Behavioral and Neurophysiological Study of Olfactory Perception and Learning in Honeybees

The honeybee Apis mellifera has been a central insect model in the study of olfactory perception and learning for more than a century, starting with pioneer work by Karl von Frisch. Research on olfaction in honeybees has greatly benefited from the advent of a range of behavioral and neurophysiological paradigms in the Lab. Here I review major findings about how the honeybee brain detects, processes, and learns odors, based on behavioral, neuroanatomical, and neurophysiological approaches. I first address the behavioral study of olfactory learning, from experiments on free-flying workers visiting artificial flowers to laboratory-based conditioning protocols on restrained individuals. I explain how the study of olfactory learning has allowed understanding the discrimination and generalization ability of the honeybee olfactory system, its capacity to grant special properties to olfactory mixtures as well as to retain individual component information. Next, based on the impressive amount of anatomical and immunochemical studies of the bee brain, I detail our knowledge of olfactory pathways. I then show how functional recordings of odor-evoked activity in the brain allow following the transformation of the olfactory message from the periphery until higher-order central structures. Data from extra- and intracellular electrophysiological approaches as well as from the most recent optical imaging developments are described. Lastly, I discuss results addressing how odor representation changes as a result of experience. This impressive ensemble of behavioral, neuroanatomical, and neurophysiological data available in the bee make it an attractive model for future research aiming to understand olfactory perception and learning in an integrative fashion

Neural Organization of A3 Mushroom Body Extrinsic Neurons in the Honeybee Brain

In the insect brain, the mushroom body is a higher order brain area that is key to memory formation and sensory processing. Mushroom body (MB) extrinsic neurons leaving the output region of the MB, the lobes and the peduncle, are thought to be especially important in these processes. In the honeybee brain, a distinct class of MB extrinsic neurons, A3 neurons, are implicated in playing a role in learning. Their MB arborisations are either restricted to the lobes and the peduncle, here called A3 lobe connecting neurons, or they provide feedback information from the lobes to the input region of the MB, the calyces, here called A3 feedback neurons. In this study, we analyzed the morphology of individual A3 lobe connecting and feedback neurons using confocal imaging. A3 feedback neurons were previously assumed to innervate each lip compartment homogenously. We demonstrate here that A3 feedback neurons do not innervate whole subcompartments, but rather innervate zones of varying sizes in the MB lip, collar, and basal ring. We describe for the first time the anatomical details of A3 lobe connecting neurons and show that their connection pattern in the lobes resemble those of A3 feedback cells. Previous studies showed that A3 feedback neurons mostly connect zones of the vertical lobe that receive input from Kenyon cells of distinct calycal subcompartments with the corresponding subcompartments of the calyces. We can show that this also applies to the neck of the peduncle and the medial lobe, where both types of A3 neurons arborize only in corresponding zones in the calycal subcompartments. Some A3 lobe connecting neurons however connect multiple vertical lobe areas. Contrarily, in the medial lobe, the A3 neurons only innervate one division. We found evidence for both input and output areas in the vertical lobe. Thus, A3 neurons are more diverse than previously thought. The understanding of their detailed anatomy might enable us to derive circuit models for learning and memory and test physiological data

The neuronal architecture of the mushroom body provides a logic for associative learning

We identified the neurons comprising the Drosophila mushroom body (MB), an associative center in invertebrate brains, and provide a comprehensive map describing their potential connections. Each of the 21 MB output neuron (MBON) types elaborates segregated dendritic arbors along the parallel axons of similar to 2000 Kenyon cells, forming 15 compartments that collectively tile the MB lobes. MBON axons project to five discrete neuropils outside of the MB and three MBON types form a feedforward network in the lobes. Each of the 20 dopaminergic neuron (DAN) types projects axons to one, or at most two, of the MBON compartments. Convergence of DAN axons on compartmentalized Kenyon cell-MBON synapses creates a highly ordered unit that can support learning to impose valence on sensory representations. The elucidation of the complement of neurons of the MB provides a comprehensive anatomical substrate from which one can infer a functional logic of associative olfactory learning and memory

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

Le rôle des circuits et signalisations dopaminergiques dans l'apprentissage aversif de l'abeille Apis mellifera

