title: Reactive searching and infotaxis in odor source localization.
abstract: Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.
authors.affiliation: CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy, France.
authors.forename: Nicole
authors.initials: N
authors.lastname: Voges
authors.affiliation: Inserm, UMR S968, Institut de la Vision, Paris, France.
authors.forename: Antoine
authors.initials: A
authors.lastname: Chaffiol
authors.affiliation: INRA, UMR 1392, Institute of Ecology and Environmental Sciences of Paris, Versailles, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lucas
authors.affiliation: CNRS, LORIA, UMR 7503, Vandoeuvre-les-Nancy, France.
authors.forename: Dominique
authors.initials: D
authors.lastname: Martinez
mesh_terms: D000465:Algorithms; D000818:Animals; D001070:Appetitive Behavior; D001185:Artificial Intelligence; D019295:Computational Biology; D005260:Female; D005426:Flight, Animal; D008297:Male; D008954:Models, Biological; D009036:Moths; D009812:Odorants / Q000032:analysis*; D010675:Pheromones; D012371:Robotics
chemical_list: D010675:Pheromones