As animals navigate through complex environments, they must integrate the activity of multiple mechanoreceptors, sensing forces throughout their bodies and allowing them to move in appropriate directions. In Caenorhabditis elegans, the only organism with a fully mapped connectome, the neural circuit involved in mechanosensation is well characterized. Although the general roles of the neurons in this circuit have been defined, most studies involve experiments with a small number of unnatural stimuli, leading to quantitative descriptions that may be biased towards the tested stimuli. In this work, we elucidate unbiased descriptions of the mechanosensory system in C. elegans by using reverse correlation analysis. We use a custom tracking and optogenetics platform to characterize and compare two mechanosensory systems in C. elegans: the gentle touch sensing TRNs and harsh touch sensing PVDs. This method yields linear filters that capture dynamics that are consistent with previous findings, as well as providing new insights on the spatial encoding of the TRN and PVD neurons. Our results suggest that the tiled network of the TRNs allow for spatial encoding with better resolution than PVD. Additionally, linearnonlinear models accurately predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that fully characterize these systems.

Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamic information. Here, we formulate a set of unbiased quantitative characterizations of the mechanosensory system by using reverse correlation analysis on behavior. We use a custom tracking, selective illumination, and optogenetics platform to compare two mechanosensory systems: the gentle-touch (TRNs) and harsh-touch (PVD) circuits. This method yields characteristic linear filters that allow for the prediction of behavioral responses. The resulting filters are consistent with previous findings and further provide new insights on the dynamics and spatial encoding of the systems. Our results suggest that the tiled network of the gentle-touch neurons has better resolution for spatial encoding than the harsh-touch neurons. Additionally, linear-nonlinear models can predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that quantitatively characterize these systems. Furthermore, this platform can be extended to capture the behavioral dynamics induced by any neuron or other excitable cells in the animal.