Learning to predict target location with turbulent odor plumes

TL;DR

This study investigates whether timing or intensity of odor detection better predicts target location, revealing that both can be effective but vary with odor dilution, influencing olfactory strategies and neural representations.

Contribution

The paper demonstrates that both odor timing and intensity can predict target location, and introduces strategies to enhance prediction accuracy by combining these measures.

Findings

Both intensity and timing predict target location from several meters away.

Efficacy of measures varies with odor dilution levels.

Combining measures improves prediction robustness.

Abstract

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.