Localization of odors is essential to animal survival, and thus animals are adept at odor-navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment. To do so, we compared mouse, in silico model, and Arduino-based robot odor-localization behavior in a standardized odor landscape. Mouse performance remains robust in the presence of increased complexity, showing a shift in strategy towards faster movement with increased environmental complexity. Implementing simple binaral and temporal models of tropotaxis and klinotaxis, an in silico model and Arduino robot, in the same environment as the mice, are equally successful in locating the odor source within a plume of low complexity. However,  performance of these algorithms significantly drops when the chaotic nature of the plume is increased. Additionally, both algorithm-driven systems show more successful performance when using a strictly binaral model at a larger sensor separation distance and more successful performance when using a temporal and binaral model when using a smaller sensor separation distance. This suggests that with an increasingly chaotic odor environment, mice rely on complex strategies that allow for robust odor localization that cannot be resolved by minimal algorithms that display robust performance at low levels of complexity. Thus, highlighting that an animal’s ability to modulate behavior with environmental complexity is beneficial for odor localization.

Extended Data 1. In silico MATLAB and Arduino codes.

Included are MATLAB codes to generate the center and corner odor plumes (file names: odorFun_plume_center.m, odorFun_plume_corner.m), test the in silico simulated robot using code A and Code B (filenames: SimRobot_test_A.m, SimRobot_test_B.m), and to test the in silico model with replicates (filenames: run_model_A_replicates.m, run_model_B_replicates.m). Additionally, the two Arduino codes for robot navigation (file names: Robot_CodeA.ino, Robot_CodeB.ino).

Also included: robot STL files for 3D printing parts.

Also included: Movies 1-19.

Four minutes of near-surface acetone planar laser-induced fluorescence (PLIF) plume data from Connor et al 2018 was used as input for these models ('11282017_10cms_bounded.h5','/dataset7').The above models are deterministic. If they are synchronized with the first frame of the plume dataset, they will always generate the same trajectory. To simulate “random” complexity, each model evaluation initialized the plume dataset at a randomly chosen frame between 1 and 3600; the four-minute dataset was then allowed to loop continuously until the simulation concluded (Movie 1, Movie 2).