Integrative circuit dissection in the behaving nonhuman primate

在自然视觉中，基于视网膜图像识别物体是具有挑战性的，并且通常是一种 这是一个不适定的问题，因为一幅图像可以兼容多种解释。 然而，灵长类动物的大脑有非凡的能力理解模棱两可的场景和 解决疑难目标识别问题。越来越多的证据表明，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 将感官信息与根据经验建立的先验知识相结合。我们的目标是 在蜂窝级别开发电路图，指定区域间交互如何支持 将与视觉图像相关的感觉信号与表示 先验知识，从而揭示作为场景理解基础的计算、对象 灵长类动物大脑中的识别和知觉决策。为了实现这一目标，我们有 组建了一个专家协同团队，将(I)基于病毒的电路跟踪和 识别连接神经元的光遗传学方法；(Ii)多光子成像和高密度 电极记录对清醒状态下神经元和信号模体的功能表征 猕猴；(Iii)行为操作和(Iv)尖端计算建模 揭示跨大脑区域的连接神经元系统如何相互作用并支持复杂的 感知过程。我们的提案包括四个项目。在项目1中，Pi Briggs将领导一个 努力建立电路跟踪协议，以支持密集、可靠和长期的 猕猴体内相互连接的神经元。我们将在组织学上比较慢病毒和 AAVretro构建的有效性、毒性、定向可靠性、分层和传播性 在标记连接的神经元时，我们将使用高密度测试光标记 神经生理学。在项目2和项目3中，皮贝尔将领导实施多光子成像的工作 在清醒的猴子体内识别投射神经元的同步生理学过程 对皮质中深达1毫米的100个神经元进行特征分析。在项目4中，交点 帕苏法将引领应用病毒方法和生理特征的努力 高密度神经像素探针和多光子成像研究视皮层(区)神经元 V4)、前额叶皮质和视觉枕，猕猴在大脑中进行形状检测 贫乏的形象。皮乌会负责解释香港的人口动态。 背景通信子空间模型和揭示三个大脑中如何连接的神经元 区域是感觉信号和先验知识的多路复用的基础，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.