Navigating Complex Environments with Finite-State Controllers

Organisms relying on chemical cues for navigation face significant challenges due to complexity in the environment. For instance, atmospheric turbulence dilutes and mixes odor signals with other scents and clean air, providing only weak, intermittent cues for insects like moths to navigate. Despite these challenges, many species develop effective strategies to locate distant targets in complex environments. This raises a key question: how are the sporadic chemical signals utilized to implement efficient source-localization strategies? The searcher’s memory of previously detected signals plays a vital role in this process. Current algorithms typically require continuous memory spaces with high dimensionality, which may impede optimization and complicate interpretation. In this research, we demonstrate through a computational modeling of the source localization problem that finite-state controllers, simple algorithmic devices with minimal memory requirements, are rich enough to explain various behavioral patterns observed in nature, first in the context of olfactory search. The controller’s memory states emerged to encode dual information streams: temporal data functioning as a clock, and spatial data serving as a map. In the microscale level, we developed a finite-state controller for E. coli chemotaxis that achieves precise adaptation and exhibits positive responses to increasing stimuli. Lastly, we extend the olfactory search problem to analyze sourcetracking in an alternative context: a porous medium characterized by chaotic flow patterns, where agents must simultaneously learn to circumvent obstacles while localizing a chemical signal source. Our findings demonstrate that finite-state controllers are simple yet powerful tools for understanding behavioral patterns in diverse navigation scenarios.