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Flying insects track turbulent odor plumes to find mates, food and egg-laying sites. To maintain contact with the plume, insects are thought to adapt their flight control according to the distribution of odor in the plume using the timing of odor onsets and intervals between odor encounters. Although timing cues are important, few studies have addressed whether insects are capable of deriving spatial information about odor distribution from bilateral comparisons between their antennae in flight. The proboscis extension reflex (PER) associative learning protocol, originally developed to study odor learning in honeybees, was used as a tool to ask if hawkmoths, Manduca sexta, can discriminate between odor stimuli arriving on either antenna. We show moths discriminated the odor arrival side with an accuracy of >70%. Information about spatial distribution of odor stimuli may be available to moths searching for odor sources, opening the possibility that they use both spatial and temporal odor information. This article has an associated First Person interview with the first author of the paper.

Two basic tasks must be performed by an olfactory robot tracking a specific odor source : navigate in a turbulent odor plume and recognize an odor regardless of its concentration. For these two tasks, we propose simple biologically inspired strategies, well suited for building dedicated circuits and for on-board implementation on real robots. The odor recognition system is based on a spiking neural network using a synchronization coding scheme. The robot navigation system is based on the use of bilateral comparison between two spatially separated gas sensors arrays at either side of the robot. We propose binary or analog navigation laws depending on the nature of the available sensory information extracted from the plume structure (isolated odor patches or smoother concentration field).