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 动物主动采集感官信息，并将其与先验知识结合起来 感觉运动反馈回路中的决策。这个复杂循环的各个方面经常被研究 隔离，使用试验结构并在简化的条件下进行，例如在 虚拟现实。在实验室中研究与行为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现的更深入的见解。 气味踪迹追踪就是这样一种行为，在包括小鼠在内的许多陆生动物中观察到， 并涉及沿步道不断重新定向。气味线索的获取非常重要 通过嗅探和身体运动进行主动采样引导，这引入了强耦合 感觉和运动动作之间。理论研究暗示基于多模式策略 关于双边采样、时间整合和内部模型的使用，其相对 贡献仍不清楚。在这里，由三名具有互补专业知识的 PI 组成的团队提出了建议 剖析嗅觉轨迹跟踪的算法和神经基础，它可以提供更深入的 对主动感觉、空间导航和持续决策的洞察。使用 行为、生理、分子和分析方法，PI 将测试算法 假设并识别由以下目标指导的神经回路。在目标 1 中，他们将 研究小鼠在踪迹追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 在定制跑步机上追踪气味轨迹时小鼠的行为。在目标 2 中，PI 将 揭示参与踪迹追踪的大脑区域的神经回路和细胞类型。他们会使用 嗅觉神经活动、病毒追踪和转录组学的细胞类型靶向测量 皮质区域揭示支持神经的活动模式和神经连接 轨迹跟踪所需的计算。在目标 3 中，PI 将在理论上和 通过计算，小鼠用来追踪气味踪迹的行为策略及其潜在的 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战略的背景下 由啮齿类动物加工。 Aim 2 的实验数据将用于揭示神经动力学 以及实现驱动行为的算法的子电路的连接。

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