AbstractAnimals 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 dynamical 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 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 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 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.Author SummaryAnimals constantly integrate the activity of neurons throughout their bodies to choose the most appropriate behavior. A key goal in quantitative neuroscience is to characterize and predict how neuronal circuits control and modulate behavior. C. elegans, a nematode with a fully mapped connectome, is an ideal model organism for elucidating the links between neuronal circuits and behavior. However, many studies relating activity in neurons to behavior rely on spontaneous behavior and lack information about their dynamics. In this study, we formulate unbiased quantitative characterizations of sensory neurons in C. elegans using with reverse correlation analysis with a white noise stimulus. We use optogenetics to stimulate body touch sensory neurons in freely moving worms, and provide quantitative descriptions that capture the dynamic transformations between sensory neuron activity and behavioral outputs. Our results are consistent with previous findings, and additionally provide new insights on the spatial encoding of these systems. Our system provides a simple platform for characterizing the behavioral output due to specific neurons, and can be extended to any excitable cell in the animal.
Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding