Neuronal correlates of sleep in honey bees

Honey bees Apis mellifera follow the day-night cycle for their foraging activity, entering rest periods during dark. To date, studies of bee sleep have focused mostly on behavioural markers; it is not known if these resting states manifest reduced information processing at the level of brain networks, a defining signature of sleep. We used two-photon calcium imaging of the antennal lobes (AL) in head-fixed bees to analyse brain activity in motion/rest epochs during the nocturnal period. The neuronal activity data during these epochs were then computationally described, and machine learning was applied to determine whether a classifier could distinguish the two states after motion correction. Out-of-sample classification accuracy reached up to 93%. The feature importance analysis suggested network features to be decisive. Accordingly, the glomerular connectivity was found to be significantly increased in the rest-state patterns. A full simulation of the AL using a leaky spiking neural network revealed that such a transition in network connectivity could be achieved by weakly correlated input noise and a reduction of synaptic conductance of the inhibitive local neurons (LNs) which couple the AL network nodes. The difference in the AL response maps between awake- and sleep-like states generated by the simulation showed a decreased specificity of the odour code in the sleep state, suggesting reduced information processing during sleep. Since LNs in the bee brain are GABAergic, this suggests that the GABAergic system plays a central role in sleep regulation in bees as in many higher species including humans. Such conservation of sleep mechanisms throughout evolution indicates that bees can serve as a model for studying these mechanisms on the level of individual neurons.