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.

家长拨款通过RFA-NS-18-009作为大脑倡议奖授予。 这是NIH大脑倡议行政副刊的一份申请，以增强多样性。 导师和首席研究员：Mala Murthy，博士生：Edna Normand，医学博士/博士生 项目总结(来自Parent Grant)： 自然的感觉输入通常是复杂的，并且通常结合了多种形式。人类的语言，例如 例如，将听觉信号与视觉提示(如面部表情)相结合，以通知解释 所说的话。因为单个感官通路仅提供感官的部分表征 可获得的信息，选择对多模式刺激的上下文适当的行为反应通常 需要跨医疗机构集成信息。神经电路是如何实现这一基本功能的 计算？我们目前对感觉加工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，从感觉感受器开始工作到大脑。因此，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，向内工作，一个细胞类型一个细胞类型，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，专门专注于 从设计上讲，单一的感觉模式不能导致对指导行为的统一感知的洞察 可以从独立的感觉处理流中出现的信息中组合而成。在这里，我们利用 建立果蝇模型的全脑成像和先进计算方法 揭示支撑多感官整合的基本原理的系统。这项建议有三个方面 目标。首先，我们将在这个实验系统中优化全脑成像，并使用这项技术来 全面描述支持视觉、机械感觉的人口动态 和品味。第二，我们将系统地量化这些感觉模式和 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，我们将把种群动态与单个的响应特性联系起来 细胞类型，为表征电路和突触机制提供了一条强大的途径。合在一起，由 开发和应用改进的方法，用于大规模监测神经活动，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。