Integrative circuit dissection in the behaving nonhuman primate

在自然视觉中，根据视网膜图像识别物体具有挑战性，并且通常是一个难题。 不适定问题，因为单个图像可以与多种解释兼容。 尽管如此，灵长类动物的大脑具有非凡的能力来理解模糊的场景和 解决困难的物体识别问题。汇聚的证据表明，这个过程， 特别是在具有挑战性的环境中——例如遮挡或低能见度环境——基于 将感官信息与从经验中建立的先验知识相结合。我们的目标是 开发细胞级别的电路图，指定区域间相互作用如何支持 将与视觉图像相关的感官信号与表示的内部模型相集成 先验知识，从而揭示场景理解、对象基础的计算 灵长类动物大脑中的识别和感知决策。为了实现这一目标，我们有 组建了一个协同专家团队，将（i）基于病毒的电路追踪和 识别连接神经元的光遗传学方法； (ii) 多光子成像和高密度 电极记录可在清醒状态下对神经元和信号基序进行功能表征 猕猴； (iii) 行为操纵和 (iv) 尖端计算模型 揭示跨大脑区域的连接神经元系统如何相互作用和支持复杂的 知觉过程。我们的提案包括四个项目。在项目 1 中，PI Briggs 将领导 努力建立电路追踪协议，以支持密集、可靠和长期的追踪 猕猴体内的连接神经元。我们将在组织学上比较慢病毒和 AAVretro 从功效、毒性、方向可靠性、分层和传播角度构建 标记连接的神经元，我们将使用高密度测试光标记 神经生理学。在项目 2 和 3 中，PI Bair 将领导实现多光子成像 在清醒的猴子中识别同步生理过程中体内的投射神经元 表征皮层中 1 毫米深度的 100 个神经元。在项目4中，PI Pasupathy 将引领病毒方法和生理学特征的应用 高密度神经像素探针和多光子成像来研究视觉皮层（区域）中的神经元 V4）、前额叶皮层和视枕，猕猴执行形状检测 贫穷的图像。 PI Wu 将领导解释人口动态的工作 通信子空间模型的上下文并揭示三个大脑中的神经元如何连接 区域是感觉信号和先验知识复用的基础，以促进对象 检测和场景理解。

In natural vision, recognizing objects based on the retinal image is challenging and is often an ill-posed problem because a single image is compatible with multiple interpretations. Nevertheless, the primate brain has a remarkable ability to understand ambiguous scenes and solve difficult object recognition problems. Converging evidence suggests that this process, especially in challenging contexts—e.g., occlusion or low-visibility environments—is based on the integration of sensory information with prior knowledge built from experience. Our goal is to develop circuit diagrams at a cellular level that specify how inter-areal interactions support the integration of sensory signals related to the visual image with internal models that represent prior knowledge, thereby revealing the computations that underlie scene understanding, object recognition, and perceptual decision making in the primate brain. To achieve this goal, we have assembled a synergistic team of experts to bring together, (i) viral-based circuit tracing and optogenetic methods to identify connected neurons; (ii) multiphoton imaging and high-density electrode recordings to functionally characterize neurons and signaling motifs in the awake macaque monkey; (iii) behavioral manipulations and (iv) cutting-edge computational modeling to reveal how systems of connected neurons across brain regions interact and support complex perceptual processes. Our proposal includes four projects. In Project 1, PI Briggs will lead an effort to establish circuit tracing protocols to support dense, reliable, and long-term tracking of connected neurons in the macaque monkey. We will histologically compare lentivirus and AAVretro constructs in terms of their efficacy, toxicity, directional reliability, layering, and spread in labeling connected neurons, and we will test opto-tagging using high density neurophysiology. In Projects 2 & 3, PI Bair will lead the effort to implement multiphoton imaging in the awake monkey to identify projecting neurons in vivo during the simultaneous physiological characterization of 100s of neurons down to a depth of ~1 mm in cortex. In Project 4, PI Pasupathy will lead the effort to apply the viral methods and physiological characterization with high-density neuropixels probes and multiphoton imaging to study neurons in visual cortex (area V4), prefrontal cortex and the visual pulvinar as macaque monkeys perform shape detection in impoverished images. PI Wu will lead the effort to interpret the population dynamics in the context of communication subspace models and reveal how connected neurons in three brain regions underlie the multiplexing of sensory signals and prior knowledge to facilitate object detection and scene understanding.