Navigation strategies in Caenorhabditis elegans are differentially altered by learning.

Learned olfactory-guided navigation is a powerful platform for studying how a brain generates goal-directed behaviors. However, the quantitative changes that occur in sensorimotor transformations and the underlying neural circuit substrates to generate such learning-dependent navigation is still unclear. Here we investigate learned sensorimotor processing for navigation in the nematode Caenorhabditis elegans by measuring and modeling experience-dependent odor and salt chemotaxis. We then explore the neural basis of learned odor navigation through perturbation experiments. We develop a novel statistical model to characterize how the worm employs two behavioral strategies: a biased random walk and weathervaning. We infer weights on these strategies and characterize sensorimotor kernels that govern them by fitting our model to the worm's time-varying navigation trajectories and precise sensory experiences. After olfactory learning, the fitted odor kernels reflect how appetitive and aversive trained worms up- and down-regulate both strategies, respectively. The model predicts an animal's past olfactory learning experience with  > 90% accuracy given finite observations, outperforming a classical chemotaxis metric. The model trained on natural odors further predicts the animals' learning-dependent response to optogenetically induced odor perception. Our measurements and model show that behavioral variability is altered by learning-trained worms exhibit less variable navigation than naive ones. Genetically disrupting individual interneuron classes downstream of an odor-sensing neuron reveals that learned navigation strategies are distributed in the network. Together, we present a flexible navigation algorithm that is supported by distributed neural computation in a compact brain.