Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond
Year: 2025
Type: article
Abstract: Certain animals have evolved complex strategies to track sources of odors which are advected by turbulent flows. In this paper, we model this search task as a partially observable Markov decision process, which allows us to compute optimal Bayesian search strategies in the sense that they reach the source in minimal average time. We apply this approach to realistic data taken from direct numerical simulation. Focusing on the especially difficult decision of what to do when contact with the odor has been lost, we study the optimal trajectories in this scenario --- which strongly resemble known animal behaviors --- and try to understand the results by way of a simplified model.
Source: Physical Review Fluids
Language: English
Subfield: Insect Science
Domain: Life Sciences
Open Access status: green
Awards: G2385671131, G2943358811, G434337789, G7145292521, G7609994795, G874694918, Odor trail tracking: a new paradigm to unveil algorithms and neural circuits underlying active sensation and continuous decision making, Odor trail tracking: a new paradigm to unveil algorithms and neural circuits underlying active sensation and continuous decision making
OpenAlex ID: W4409292123