Behavioral Dynamics and Neural Computation for Sensory Navigation in C. elegans

Abstract

Sensory navigation is crucial for survival and is a common trait across species of all scales. Studying navigation strategies in animals strengthens our understanding of behavioral algorithms and the underlying neural computation. In this thesis, we investigated sensory navigation in the nematode worm C. elegans by combining a novel experimental setup, statistical modeling, neural perturbation and measurements. The integrative approach provides scientific insight into the worm’s navigation strategies, learningdependent behavior, and the underlying neural mechanisms. In the following three chapters, we first built an odor flow chamber and sensory array to control and monitor airborne odor environments, enabling the study of navigation in small animals, such as C. elegans and Drosophila larvae. Secondly, we measured learning-dependent odor-guided navigation in worms and developed a novel statistical model to analyze detailed navigation trajectories. We characterized how learning alters the sensorimotor transformation in worms, demonstrate that the statistical model outperforms classic chemotaxis metric, and revealed distributed neural mechanisms through optogenetic perturbation and genetic ablation. Thirdly, we examined behavioral dynamics along the worm’s chemotaxis trajectory by fitting an extended model with latent states, forming an input-output hidden Markov model for navigation. We found that this state-dependent model better describes the data, reveals complex state-dependent navigation strategies, and pinpoints neural circuits involved in the behavioral dynamics. Additionally, in the last two chapters, we introduced algorithms for stochastic network dynamics that generate state-dependent behavior and analyzed the underlying dynamics. Theoretical projects include a latent variable model for signal progressing in neural imaging, a dynamical model that captures adaptive neural response, and a theoretical framework to analyze nonequilibrium networks. Together, this thesis demonstrates the integration of precise measurements, statistical models, and theory-driven experiments, providing insights into animal navigation algorithms and neural computation.