V1投射到DMS和ACC对学习和执行奖赏视觉引导任务的因果贡献。通过揭示输入，在学习过程中，局部电路的计算以及V1神经元对DMS和ACC的输出，我将能够提出一个统一的模型，该模型能够捕捉行为训练如何改变新大脑皮层，以改善行为相关视觉对象的编码。

Behavioural adaptation to the environment is vital for survival and relies on the ability to selectively integrate relevant sensory information. This ability depends on the potential of neuronal networks to change through experience; for example, by learning the association of a specific sensory stimulus with a reward. Recent studies have demonstrated that sensory processing in the primary visual cortex (V1) is modulated by axonal feedback projections from higher-order cortical areas. These projections convey predictive signals encoding task-relevant features of the environment. Feedback modulation arises from experience and is thought to gate sensory information according to task demands. For example, it was shown that feedback from the higher-order anterior cingulate cortex (ACC) to V1 conveys information on spatial expectations and self-motion which is important for spatial navigation. It remains largely elusive how feedback is integrated into V1 local circuits and how it alters sensory processing. Further, it is unknown how this contextual feedback circuit is established during learning. A key structure thought to be involved in this process are the basal ganglia: They receive input from both V1 and ACC through the dorsomedial striatum (DMS) and encode action-outcome contingencies.The aim of this proposal is to determine how the integration of visual input with self-motion related input is established in V1 to encode spatial information that is crucial for navigation. Specifically, how is contextual information from ACC integrated with visual information in V1? Does this process depend on neuronal projections from V1 to DMS during the learning of a rewarded navigation task?I will use state-of-the-art two-photon Ca2+ imaging and electrophysiological recordings in combination with optogenetic and chemogenetic approaches to record and modulate neuronal activity in a projection-specific manner.The project will be organized around two aims:1) To characterize the functional inputs from ACC to V1 during and after learning a rewarded visually guided task.2) To determine the causal contribution of V1 projections to DMS and ACC to learning and performance of a rewarded visually guided task.By revealing the inputs, the local circuits computation and the output of V1 neurons to DMS and ACC during learning, I will be able to propose a unifying model that captures how behavioural training changes the neocortex to improve the encoding of behaviourally relevant visual objects.