Dans un apprentissage Pavlovien les animaux apprennent à associer un stimulus conditionné (SC) à un stimulus inconditionné (SI). L'abeille Apis mellifera est un modèle bien établi pour étudier cette forme d'apprentissage. Dans un nouveau protocole Pavlovien, les abeilles associent une substance odorante (SC) à un choc électrique (SI) et la réponse conditionnée produite à l'odorant appris est le reflexe d'extension du dard (RED). Cet apprentissage aversif dépend de l'amine biogène dopamine (DA) qui assure la médiation des propriétés de renforcement du choc électrique. Nous avons combiné plusieurs approches pour caractériser les circuits DA et leur signalisation dans le cerveau de l'abeille. Tout d'abord, nous avons étudié la réponse innée aversive et ses substrats en quantifiant le RED à une série de chocs de tension croissante et en injectant des antagonistes de neurotransmetteurs dans le cerveau. Nous avons trouvé que la DA et la sérotonine régulent negativement RED et le blocage de ces amines augmente la réponse. Nous avons fourni la première preuve de l'implication de ces amines biogènes dans le contrôle central de la réponse aux stimuli nocifs. Nos résultats proposent que différentes classes de neurones DA existent dans le cerveau de l'abeille: une classe " instructive " qui code pour l'aversion des stimuli conditionnés dans l'apprentissage associatif et une classe de " commande générale " qui diminue la réponse de la perception des stimuli nocifs. Ensuite, nous avons caractérisé la neuroanatomie des neurones DA dans le cerveau de l'abeille. Nous avons pratiqué l'immunocytochimie en appliquant un anticorps dirigé contre la tyrosine hydroxylase, un précurseur de la DA, et la microscopie confocale pour reconstruire les neurones DA en 3D. Nos résultats ont confirmé des données antérieures montrant de l'innervation dense dans un neuropile d'ordre supérieur, le corps pedonculés (MB) étant fortement innervé par 3 grands groupes de neurones DA. Des processus DA étaient également trouvés dans les lobes protocerebraux et les lobes antennaires. Ces neurones sont connectés au circuit olfactif à différentes étapes et constituent la base neuronale pour l'étiquetage DA de différentes formes de signalisation olfactive. Nous avons également trouvé la première preuve de l'existence de prolongements DA dans le lobe optique et identifié leur nouveau groupe DA. Enfin, nous avons étudié les mécanismes moléculaires qui sous-tendent la réponse aversive et l'apprentissage associatif en evaluant si l'apprentissage aversif induit des variations dans le niveau d'expression des gènes des récepteurs spécifiques ce qui modifie la sensibilité d'aversion aux stimuli aversifs. Nous avons quantifié le RED aux chocs avant un conditionnement et trois jours après le conditionnement, après la mesure de la mémoire d'aversion à long terme. Nous avons trouvé que l'apprentissage olfactif aversif induit une diminution à long terme de la réponse au choc. En utilisant la microdissection au laser, nous avons collecté des populations particulières de cellules de Kenyon puis mesuré les changements à long terme dans l'expression du récepteur du gène. Nous avons trouvé que l'apprentissage aversif seulement favorise une augmentation spécifique à long terme de l'expression des gènes du récepteur dopaminergique Amdop2 et partiellement d'Amdop1. Cette variation est corrélée avec la diminution à long terme de la réponse au choc résultant de l'apprentissage. Nos résultats proposent que les changements moléculaires spécifiques de l'expression du récepteur DA induisent une diminution de la réponse comportementale ce qui pourrait constituer une perte de l'"effet de surprise" résultant du conditionnement. Nos études couvrant les niveaux comportementale, pharmacologiques, neuroanatomiques et moléculaire, afin de fournir une nouvelle vision intégrée de la signalisation DA dans le cerveau de l'abeille c.à.d. de la représentation neural du SI aversif dans un cerveau d'insecte.In Pavlovian learning animals learn to associate a conditioned stimulus (CS) and an unconditioned stimulus (US). The honey bee Apis mellifera is a well-established model for the study of this learning form. In a novel Pavlovian protocol, bees associate an odorant (CS) with an electric shock (US) and the conditioned response produced to the learned odorant is the sting extension response. This aversive learning depends on the biogenic amine dopamine (DA), which mediates the reinforcing properties of the electric shock. Yet, the neural mechanisms and architecture underlying aversive US perception remain largely unknown. We combined behavioural, pharmacological, neuroanatomical and molecular approaches to characterize dopaminergic circuits and signalling in the bee brain. Firstly, we studied innate aversive responsiveness and its neurotransmitter basis by quantifying the sting extension reflex (SER) to a series of increasing voltages and injecting into the brain pharmacological antagonists of candidate neurotransmitters. We found that DA and serotonin act as down-regulators of SER so that blockade of these amines increased US responsiveness. We thus provided the first evidence of the involvement of these biogenic amines in the central control of sting responsiveness to noxious stimuli. Our results suggested that different classes of dopaminergic neurons exist in the bee brain: an instructive class mediating aversive conditioned stimuli in associative learning and a global gain-control class regulating responsiveness to noxious stimuli. Secondly, we characterized the neuroanatomy of DA neurons in the bee brain. We performed immunocytochemistry using an antibody against tyrosine hydroxylase, a DA precursor, and fluorescence confocal microscopy to obtain a 3D reconstruction of DA neurons. Our results confirmed prior reports showing dense innervations in the higher-order neuropil, the mushroom body (MB), which is heavily innervated by 3 relatively big clusters of DA neurons surrounding it. Smaller DA processes were also found in the protocerebral and antennal lobes. These DA neurons contact the olfactory circuit at different stages and provide the neural basis for DA involvement in olfactory signalling. We also provided the first evidence of previously unknown DA processes in the optic lobes and identified a novel DA cluster at the origin of these processes, which may provide instructive aversive signals for visual cues. Thirdly, we studied the molecular mechanisms underlying aversive responsiveness and associative learning to analyze whether aversive learning induces variations in the expression of specific receptor genes, thereby changing the responsiveness to punishment. We quantified the SER to shocks before a differential aversive conditioning and 3 days after conditioning, after measuring the presence of aversive long-term memory. We found that aversive olfactory learning induces a long-term decrease in shock responsiveness for shocks that were lower than the US. Using laser-capture micro dissection, we collected specific populations of MB's Kenyon cells and measured long-term changes in receptor-gene expression induced by aversive learning/retrieval. We found that aversive learning (but not retrieval) promotes a specific long-term increase in the expression of the dopaminergic receptor genes Amdop2 and partially Amdop1. This variation correlates with the long-term decrease in shock sensitivity resulting from learning. Our results suggest that specific molecular changes - here dopaminergic receptor expression - mediate a decrease in behavioural responsiveness to reinforcing stimuli that lose their 'surprising effect' as a consequence of conditioning. Our studies span a broad spectrum spanning from behavioural to molecular analyses and provide a novel, integrative view of dopaminergic signalling in the bee brain. They yield new insights into the neural/molecular representation of aversive US in an insect brain

