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: 10310712
• 负责人: Thomas Robert Clandinin
• 金额: $ 9.49万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10310712
• 关键词: Administrative Supplement; Algorithms; Animals; Auditory; Award; BRAIN initiative; Behavior; Biological Models; Brain; Brain imaging; Complex; Computer Models; Cues; Disease; Doctor of Philosophy; Drosophila genus; Environment; Esthesia; Facial Expression; Goals; Human; Impairment; Individual; Lead; Left; Link; Mediating; Mentors; Methods; Modality; Monitor; Parkinson Disease; Pathway interactions; Perception; Peripheral; Population; Population Dynamics; Principal Investigator; Property; Sensory; Sensory Receptors; Signal Transduction; Speech; Statistical Models; Stimulus; Stream; Synapses; System; Taste Perception; Technology; Testing; United States National Institutes of Health; Vision; Visual; autism spectrum disorder; base; behavioral response; cell type; design; improved; insight; multimodality; multisensory; neural circuit; parent grant; relating to nervous system; response; sensory input

The parent grant was awarded as a BRAIN Initiative award via RFA-NS-18-009. This is an application for NIH BRAIN Initiative Administrative Supplement to enhance diversity. Mentor and Principal Investigator: Mala Murthy, PhD Candidate: Edna Normand, MD/PhD Candidate Project Summary (from Parent Grant): 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.

家长补助金通过 RFA-NS-18-009 被授予 BRAIN Initiative 奖。 这是 NIH BRAIN Initiative 行政补充的应用程序，旨在增强多样性。 导师兼首席研究员：Mala Murthy，博士生：Edna Normand，医学博士/博士生 项目摘要（来自家长资助）： 自然感官输入通常很复杂，并且通常结合多种模式。人类的言语，对于 例如，将听觉信号与视觉线索（例如面部表情）结合起来，为解释提供信息 所说的话。由于各个感觉通路仅提供感觉的部分表征 信息可用，通常选择对多模式刺激的适合上下文的行为反应 需要跨模式整合信息。神经回路如何执行这一基本功能 计算？我们目前对感觉处理的理解主要建立在以下研究的基础上： 专注于单一感觉模式，从感觉受体开始作用于大脑。结果，我们 对许多不同实验环境中的外围电路计算有深入的了解。 然而，逐个细胞类型的向内研究，已经离开了我们对电路和计算的理解。 将感觉与行动联系起来的原则是不完整的。此外，实验策略专门关注 从设计上来说，单一的感官模式无法深入了解统一的感知如何指导行为 可以根据单独的感官处理流中出现的信息进行组装。在这里我们利用 全脑成像和先进的计算方法建立果蝇模型 揭示支持多感官整合的基本原理的系统。这个提案有三点 目标。首先，我们将在这个实验系统中优化全脑成像，并利用该技术 全面表征支撑视觉、机械感觉的群体动态 和味道。其次，我们将系统地量化这些感觉方式和 跨动物变异性，测试统计推断的计算模型，并确定算法 多式联运一体化的基础。第三，我们将种群动态与单个个体的响应特性联系起来。 细胞类型，提供了表征电路和突触机制的强大途径。综合起来，由 开发和应用大规模神经活动监测的改进方法，结合 计算建模和定量分析，这个项目将极大地扩展我们对感官的理解 整个大脑的处理机制。