Population Neural Activity Mediating Sensory Perception Across Modalities

父母补助金通过RFA-NS-18-009作为BRAIN倡议奖授予。 这是NIH BRAIN Initiative Administration Supplement的应用程序，以增强多样性。 导师和主要研究者：Mala Murthy，博士候选人：Edna Normand，MD/PhD候选人 项目摘要（来自父母补助金）： 自然感觉输入通常是复杂的，并且通常联合收割机组合多种形式。人类的语言，因为 例如，将听觉信号与视觉线索（如面部表情）结合起来，为解释提供信息 的话语。由于单个感觉通路仅提供感觉的部分表征， 信息可用，选择上下文适当的行为反应，多模态刺激往往 需要整合各种模式的信息。神经回路如何执行这个基本的 计算？我们目前对感觉加工的理解主要建立在以下研究基础上： 专注于单一的感觉方式，从感觉受体开始进入大脑。结果我们 在许多不同的实验环境中对外围电路计算有深刻的理解。 然而，向内工作，细胞类型的细胞类型，已经离开了我们对电路和计算的理解。 将感觉与不完整动作联系起来的原则。此外，实验策略，专门侧重于 单一的感觉形式，通过设计，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。在这里，我们利用 全脑成像和先进的计算方法建立果蝇作为模型 揭示多感官整合的基本原理的系统。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，并使用该技术来 全面表征支撑视觉、机械感觉 和味道。其次，我们将系统地量化这些感觉方式之间的电路相互作用， 跨动物变异性，测试统计推断的计算模型，并确定算法 多式联运的基础。第三，我们将把种群动力学与单个 细胞类型，提供了一个强大的路径来表征电路和突触机制。总的来说， 开发和应用大规模监测神经活动的改进方法， 计算建模和定量分析，这个项目将大大扩展我们对感官的理解， 大脑的处理机制。

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.