Integrative circuit dissection in the behaving nonhuman primate

非人类灵长类动物的集成电路解剖

基本信息

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
项目类别：
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

项目摘要

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，识别基于视网膜图像的物体具有挑战性，通常是不适的问题是因为单个图像与多种解释兼容。然而，主要的大脑具有明显的理解模棱两可的场景和解决困难的对象识别问题。融合的证据表明这个过程，尤其是在挑战环境（例如，遮挡或低可见性环境）基于感官信息与经验构建的先验知识的集成。我们的目标是开发电路图，在蜂窝级别，以指定美联社之间的相互作用如何支持与代表的内部模型相关的感官信号的集成先验知识，从而揭示了场景理解的计算，对象识别和质感大脑中的知觉决策。为了实现这一目标，我们有组建了一个协同的专家团队来汇集在一起，（i）基于病毒的电路跟踪和鉴定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表征神经元和信号基序猕猴；（iii）行为操纵和（iv）尖端计算建模揭示跨大脑区域的连接神经元的系统如何相互作用和支持复合物感知过程。我们的建议包括四个项目。在项目1中，Pi Briggs将领导努力建立电路跟踪协议，以支持密集，可靠和长期跟踪猕猴中的神经元连接。我们将在组织学上比较慢病毒和 Aavretro的效率，毒性，定向可靠性，分层和传播的构造在标记连接的神经元时，我们将使用高密度测试选择神经生理学。在项目2＆3中，Pi Bair将领导实施多光子成像的努力在清醒的猴子中，在同时生理期间识别体内的投射神经元在皮质中，100s神经元的表征下降到约1毫米的深度。在项目4中，PI Pasupathy将领导将病毒方法和身体表征应用的努力高密度神经质子问题和多光子成像研究视觉皮层中的神经元（区域） v4），前额叶皮层和视觉pulvinar，猕猴在贫穷的图像。 Pi Wu将领导解释人口动态的努力通信子空间模型的背景，并揭示了三个大脑中的神经元如何连接感官信号和先验知识的多路复用的区域是促进对象的检测和场景理解。