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.

总结 动物会主动采集感觉信息，然后将这些信息与先前的知识联合收割机结合起来， 感觉运动反馈回路中的决定。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下，如头部限制的动物， 虚拟现实在实验室里研究一种与动物行为学相关的自然行为， 更深入地了解行为策略及其机械神经实现。 气味跟踪就是这样一种行为，在包括老鼠在内的许多陆生动物中观察到， 并且包括沿轨迹沿着连续的重新定向。气味线索的获得是严重的 通过嗅探和身体运动进行主动采样，这引入了强耦合 感觉和运动之间的联系理论研究暗示基于多模式战略 双边抽样，时间整合和使用内部模型，其相对 捐款情况尚不清楚。在这里，一个由三名专业知识互补的PI组成的团队建议 剖析嗅觉跟踪的算法和神经基础，可以提供更深入的 对主动感觉、空间导航和连续决策的深入了解。使用 行为，生理，分子和分析方法，PI将测试算法 假设和识别由以下目标指导的神经回路。在目标1中，他们将 研究小鼠在跟踪过程中表现出的策略，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 老鼠在定制的跑步机上追踪气味踪迹时的行为。在目标2中，PI将 揭示神经回路和细胞类型的大脑区域参与跟踪线索。他们将使用 嗅觉神经活动的细胞类型靶向测量、病毒示踪和转录组学 皮层区域，以揭示活动模式和神经连接，支持神经 跟踪所需的计算。在目标3中，PI将从理论上阐明， 在计算上，老鼠用来追踪气味踪迹的行为策略，以及它们的潜在的 神经算法他们将使用目标1的实验数据来评估小说的有效性 理论框架，特别是在部门搜索战略和双边 由啮齿动物加工。Aim 2的实验数据将用于揭示神经动力学 以及实现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