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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 nematodeCaenorhabditis elegansby 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.
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Olfactory navigation in arthropods
Abstract Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources—such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures—antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments.
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- Award ID(s):
- 2014217
- PAR ID:
- 10392103
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Comparative Physiology A
- Volume:
- 209
- Issue:
- 4
- ISSN:
- 0340-7594
- Page Range / eLocation ID:
- p. 467-488
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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