bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.10.11.617548

Neuronal correlates of sleep in honey bees

Moguilner, S.·Tiraboschi, E.·Fantoni, G.·Strelevitz, H.·Soleimani, H.·Del Torre, L.·Hasson, U.·Haase, A.
View original source ↗Export metadata ↓

Abstract

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.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moguilner, S., Tiraboschi, E., Fantoni, G., Strelevitz, H., Soleimani, H., Del Torre, L., Hasson, U., Haase, A.. 2024-10-12. Neuronal correlates of sleep in honey bees. https://doi.org/10.1101/2024.10.11.617548

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗
bioRxiv2026

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗
bioRxiv2026

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