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

果蝇大脑中的跨模式感觉交互、处理和表征

• 批准号: 10645611
• 负责人: Itai Cohen
• 金额: $ 253.85万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645611
• 关键词: Affect; Air Movements; Anatomy; Animals; Behavior; Behavioral; Biological; Biological Models; Biosensor; Brain; Calcium; Cells; Cerebellar Ataxia; Collaborations; Complex; Computer Models; Computer software; Conflict (Psychology); Darkness; Data; Decision Making; Development; Disease; Drosophila genus; Drosophila melanogaster; Electron Microscopy; Environment; Environmental Wind; Esthesia; Eye; Flying body movement; Generations; Genetic; Goals; Hand; Human; Human Engineering; Image; Impairment; Magnetism; Mammals; Mechanics; Methods; Microscope; Modality; Modeling; Monitor; Motion; Motor; Motor output; Muscle; Nerve; Nervous System; Neural Pathways; Neurons; Organ; Pathway interactions; Photons; Physiological; Positioning Attribute; Process; Reflex action; Resolution; Rotation; Seminal; Sensory; Skeletal muscle structure of neck; Speed; Stimulus; System; Systems Theory; Technology; Testing; Time; Visual; behavioral response; body sense; cell type; connectome; control theory; dynamic system; experimental study; fly; imaging study; innovation; miniaturize; multimodality; multisensory; neural; neural circuit; neural network; neuromechanism; neuromuscular; optogenetics; patch clamp; predictive modeling; reconstruction; resilience; response; sensor; sensory feedback; sensory integration; sensory stimulus; sensory system; theories; tool; two-photon; visual information

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.

鲁棒导航对于动物的生存至关重要，需要处理复杂的 感官信息跨越不同的形式和时间尺度。不像人类工程 传感器是被动的和模块化的系统， 在中心，生物传感器不断相互作用和影响，并且行为 决策是在不同的时间尺度上，针对不同的目标作出的。此外，这些决定是 基于主动收集的感官信息。我们团队的长期目标是阐明 整个神经回路动力学从感官间的相互作用和多感官整合， 在不同的时间尺度上产生运动指令。我们使用 果蝇利用遗传工具，丰富的行为， 利用全脑电子显微镜数据重建神经回路的可用工具。我们将 研究视觉信息、机械信息（风）和陀螺仪信息 （感测身体旋转）被集成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对 不同的感觉方式（例如，陀螺感可以驱使颈部肌肉有效地 改变视觉输入）。(2)来自多个传感器的信息被整合到中央大脑中 使用统一赢家通吃和卡尔曼滤波器机制的吸引子动态。(3)的 中枢脑不仅整合感觉刺激，而且主动驱动感觉肌肉， 最大化任务相关信息，增加感官处理的鲁棒性。测试这些 我们联合收割机结合了我们团队多样化和互补的专业知识，包括两个- 光子钙成像，单光子肌肉成像，全细胞膜片钳，精确 感官扰动，高速行为分析，空气动力学，单细胞分辨率 光遗传学微扰、计算建模、网络理论和控制理论。我们将 发展一个新的控制理论框架，由解剖学，行为学和 生理实验我们将使用行为和光遗传学来测试模型的预测， 微扰方法，以进一步完善理论。该项目的成功完成将 使我们的团队处于一个理想的位置，以进一步研究多种感觉运动转换 不同时间尺度的路径，跨越反射反应（快速），避障 （中等）和自愿导航决定（缓慢）。总的来说，我们的团队旨在揭示 神经网络动力学的计算原理基础鲁棒导航。