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

摘要 动物会主动采集感官信息，并将其与先验知识相结合，形成 感觉运动反馈环路中的决策。中经常研究这种复杂循环的各个方面 隔离，使用试验结构，并在简化的条件下，如在 虚拟现实。在实验室里研究一种与行为相关的自然行为可以提供 对行为策略及其机械性神经实现有更深入的了解。 气味追踪就是这样一种行为，在包括老鼠在内的许多陆地动物中观察到， 并涉及沿着小径不断地重新定位。对气味线索的获取很大程度上 由通过嗅探和身体运动的主动采样引导，这引入了强烈的耦合 在感觉和运动动作之间。理论研究暗示基于多模式的策略 双边抽样、时间整合与内部模型的使用 捐款情况仍不明朗。在这里，一个由三名具有互补专业知识的PI组成的团队提出 剖析了嗅觉跟踪的算法基础和神经基础，为更深层次的嗅觉跟踪提供了理论依据 对主动感觉、空间导航和持续决策的洞察。vbl.使用 行为、生理、分子和分析方法，PI将测试算法 假设和识别神经回路，以下列目标为指导。在目标1中，他们将 研究小鼠在追踪线索过程中表现出的策略，并识别大脑区域 支持这一行为。将使用高通量自适应系统来表征 在定制的跑步机上跟踪气味痕迹时老鼠的行为。在Aim 2中，PI将 发现牵涉到线索追踪的大脑区域的神经回路和细胞类型。他们将使用 嗅觉神经活动、病毒追踪和转录的细胞型靶向测量 皮质区域以揭示支持神经的活动和神经连接模式 跟踪踪迹所需的计算。在目标3中，私人投资总监将从理论上和 在计算上，小鼠用来追踪气味痕迹的行为策略，以及它们潜在的 神经算法。他们将使用目标1的实验数据来评估一部小说的有效性 理论框架，特别是在部门搜索战略和双边框架内 由啮齿动物加工。Aim 2的实验数据将被用来揭示神经动力学 以及实现驱动行为的算法的子电路的连接性。

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.