  Animal behavior and neural recordings show that the brain is able to measure
both the intensity of an odor and the timing of odor encounters. However,
whether intensity or timing of odor detections is more informative for
olfactory-driven behavior is not understood. To tackle this question, we
consider the problem of locating a target using the odor it releases. We ask
whether the position of a target is best predicted by measures of timing
intensity of its odor, sampled for a short period of time. To answer this
question, we feed data from accurate numerical simulations of odor transport to
machine learning algorithms that learn how to connect odor to target location.
We find that both intensity and timing can separately predict target location
even from a distance of several meters; however their efficacy varies with the
dilution of the odor in space. Thus organisms that use olfaction from different
ranges may have to switch among different modalities. This has implications on
how the brain should represent odors as the target is approached. We
demonstrate simple strategies to improve accuracy and robustness of the
prediction by modifying odor sampling and appropriately combining distinct
measures together. To test the predictions, animal behavior and odor
representation should be monitored as the animal moves relative to the target,
or in virtual conditions that mimic concentrated dilute environments.
