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标题：Sector search strategies for odor trail tracking
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作者：Gautam Reddy ; Boris I. Shraiman ; Massimo Vergassola 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：1
DOI：10.1073/pnas.2107431118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance Surface-bound odor trail tracking is critical for the survival of terrestrial animals dependent on olfaction. Little is known about how animals track trails at the algorithmic level. In the present study, we propose that a tracking animal maintains a noisy estimate of where the trail is headed based on its past contacts with the trail. We show that virtual agents trained to exploit this strategy reproduce the tracking patterns of ants and rodents. The observed patterns emerge simply as a consequence of common geometric constraints, which also impose fundamental limits on how quickly an animal can track trails. A series of experiments is proposed to quantify how past experience and trail statistics shape tracking behavior. Ants, mice, and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies remain poorly understood. Chemotaxis-based strategies cannot explain casting, a characteristic sequence of wide oscillations with increasing amplitude performed upon sustained loss of contact with the trail. We propose that tracking animals have an intrinsic, geometric notion of continuity, allowing them to exploit past contacts with the trail to form an estimate of where it is headed. This estimate and its uncertainty form an angular sector, and the emergent search patterns resemble a “sector search.” Reinforcement learning agents trained to execute a sector search recapitulate the various phases of experimentally observed tracking behavior. We use ideas from polymer physics to formulate a statistical description of trails and show that search geometry imposes basic limits on how quickly animals can track trails. By formulating trail tracking as a Bellman-type sequential optimization problem, we quantify the geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate, and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and biomimetic robots and formulate trail tracking as a behavioral paradigm for learning, memory, and planning.
关键词：enstrackingalgorithmbehavioroptimization