Neural Organization of A3 Mushroom Body Extrinsic Neurons in the Honeybee Brain

In the insect brain, the mushroom body is a higher order brain area that is key to memory formation and sensory processing. Mushroom body (MB) extrinsic neurons leaving the output region of the MB, the lobes and the peduncle, are thought to be especially important in these processes. In the honeybee brain, a distinct class of MB extrinsic neurons, A3 neurons, are implicated in playing a role in learning. Their MB arborisations are either restricted to the lobes and the peduncle, here called A3 lobe connecting neurons, or they provide feedback information from the lobes to the input region of the MB, the calyces, here called A3 feedback neurons. In this study, we analyzed the morphology of individual A3 lobe connecting and feedback neurons using confocal imaging. A3 feedback neurons were previously assumed to innervate each lip compartment homogenously. We demonstrate here that A3 feedback neurons do not innervate whole subcompartments, but rather innervate zones of varying sizes in the MB lip, collar, and basal ring. We describe for the first time the anatomical details of A3 lobe connecting neurons and show that their connection pattern in the lobes resemble those of A3 feedback cells. Previous studies showed that A3 feedback neurons mostly connect zones of the vertical lobe that receive input from Kenyon cells of distinct calycal subcompartments with the corresponding subcompartments of the calyces. We can show that this also applies to the neck of the peduncle and the medial lobe, where both types of A3 neurons arborize only in corresponding zones in the calycal subcompartments. Some A3 lobe connecting neurons however connect multiple vertical lobe areas. Contrarily, in the medial lobe, the A3 neurons only innervate one division. We found evidence for both input and output areas in the vertical lobe. Thus, A3 neurons are more diverse than previously thought. The understanding of their detailed anatomy might enable us to derive circuit models for learning and memory and test physiological data

A decentralised neural model explaining optimal integration of navigational strategies in insects

Insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which each functions, and are then coordinated, remains unknown. We propose that insects require distinct strategies to retrace familiar routes (route-following) and directly return from novel to familiar terrain (homing) using different aspects of frequency encoded views that are processed in different neural pathways. We also demonstrate how the Central Complex and Mushroom Bodies regions of the insect brain may work in tandem to coordinate the directional output of different guidance cues through a contextually switched ring-attractor inspired by neural recordings. The resultant unified model of insect navigation reproduces behavioural data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information that they provide and coordinate their outputs to achieve the adaptive behaviours observed in the wild

Using an insect mushroom body circuit to encode route memory in complex natural environments

Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images

Abstract concept learning in a simple neural network inspired by the insect brain

The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of 'sameness' and 'difference' that is compatible with the insect brain, and is not dependent on top-down or executive control processing