Apprentissage visuel en réalité virtuelle chez Apis mellifera

Dotées d'un cerveau de moins d'un millimètre cube et contenant environ 950 000 neurones, les abeilles présentent un riche répertoire comportemental, parmi lesquels l'apprentissage appétitif et la mémoire jouent un rôle fondamental dans le contexte des activités de recherche de nourriture. Outre les formes élémentaires d'apprentissage, où les abeilles apprennent une association spécifique entre des événements de leur environnement, les abeilles maîtrisent également différentes formes d'apprentissage non-élémentaire, à la fois dans le domaine visuel et olfactif, y compris la catégorisation, l'apprentissage contextuel et l'abstraction de règles. Ces caractéristiques en font un modèle idéal pour l'étude de l'apprentissage visuel et pour explorer les mécanismes neuronaux qui sous-tendent leurs capacités d'apprentissage. Afin d'accéder au cerveau d'une abeille lors d'une tâche d'apprentissage visuel, l'insecte doit être immobilisé. Par conséquent, des systèmes de réalité virtuelle (VR) ont été développés pour permettre aux abeilles d'agir dans un monde virtuel, tout en restant stationnaires dans le monde réel. Au cours de mon doctorat, j'ai développé un logiciel de réalité virtuelle 3D flexible et open source pour étudier l'apprentissage visuel, et je l'ai utilisé pour améliorer les protocoles de conditionnement existants en VR et pour étudier le mécanisme neuronal de l'apprentissage visuel. En étudiant l'influence du flux optique sur l'apprentissage associatif des couleurs, j'ai découvert que l'augmentation des signaux de mouvement de l'arrière-plan nuisait aux performances des abeilles. Ce qui m'a amené à identifier des problèmes pouvant affecter la prise de décision dans les paysages virtuels, qui nécessitent un contrôle spécifique par les expérimentateurs. Au moyen de la VR, j'ai induit l'apprentissage visuel chez des abeilles et quantifié l'expression immédiate des gènes précoces (IEG) dans des zones spécifiques de leur cerveau pour détecter les régions impliquées dans l'apprentissage visuel. En particulier, je me suis concentré sur kakusei, Hr38 et Egr1, trois IEG liés à la recherche de nourriture et à l'orientation des abeilles et qui peuvent donc également être pertinents pour la formation d'association visuelle appétitive. Cette analyse suggère que les corps pédonculés sont impliqués dans l'apprentissage associatif des couleurs. Enfin, j'ai exploré la possibilité d'utiliser la VR sur d'autres modèles d'insectes et effectué un conditionnement différentiel sur des bourdons. Cette étude a montré que non seulement les bourdons sont capables de résoudre cette tâche cognitive aussi bien que les abeilles, mais aussi qu'ils interagissent davantage avec la réalité virtuelle, ce qui entraîne un ratio plus faible d'individus rejetés de l'expérience par manque de mouvement. Ces résultats indiquent que les protocoles VR que j'ai établis au cours de cette thèse peuvent être appliqués à d'autres insectes, et que le bourdon est un bon candidat pour l'étude de l'apprentissage visuel en VR.Equipped with a brain smaller than one cubic millimeter and containing ~950,000 neurons, honeybees display a rich behavioral repertoire, among which appetitive learning and memory play a fundamental role in the context of foraging activities. Besides elemental forms of learning, where bees learn specific association between environmental features, bees also master different forms of non-elemental learning, including categorization, contextual learning and rule abstraction. These characteristics make them an ideal model for the study of visual learning and its underlying neural mechanisms. In order to access the working brain of a bee during visual learning the insect needs to be immobilized. To do so, virtual reality (VR) setups have been developed to allow bees to behave within a virtual world, while remaining stationary within the real world. During my PhD, I developed a flexible and open source 3D VR software to study visual learning, and used it to improve existing conditioning protocols and to investigate the neural mechanism of visual learning. By developing a true 3D environment, we opened the possibility to add frontal background cues, which were also subjected to 3D updating based on the bee movements. We thus studied if and how the presence of such motion cues affected visual discrimination in our VR landscape. Our results showed that the presence of frontal background motion cues impaired the bees' performance. Whenever these cues were suppressed, color discrimination learning became possible. Our results point towards deficits in attentional processes underlying color discrimination whenever motion cues from the background were frontally available in our VR setup. VR allows to present insects with a tightly controlled visual experience during visual learning. We took advantage of this feature to perform ex-vivo analysis of immediate early gene (IEG) expression in specific brain area, comparing learner and non-learner bees. Using both 3D VR and a lore restrictive 2D version of the same task we tackled two questions, first what are the brain region involved in visual learning? And second, is the pattern of activation of the brain dependent on the modality of learning? Learner bees that solved the task in 3D showed an increased activity of the Mushroom Bodies (MB), which is coherent with the role of the MB in sensory integration and learning. Surprisingly we also found a completely different pattern of IEGs expression in the bees that solved the task in 2D conditions. We observed a neural signature that spanned the optic lobes and MB calyces and was characterized by IEG downregulation, consistent with an inhibitory trace. The study of visual learning's neural mechanisms requires invasive approach to access the brain of the insects, which induces stress in the animals and can thus impair behaviors in itself. To potentially mitigate this effect, bumble bees Bombus terrestris could constitute a good alternative to Apis mellifera as bumble bees are more robust. That's why in the last part of this work we explored the performances of bumblebees in a differential learning task in VR and compared them to those of honey bees. We found that, not only bumble bees are able to solve the task as well as honey bees, but they also engage more with the virtual environment, leading to a lower ratio of discarded individuals. We also found no correlation between the size of bumble bees and their learning performances. This is surprising as larger bumble bees, that assume the role of foragers in the colony, have been shown to be better at learning visual tasks in the literature

Age-specific olfactory attraction between Western honey bee drones (Apis mellifera) and its chemical basis.

During the mating season, drones (males) of the Western honey bee (Apis mellifera) form congregations numbering thousands high in the air. Virgin queens arrive at these congregations after they have formed and mate on the fly with 15-20 drones. To explain the formation of drone congregations, a drone-produced aggregation pheromone has been proposed many years ago but due to the low accessibility of natural mating sites in bees, its study has progressed slowly. Recently, we used a walking simulator in controlled laboratory conditions to show that drones are indeed attracted by groups of other drones. Since these previous experiments were carried out with drones captured when flying out of the hive, it is currently unclear if this olfactory attraction behaviour is related to the drones' sexual maturity (usually reached between 9 and 12 days) and may thus be indicative of a possible role in congregation formation, or if it is observed at any age and may represent in-hive aggregation. We thus assessed here the dependency of drone olfactory attraction on their age. First, we performed behavioural experiments in the walking simulator to measure olfactory preferences of drones in three age groups from 2-3 to 12-15 days. Then, we performed chemical analyses in the same age groups to evaluate whether chemical substances produced by the drones may explain age differences in olfactory attraction. We show that honey bee drones are attracted by conspecifics of the same age when they are sexually mature (12-15 days old) but not when they are younger (2-3 and 7-8 days old). In parallel, our data show that drones' chemical profile changes with age, including its most volatile fraction. These results are discussed in the context of drone mutual attraction both within the hive and at drone congregations

Virgin queen attraction toward males in honey bees

International audienc

3D correlation NMR spectrum between three distinct heteronuclei for the characterization of inorganic samples: Application on sodium alumino-phosphate materials

International audienceWe report here an original NMR sequence allowing the acquisition of 3D correlation NMR spectra between three distinct heteronuclei, among which two are half-integer spin quadrupolar nuclei. Furthermore, as two of them exhibit close Larmor frequency, this experiment was acquired using a standard triple-resonance probe equipped with a commercial frequency splitter. This NMR technique was tested and applied to sodium alumino-phosphate compounds with 31P as the spin-1/2 nucleus and 23Na and 27Al as the close Larmor frequencies isotopes. To the best of our knowledge, such experiment with direct 31P and indirect 27Al and 23Na detection is the first example of 3D NMR experiment in solids involving three distinct heteronuclei. This sequence has first been demonstrated on a mixture of Al(PO3)3 and NaAlP2O7 crystalline phases, for which a selective observation of NaAlP2O7 is possible through the 3D map edition. This 3D correlation experiment is then applied to characterize mixing and phase segregation in a partially devitrified glass that has been proposed as a material for the sequestration of radioactive waste. The 31P-{23Na,27Al} 3D experiment conducted on the partially devitrified glass material conclusively demonstrates that the amorphous component of the material does not contain aluminum. The as-synthesized material thus presents a poor resistance against water, which is a severe limitation for its application in the radioactive waste encapsulation domain

3D correlation NMR spectrum between three distinct heteronuclei for the characterization of inorganic samples: Application on sodium alumino-phosphate materials

