Integrative circuit dissection in the behaving nonhuman primate

非人类灵长类动物的集成电路解剖

• 批准号: 10653435
• 负责人: Wyeth Daniel Bair
• 金额: $ 117.96万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653435
• 关键词: Agnosia; Anesthesia procedures; Animal Model; Animals; Area; Bayesian Analysis; Behavior; Behavior Control; Behavioral; Brain; Brain Diseases; Brain region; Chronic; Cognitive; Collaborations; Communication; Complex; Computer Models; Decision Making; Detection; Dissection; Distant; Electrodes; Ensure; Environment; Ethology; Functional disorder; Future; Goals; Histologic; Histology; Image; Impairment; Knowledge; Label; Lasers; Lead; Lentivirus; Macaca; Methodology; Methods; Modeling; Monkeys; Morphologic artifacts; Motion; Motor; Neurons; Neurosciences; Opsin; Pathway interactions; Perception; Photons; Physiological; Population; Population Dynamics; Poverty; Prefrontal Cortex; Primates; Process; Protocols documentation; Pulvinar structure; Scanning; Science; Sensory; Shapes; Signal Transduction; Specific qualifier value; Stimulus; Structure; System; Task Performances; Techniques; Testing; Toxic effect; Training; Viral; Vision; Visual; Visual Cortex; Visualization; area V4; autism spectrum disorder; awake; calcium indicator; density; executive function; experience; frontal lobe; head-to-head comparison; in vivo; insight; multiphoton imaging; neurophysiology; nonhuman primate; object recognition; optogenetics; response; retinal imaging; sensory integration; technique development; tool; visual processing

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.

在自然视觉中，基于视网膜图像识别物体具有挑战性，并且通常是一个挑战。 不适定问题，因为单个图像与多种解释兼容。 然而，灵长类动物的大脑有一种非凡的能力，可以理解模糊的场景， 解决困难的物体识别问题。越来越多的证据表明， 特别是在具有挑战性的环境中-例如，遮挡或低可见度环境-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 在细胞水平上绘制电路图，说明区域间的相互作用如何支持 将与视觉图像相关的感觉信号与代表 先验知识，从而揭示了场景理解的基础计算，对象 认知和知觉决策的能力。为了实现这一目标，我们 组建了一个协同增效的专家小组，汇集了（i）基于病毒的电路跟踪， 识别连接神经元的光遗传学方法;（ii）多光子成像和高密度 电极记录以功能性地表征清醒时的神经元和信号基序 猕猴;（iii）行为操纵和（iv）尖端计算建模， 揭示了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 知觉过程我们的建议包括四个项目。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，可靠和长期的跟踪， 连接的神经元。我们将在组织学上比较慢病毒和 AAVretro结构的有效性、毒性、方向可靠性、分层和扩散 在标记连接的神经元中，我们将使用高密度 神经生理学在项目2和3中，PI Bair将领导实施多光子成像 在清醒的猴子中，在同时的生理过程中， 图1示出了在皮层中向下至约Imm深度的100个神经元的表征。在项目4中，PI Pasupathy将领导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用于研究视皮层（区域）神经元 V4），前额叶皮层和视觉枕作为猕猴执行形状检测， 贫困的形象。吴丕将领导这项工作， 通信子空间模型的上下文，并揭示如何连接三个大脑中的神经元 区域是感觉信号和先验知识的多路复用的基础， 检测和场景理解。