ArticleProceedings of the National Academy of Sciences of the United States of America2022
Sector search strategies for odor trail tracking.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- Combining social and private information: How ants use pheromones and learnt cues to navigate.Learning & behavior · 2026Review
- Learning spatial hearing via innate mechanisms.PLoS computational biology · 2025Article
- Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.bioRxiv : the preprint server for biology · 2025Article
- Mice navigate scent trails using predictive policies.bioRxiv : the preprint server for biology · 2025Article
- Manifold learning for olfactory habituation to strongly fluctuating backgrounds.bioRxiv : the preprint server for biology · 2025Article
- Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond.Physical review fluids · 2025Article
- Design Principles From Natural Olfaction for Electronic Noses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- How do mammals convert dynamic odor information into neural maps for landscape navigation?PLoS biology · 2024Article
- Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice.bioRxiv : the preprint server for biology · 2024Article
- Olfactory cues and memories in animal navigation.Nature reviews. Physics · 2024Article
- Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice.PloS one · 2024Article
- Adaptive algorithms for shaping behavior.bioRxiv : the preprint server for biology · 2023Article
- A dynamical model ofProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.Nature machine intelligence · 2023Article
- Article
- Olfactory Sensing and Navigation in Turbulent Environments.Annual review of condensed matter physics · 2022Article
- A new angle on odor trail tracking.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.