Behavioral responsiveness to external stimulation is shaped by context. We studied how sensory information can be contextualized, by examining light-evoked locomotor responsiveness of Drosophila relative to time of day. We found that light elicits an acute increase in locomotion (startle) that is modulated in a time-of-day–dependent manner: Startle is potentiated during the nighttime, when light is unexpected, but is suppressed during the daytime. The internal daytime-nighttime context is generated by two interconnected and functionally opposing populations of circadian neurons—LNvs generating the daytime state and DN1as generating the nighttime state. Switching between the two states requires daily remodeling of LNv and DN1a axons such that the maximum presynaptic area in one population coincides with the minimum in the other. We propose that a dynamic model of environmental light resides in the shifting connectivities of the LNv-DN1a circuit, which helps animals evaluate ongoing conditions and choose a behavioral response.

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Ongoing sensations are compared to internal, experience-based, reference models; mismatch between reality and expectation can signal opportunity or danger, and can shape behavior. The nature of internal reference models is largely unknown. We describe a model that enables moment-to-moment luminance evaluation in flies. Abrupt shifts to lighting conditions inconsistent with the subjective time-of-day trigger locomotion, whereas shifts to appropriate conditions induce quiescence. The time-of-day prediction is generated by a slowly shifting activity balance between opposing neuronal populations, LNvs and DN1as. The two populations undergo structural changes in axon length that accord with, and are required for, conveying time-of-day information. Each day, in each population, the circadian clock directs cellular remodeling such that the maximum axonal length in one population coincides with the minimum in the other; preventing remodeling prevents transitioning between opposing internal states. We propose that a dynamic predictive model resides in the shifting connectivities of the LNv- DN1a circuit.