Strelevitz, H. | Search bioRxiv

bioRxiv2024

Neuronal correlates of sleep in honey bees

Honey bees Apis mellifera follow the day-night cycle for their foraging activity, entering rest periods during darkness. Despite considerable research on sleep behaviour in bees, its underlying neurophysiological mechanisms are not well understood, partly due to the lack of brain imaging data that allow for analysis from a network- or system-level perspective. This study aims to fill this gap by investigating whether neuronal activity during rest periods exhibits stereotypic patterns comparable to sleep signatures observed in vertebrates. Using two-photon calcium imaging of the antennal lobes (AL) in head-fixed bees, we analysed brain dynamics across motion and rest epochs during the nocturnal period. The recorded activity was computationally characterised, and machine learning was applied to determine whether a classifier could distinguish the two states after motion correction. Out-of-sample classification accuracy reached 93%, and a 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. Our findings provide the first evidence that sleep-related network modulation mechanisms may be conserved throughout evolution, highlighting the bees potential as an invertebrate model for studying sleep at the level of single neurons.

neuroscience↗

bioRxiv2023

Temporal dynamics of honeybee learning and decision-making are revealed by a novel automated PER system

The proboscis extension response (PER) has been widely used for decades to evaluate honeybees (Apis mellifera) learning and memory abilities. This classical conditioning paradigm is traditionally administered manually, and produces a binary score for each subject depending on the presence or absence of the proboscis extension in response to a stimulus - typically an odor which has been associated with a sucrose reward - to classify whether or not the bee has learned the association. Here we present a fully automated PER system which delivers stimuli in a more controlled manner, and thus standardizes the protocol within and between labs; further, the AI-facilitated behavioral scoring reduces human error and allows us to extract a richer meaning from the outcome. The automated frame-by-frame assessment goes beyond the binary classification of "learned" or "not learned", expanding the possibilities for many other measures. Using this method, we investigate the real-time decision-making processes of honeybees faced with difficult learning tasks. When posed with a quantitative (rather than qualitative, as in the case of different odors) PER association, honeybees show a pattern of rapid generalization to both the rewarded and non-rewarded stimuli, followed by a slowly acquired discrimination between the two. Our work lays the foundation for deeper exploration of the honeybee cognitive processes when posed with complex learning challenges.

neuroscience↗