Population Neural Activity Mediating Sensory Perception Across Modalities

项目摘要： 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的基础上： 单一的感觉方式，从感觉受体开始进入大脑。因此，我们有一个 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，在这方面， 一个细胞一个细胞地向内工作，已经让我们对电路和计算原理的理解 将感觉和不完整的动作联系起来此外，实验策略，专门侧重于单一的 通过设计，感官形式不能导致对指导行为的统一感知如何被理解的洞察力。 由独立的感官处理流中出现的信息组合而成。在这里，我们利用全脑 成像和先进的计算方法来建立果蝇作为一个模型系统， 揭示多感官整合的基本原理。 这项建议有三个目标。首先，我们将在这个实验系统中优化全脑成像，并使用 这项技术可以全面描述支撑视觉感觉的人口动态， 机械感觉和味觉。其次，我们将系统地量化这些之间的电路相互作用， 感觉形态和跨动物变异性，测试统计推断的计算模型，以及 识别多模态整合的算法基础。第三，我们将把人口动态与 单细胞类型的响应特性，提供了一个强大的路径来表征电路和突触 机制等总之，通过开发和应用改进的方法， 神经活动，结合计算建模和定量分析，这个项目将大大扩大 我们对大脑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.