Population Neural Activity Mediating Sensory Perception Across Modalities

项目总结： 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 单一感觉模式，从感觉感受器开始进入大脑。因此，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，一个细胞类型一个细胞类型，留下了我们对电路和计算原理的理解 这种感觉与行动之间的联系并不完整。此外，只关注单身人士的试验性策略 通过设计，感官形式不能引导人们深入了解指导行为的统一感知是如何 从独立的感觉处理流中出现的信息组合而成。在这里，我们利用全脑 建立果蝇模型系统的成像和先进计算方法 揭示支撑多感官整合的基本原理。 这项提议有三个目标。首先，我们将在这个实验系统中优化全脑成像，并使用 这项技术全面描述了支撑视觉的人口动态， 机械感官和味觉。第二，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 确定多通道集成的算法基础。第三，我们将把人口动态与 单细胞类型的响应特性，为表征电路和突触提供了一条强有力的途径 机械装置。总而言之，通过开发和应用改进的方法来大规模监测 神经活动，结合计算建模和定量分析，这个项目将大大扩展 我们对整个大脑的感觉处理机制的理解。

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.