Animals adjust their behavioral response to sensory input adaptively
depending on past experiences. The flexible brain computation is crucial for
survival and is of great interest in neuroscience. The nematode C. elegans
modulates its navigation behavior depending on the association of odor butanone
with food (appetitive training) or starvation (aversive training), and will
then climb up the butanone gradient or ignore it, respectively. However, the
exact change in navigation strategy in response to learning is still unknown.
Here we study the learned odor navigation in worms by combining precise
experimental measurement and a novel descriptive model of navigation. Our model
consists of two known navigation strategies in worms: biased random walk and
weathervaning. We infer weights on these strategies by applying the model to
worm navigation trajectories and the exact odor concentration it experiences.
Compared to naive worms, appetitive trained worms up-regulate the biased random
walk strategy, and aversive trained worms down-regulate the weathervaning
strategy. The statistical model provides prediction with >90% accuracy of
the past training condition given navigation data, which outperforms the
classical chemotaxis metric. We find that the behavioral variability is altered
by learning, such that worms are less variable after training compared to naive
ones. The model further predicts the learning-dependent response and
variability under optogenetic perturbation of the olfactory neuron
AWCON. Lastly, we investigate neural circuits downstream from
AWCON that are differentially recruited for learned odor-guided
navigation. Together, we provide a new paradigm to quantify flexible navigation
algorithms and pinpoint the underlying neural substrates