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Animals adjust their behavioral response to sensory input adaptivelydepending on past experiences. The flexible brain computation is crucial forsurvival and is of great interest in neuroscience. The nematode C. elegansmodulates its navigation behavior depending on the association of odor butanonewith food (appetitive training) or starvation (aversive training), and willthen climb up the butanone gradient or ignore it, respectively. However, theexact change in navigation strategy in response to learning is still unknown.Here we study the learned odor navigation in worms by combining preciseexperimental measurement and a novel descriptive model of navigation. Our modelconsists of two known navigation strategies in worms: biased random walk andweathervaning. We infer weights on these strategies by applying the model toworm navigation trajectories and the exact odor concentration it experiences.Compared to naive worms, appetitive trained worms up-regulate the biased randomwalk strategy, and aversive trained worms down-regulate the weathervaningstrategy. The statistical model provides prediction with $>90 \%$ accuracy ofthe past training condition given navigation data, which outperforms theclassical chemotaxis metric. We find that the behavioral variability is alteredby learning, such that worms are less variable after training compared to naiveones. The model further predicts the learning-dependent response andvariability under optogenetic perturbation of the olfactory neuronAWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream fromAWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guidednavigation. Together, we provide a new paradigm to quantify flexible navigationalgorithms and pinpoint the underlying neural substrates.

Olfactory learning alters navigation strategies and behavioral variability in C. elegans