Odor trail tracking: a new paradigm to unveil algorithms and neural circuits underlying active sensation and continuous decision making

气味踪迹追踪：揭示主动感觉和持续决策背后的算法和神经回路的新范例

• 批准号: 10524245
• 负责人: Catherine Dulac
• 金额: $ 273.03万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524245
• 关键词: Algorithms; Animals; Area; Behavior; Behavioral; Bilateral; Brain; Brain Diseases; Brain region; COVID-19 pandemic; Complex; Coupling; Cues; Custom; Data; Decision Making; Deposition; Environment; Esthesia; Exhibits; Face; Feedback; Genetic; Head; Knowledge; Laboratories; Life; Light; Measurement; Memory; Modeling; Molecular; Motor; Movement; Mus; Neurons; Neurosciences; Odors; Paper; Pattern; Physiological; Positioning Attribute; Process; Research; Research Personnel; Rodent; Sampling; Science; Sensory; Short-Term Memory; Smell Perception; Structure; System; Testing; Theoretical Studies; Update; Viral; analytical method; base; cell type; driving behavior; experience; experimental study; information processing; insight; multimodality; neural circuit; novel; public health relevance; relating to nervous system; transcriptomics; treadmill; virtual reality; way finding

Summary Animals actively sample sensory information, which they combine with prior knowledge to make decisions in a sensorimotor feedback loop. Aspects of this complex loop are often studied in isolation, using trial structures and in simplified conditions such as head-restrained animals in virtual reality. Studying an ethologically relevant, natural behavior in the laboratory can offer deeper insights about the behavioral strategies and their mechanistic neural implementation. Odor trail tracking is one such behavior, observed in many terrestrial animals including mice, and involves continuous re-orientation along the trail. The acquisition of odor cues is heavily guided by active sampling via sniffing and body movements, which introduces a strong coupling between sensation and motor actions. Theoretical studies hint at multi-modal strategies based on bilateral sampling, temporal integration and the use of internal models, whose relative contributions remain unclear. Here, a team of three PIs with complementary expertise, proposes to dissect the algorithmic and neural basis of olfactory trail tracking, which can offer deeper insights into active sensation, spatial navigation and continuous decision making. Using behavioral, physiological, molecular and analytical methods, the PIs will test algorithmic hypotheses and identify neural circuits guided by the following aims. In Aim 1, they will investigate the strategies exhibited by mice during trail tracking and identify brain regions supporting this behavior. A high-throughput adaptive system will be used to characterize the behavior of mice while tracking odor trails in a custom-built treadmill. In Aim 2, the PIs will uncover the neural circuits and cell types in brain regions involved in trail tracking. They will use cell-type targeted measurement of neural activity, viral tracing and transcriptomics in olfactory cortical areas to uncover patterns of activity and neural connectivity supporting neural computations necessary for trail tracking. In Aim 3, the PIs will elucidate, theoretically and computationally, behavioral strategies that mice use to track odor trails, and their underlying neural algorithms. They will use experimental data of Aim 1 to assess the validity of a novel theoretical framework, specifically in the context of sector search strategies and bilateral processing by rodents. Experimental data of Aim 2 will be used to unveil the neural dynamics and connectivity of sub-circuits that implement the algorithms driving behavior.

概括 动物积极采样感官信息，他们将其与先验知识结合在一起 感觉运动反馈循环中的决策。这个复杂循环的各个方面经常在 隔离，使用试验结构和简化条件，例如头部缠绕的动物 虚拟现实。研究实验室中与伦理学相关的自然行为可以提供 关于行为策略及其机械神经实施的更深入的见解。 气味径迹跟踪就是这样一种行为，在许多陆地动物中都观察到，包括小鼠， 并涉及沿着小径的连续重新定位。气味提示的获取很大 通过通过嗅探和身体运动进行主动采样的指导，这引入了较强的耦合 在感觉和运动动作之间。理论研究暗示了基于多模式策略 关于双边抽样，时间整合和内部模型的使用 贡献尚不清楚。在这里，由三个具有互补专业知识的PI团队组成 为了剖析嗅觉跟踪的算法和神经基础，可以更深入地提供 深入了解主动感觉，空间导航和持续决策。使用 行为，生理，分子和分析方法，PI将测试算法 假设并确定以以下目的为指导的神经回路。在AIM 1中，他们将 调查小鼠在跟踪过程中的策略并识别大脑区域 支持这种行为。高通量自适应系统将用于表征 在定制的跑步机中跟踪气味痕迹时，小鼠的行为。在AIM 2中，PI将 发现涉及跟踪跟踪的大脑区域中的神经回路和细胞类型。他们会使用 细胞类型针对嗅觉的神经活性，病毒追踪和转录组学的测量 揭示活动模式和神经连通性的皮质区域支持神经 跟踪跟踪所需的计算。在AIM 3中，PI将在理论上和 从计算上，小鼠用来跟踪气味径的行为策略及其基础 神经算法。他们将使用AIM 1的实验数据来评估新颖的有效性 理论框架，特别是在行业搜索策略和双边的背景下 啮齿动物的处理。 AIM 2的实验数据将用于揭示神经动力学 以及实施算法驱动行为的子电路的连通性。

项目成果

A new angle on odor trail tracking.

• DOI: 10.1073/pnas.2121332119
• 发表时间: 2022-01-18
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Jayakumar S;Murthy VN
• 通讯作者: Murthy VN