International audienceWe report here an original NMR sequence allowing the acquisition of 3D correlation NMR spectra between three distinct heteronuclei, among which two are half-integer spin quadrupolar nuclei. Furthermore, as two of them exhibit close Larmor frequency, this experiment was acquired using a standard triple-resonance probe equipped with a commercial frequency splitter. This NMR technique was tested and applied to sodium alumino-phosphate compounds with 31P as the spin-1/2 nucleus and 23Na and 27Al as the close Larmor frequencies isotopes. To the best of our knowledge, such experiment with direct 31P and indirect 27Al and 23Na detection is the first example of 3D NMR experiment in solids involving three distinct heteronuclei. This sequence has first been demonstrated on a mixture of Al(PO3)3 and NaAlP2O7 crystalline phases, for which a selective observation of NaAlP2O7 is possible through the 3D map edition. This 3D correlation experiment is then applied to characterize mixing and phase segregation in a partially devitrified glass that has been proposed as a material for the sequestration of radioactive waste. The 31P-{23Na,27Al} 3D experiment conducted on the partially devitrified glass material conclusively demonstrates that the amorphous component of the material does not contain aluminum. The as-synthesized material thus presents a poor resistance against water, which is a severe limitation for its application in the radioactive waste encapsulation domain

Age effect on drones’ chemical profile—most abundant compounds.

<p>Linear discriminant analyses performed on <i>abundant</i> compounds (defined as amounting to more than 1% within each fraction), (<b>A</b>) using the 42 abundant peaks from both fractions, (<b>B</b>) the 22 abundant peaks of the high-volatility fraction (<b>C</b>) the 20 abundant peaks of the low-volatility fraction. Discriminant analyses clearly segregated the drones depending on their age in all cases. Separation was slightly less marked in the case of the low-volatility fraction.</p

Olfactory attraction between 2–3 day-old drones.

<p>2–3 day-old drones’ behaviour on the walking simulator, when stimulated with the odour bouquet of 10 same age drones. <b>A,C)</b> Circular histograms showing the percentage of time spent (A) or of distance travelled (C) by drones according to 15° sectors, with the odour quadrant being represented on the upper left (grey area). Light grey bars represent the 5 min before odour stimulation (‘before’), black bars represent the 5 min during stimulation (‘during’), and hence, dark grey bars show the overlap of the two phases. <b>B,D)</b> Histograms of the percentage of time spent (B), or of distance travelled (D) by drones in the odour quadrant (gray box) and on average in the three odourless quadrants (white box) before and during odour stimulation.</p

Mean proportions (± SEM) of five classes of compounds in drones according to age.

<p>Considering typical cuticular hydrocarbons (mostly from the low-volatility fraction), mature drones (12–15 days old) had significantly higher levels of alkenes and dimethyl alkanes but lower levels of straight chain alkanes compared to younger drones (2–3 and 7–8 days old). Apart from these, 2–3 day-old drones had significantly lower levels of highly-volatile compounds than older drones (7–8 and 12–15 days old). Letters indicate significant differences between groups of age (Kruskal-Wallis test followed by Dunn’s post-hoc test).</p

Olfactory attraction between 7–8 day-old drones.

<p>7–8 day-old drones’ behaviour on the walking simulator, when stimulated with the odour bouquet of 10 same age drones. <b>A,C)</b> Circular histograms showing the percentage of time spent (A) or of distance travelled (C) by drones according to 15° sectors, with the odour quadrant being represented on the upper left (grey area). Light grey bars represent the 5 min before odour stimulation (‘before’), black bars represent the 5 min during stimulation (‘during’), and hence, dark grey bars show the overlap of the two phases. <b>B,D)</b> Histograms of the percentage of time spent (B), or of distance travelled (D) by drones in the odour quadrant (gray box) and on average in the three odourless quadrants (white box) before and during odour stimulation. (*): 0.05 < p < 0.1, Wilcoxon matched pairs tests.</p

Age effect on drones’ chemical profile.

<p>The chemical composition of drone pentane extracts was analysed using gas-chromatography. The figure shows the representations of all individuals in the three age groups according to the first three factors of Principal Component Analyses (PCA) performed on (<b>A</b>) the 183 peaks of the whole chemical profiles, (<b>B</b>) only the 84 peaks of the high-volatility fraction, or (<b>C</b>) only the 99 peaks of the low-volatility fraction. The three PCAs show clear differences between the chemical profiles of the three age groups.</p