Cross-modal sensory interactions, processing, and representation in the Drosophila brain

强健的导航对动物的生存至关重要，它需要处理复杂的 跨越不同模式和时间尺度的感觉信息。与人类工程不同 系统，其中传感器是被动的和模块化的，通常是由 从本质上讲，生物传感器不断地相互作用和影响，行为 决策是在不同的时间尺度上做出的，有着不同的目标。此外，这样的决定是 基于主动收集的感官信息。我们团队的长期目标是阐明 来自感觉间相互作用和多感觉整合的整个神经回路动力学 中央大脑在不同的时间尺度上产生运动指令。我们用 果蝇利用遗传工具，丰富的行为，和 使用完整的脑部电子显微镜数据重建神经回路的可用工具。我们会 调查视觉信息、机械信息(风)和陀螺仪信息如何 (感应车身旋转)集成在一起，并用于生成电机命令。我们的目标是测试 以下假设：(1)来自一种通道的感觉信息影响加工 不同的感觉模式(例如，陀螺仪的感觉可以有效地驱动颈部肌肉 更改可视输入)。(2)来自多个传感器的信息在中央大脑中整合 使用吸引子动力学，将赢家通吃和卡尔曼滤波机制统一起来。(3) 中央大脑不仅整合感觉刺激，而且主动驱动感觉肌肉 最大限度地利用与任务相关的信息，增强感觉处理的健壮性。为了测试这些 假设，我们结合了我们团队的不同和互补的专业知识，包括两个- 光子钙成像，单光子肌肉成像，全细胞膜片钳，精确 感觉扰动、高速行为分析、空气动力学、单细胞分辨率 光发生微扰、计算建模、网络理论和控制理论。我们会 开发一个新的控制理论框架，由解剖学、行为学和 生理学实验。我们将使用行为和光遗传学来测试模型的预测 用摄动法进一步提炼理论。这一项目的成功完成将 使我们的团队处于一个理想的位置，以进一步研究多个感觉运动转换 具有不同时间尺度的路径、跨越反射反应(FAST)、避障 (中等)和自愿航行决定(慢)。总体而言，我们团队的目标是揭示 稳健导航背后的神经网络动力学计算原理。

Robust navigation, which is critical for an animal’s survival, requires the processing of complex sensory information spanning different modalities and time scales. Unlike human-engineered systems, where sensors are passive and modularized and decisions are typically made centrally, biological sensors constantly interact and influence each other, and behavioral decisions are made on different time scales with diverse goals. Further, such decisions are based on actively collected sensory information. Our team’s long-term goal is to elucidate the entire neural circuit dynamics from inter-sensory interaction and multisensory integration in the central brain to the generation of motor commands across different time scales. We use Drosophila melanogaster to take advantage of the genetic tools, rich behaviors, and the available tools to reconstruct the neural circuits with full brain electron microscopy data. We will investigate how visual information, mechanical information (wind), and gyroscopic information (sensing body rotation) are integrated and used to generate motor commands. We aim to test the following hypotheses: (1) Sensory information from one modality affects the processing of different sensory modalities (e.g., gyroscopic sensation may drive neck muscles to effectively change the visual input). (2) Information from multiple sensors is integrated in the central brain using attractor dynamics that unify winner-takes-all and Kalman filter mechanisms. (3) The central brain not only integrates sensory stimuli, but actively drives sensory muscles to maximize task-relevant information, increasing robustness of sensory processing. To test these hypotheses, we combine our team’s diverse and complementary expertise, including two- photon calcium imaging, one photon muscle imaging, whole-cell patch clamping, precise sensory perturbation, high speed behavioral analysis, aerodynamics, single-cell resolution optogenetic perturbation, computational modeling, network theory, and control theory. We will develop a new control theoretical framework supported by anatomical, behavioral, and physiological experiments. We will test predictions of models using behavioral and optogenetic perturbation methods to further refine the theory. The successful completion of this project will put our team in an ideal position to further investigate multiple sensorimotor transformation pathways with different time scales, spanning reflexive response (fast), obstacle avoidance (medium), and voluntary navigational decisions (slow). Overall, our team aims to reveal the computational principles of neural network dynamics underlying robust navigation.