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: 10242189
• 负责人: Thomas Robert Clandinin
• 金额: $ 109.05万
• 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/10242189
• 关键词: Address; Algorithms; Animals; Architecture; Atlases; Auditory; Behavior; Behavioral; Biological Models; Brain; Brain imaging; Code; Collaborations; Complex; Computer Models; 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; 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; motor behavior; 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.

项目概要： 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： 单一感觉模式，从感觉受体开始进入大脑。结果，我们有一个 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，逐个细胞类型，已经离开了我们对电路和计算原理的理解 感觉与行动的联系不完整。此外，只关注单一的实验策略 从设计上来说，感官模式无法深入了解指导行为的统一知觉是如何形成的。 由单独的感官处理流中出现的信息组装而成。在这里我们利用全脑 成像和先进的计算方法将果蝇建立为模型系统 揭示支撑多感官整合的基本原理。 该提案有三个目标。首先，我们将在这个实验系统中优化全脑成像，并使用 这项技术可以全面表征支撑视觉的人口动态， 机械感觉和味觉。其次，我们将系统地量化这些电路之间的相互作用 感觉模式和跨动物变异性，测试统计推断的计算模型，以及 确定多模式集成的算法基础。第三，我们将人口动态与 单细胞类型的响应特性，为表征电路和突触提供了强大的途径 机制。总之，通过开发和应用大规模监测的改进方法 神经活动，结合计算建模和定量分析，这个项目将大大扩展 我们对大脑感觉处理机制的理解。