Population Neural Activity Mediating Sensory Perception Across Modalities

群体神经活动介导跨模态的感官知觉

• 批准号: 9789712
• 负责人: Thomas Robert Clandinin
• 金额: $ 100.48万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789712
• 关键词: Address; Algorithms; Animals; Architecture; Atlases; Auditory; Behavior; Behavioral; Biological Models; Brain; Brain imaging; Code; Collaborations; Complex; Computer Simulation; Courtship; Cues; Data; Data Analyses; Data Set; Disease; Drosophila genus; Environment; Esthesia; Facial Expression; Future; Genetic; Goals; Human; Image; Impairment; Individual; Lead; Left; Link; Locomotion; Maps; Measures; Mediating; Methods; Modality; Modeling; Monitor; Motor; Neurons; Neurosciences; Parkinson Disease; Pathway interactions; Perception; Peripheral; Population; Population Dynamics; Property; Research Personnel; Resolution; Sampling; Sensory; Sensory Receptors; Shapes; Signal Transduction; Speech; Statistical Models; Stereotyping; Stimulus; Stream; Synapses; System; Taste Perception; Technology; Testing; Vision; Visual; Work; autism spectrum disorder; base; behavior measurement; behavioral response; cell type; design; experience; experimental study; fly; improved; in vivo two-photon imaging; insight; integration site; multimodality; multisensory; neural circuit; neural model; relating to nervous system; response; sensory input; sensory integration; sensory stimulus; statistics; theories; tool

Project Summary: Natural sensory inputs are typically complex, and often combine multiple modalities. Human speech, for example, combines auditory signals with visual cues, such as facial expressions, that inform the interpretation of the spoken words. As individual sensory pathways only provide a partial representation of the sensory information available, selecting the context-appropriate behavioral response to a multimodal stimulus often requires integrating information across modalities. How do neural circuits perform this fundamental computation? Our current understanding of sensory processing is predominantly built upon studies that have focused on single sensory modalities, working into the brain beginning from sensory receptors. As a result, we have a deep understanding of peripheral circuit computations in many different experimental contexts. However, working inward, cell-type by cell-type, has left our understanding of the circuits and computational principles that link sensation to action incomplete. Moreover, experimental strategies that focus exclusively on single sensory modalities cannot, by design, lead to insights into how the unified percepts that guide behavior can be assembled from information emerging in separate sensory processing streams. Here we leverage whole-brain imaging and advanced computational approaches to establish the fruit fly Drosophila as a model system for uncovering fundamental principles underpinning multisensory integration. This proposal has three goals. First, we will optimize whole-brain imaging in this experimental system, and use this technology to comprehensively characterize population dynamics underpinning the sensations of vision, mechanosensation and taste. Second, we will systematically quantify circuit interactions between these sensory modalities and across-animal variability, testing computational models of statistical inference, and identifying the algorithmic bases of multimodal integration. Third, we will link population dynamics to the response properties of single cell-types, providing a powerful path to characterizing circuit and synaptic mechanisms. Taken together, by developing and applying improved methods for large-scale monitoring of neural activity, combined with computational modeling and quantitative analysis, this project will greatly expand our understanding of sensory processing mechanisms across the brain.

项目摘要： 天然感觉输入通常很复杂，并且通常结合了多种方式。人类的演讲 例如，将听觉信号与视觉提示（例如面部表情）结合起来，以告知解释 口语。由于单个感觉途径仅提供感觉的部分表示 可用的信息，通常选择适合上下文的行为响应对多模式刺激 需要跨模式集成信息。神经电路如何执行此基本 计算？ 我们目前对感官处理的理解主要是基于关注的研究 单个感觉方式，从感觉受体开始进入大脑。结果，我们有一个 在许多不同的实验环境中对外围电路计算的深入了解。然而， 通过细胞类型的细胞类型向内工作，使我们对电路和计算原理的理解 与动作不完整的链接感觉。此外，专注于单个的实验策略 通过设计，感官方式不能导致对指导行为的统一感知的见解 从单独的感官处理流中出现的信息组装。在这里，我们利用全脑 成像和先进的计算方法，以建立果蝇果蝇作为模型系统 揭示基本原则的基础。 该提议有三个目标。首先，我们将在此实验系统中优化全脑成像，并使用 这项技术是为了全面表征人口动态，这是视觉感觉的基础， 机械敏和口味。其次，我们将系统地量化这些电路相互作用 感觉方式和跨动物变异性，测试统计推断的计算模型，以及 识别多模式积分的算法碱基。第三，我们将将人口动态链接到 单细胞类型的响应特性，为表征电路和突触提供了强大的路径 机制。共同开发和应用改进的方法来大规模监控 神经活动，结合计算建模和定量分析，该项目将大大扩展 我们对整个大脑的感觉处理机制的理解。