Mushroom Body Extrinsic Neurons in Walking Bumblebees Correlate With Behavioral States but Not With Spatial Parameters During Exploratory Behavior

Central place foraging insects like honeybees and bumblebees learn to navigate efficiently between nest and feeding site. Essential components of this behavior can be moved to the laboratory. A major component of navigational learning is the active exploration of the test arena. These conditions have been used here to search for neural correlates of exploratory walking in the central arena (ground), and thigmotactic walking in the periphery (slope). We chose mushroom body extrinsic neurons (MBENs) because of their learning-related plasticity and their multi-modal sensitivities that may code relevant parameters in a brain state-dependent way. Our aim was to test whether MBENs code space-related components or are more involved in state-dependent processes characterizing exploration and thigmotaxis. MBENs did not respond selectively to body directions or locations. Their spiking activity differently correlated with walking speed depending on the animals’ locations: on the ground, reflecting exploration, or on the slope, reflecting thigmotaxis. This effect depended on walking speed in different ways for different animals. We then asked whether these effects depended on spatial parameters or on the two states, exploration and thigmotaxis. Significant epochs of stable changes in spiking did not correlate with restricted locations in the arena, body direction, or walking transitions between ground and slope. We thus conclude that the walking speed dependencies are caused by the two states, exploration and thigmotaxis, rather than by spatial parameters

Neural Correlates of Social Behavior in Mushroom Body Extrinsic Neurons of the Honeybee Apis mellifera

The social behavior of honeybees (Apis mellifera) has been extensively investigated, but little is known about its neuronal correlates. We developed a method that allowed us to record extracellularly from mushroom body extrinsic neurons (MB ENs) in a freely moving bee within a small but functioning mini colony of approximately 1,000 bees. This study aimed to correlate the neuronal activity of multimodal high-order MB ENs with social behavior in a close to natural setting. The behavior of all bees in the colony was video recorded. The behavior of the recorded animal was compared with other hive mates and no significant differences were found. Changes in the spike rate appeared before, during or after social interactions. The time window of the strongest effect on spike rate changes ranged from 1 s to 2 s before and after the interaction, depending on the individual animal and recorded neuron. The highest spike rates occurred when the experimental animal was situated close to a hive mate. The variance of the spike rates was analyzed as a proxy for high order multi-unit processing. Comparing randomly selected time windows with those in which the recorded animal performed social interactions showed a significantly increased spike rate variance during social interactions. The experimental set-up employed for this study offers a powerful opportunity to correlate neuronal activity with intrinsically motivated behavior of socially interacting animals. We conclude that the recorded MB ENs are potentially involved in initiating and controlling social interactions in honeybees

Eavesdropping on Honeybee Communication via Electrostatic Field Recordings

As a canary in a coalmine warns of dwindling breathable air, the honeybee can indicate the health of an ecosystem. Honeybees are the most important pollinators of fruit-bearing flowers, and share similar ecological niches with many other pollinators; therefore, the health of a honeybee colony can reflect the conditions of a whole ecosystem. The health of a colony may be mirrored in social signals that bees exchange during their sophisticated body movements such as the waggle dance. To observe these changes, we developed an automatic system that records and quantifies social signals under normal beekeeping conditions. Here, we describe the system and report representative cases of normal social behavior in honeybees. Our approach utilizes the fact that honeybee bodies are electrically charged by friction during flight and inside the colony, and thus they emanate characteristic electrostatic fields when they move their bodies. These signals, together with physical measurements inside and outside the colony (temperature, humidity, weight of the hive, and activity at the hive entrance) will allow quantification of normal and detrimental conditions of the whole colony. The information provided instructs how to setup the recording device, how to install it in a normal bee colony, and how to interpret its data

Neuronale Korrelate des sozial Verhaltens der Honigbiene

The honeybee (Apis mellifera) is an excellent model organism for social and navigational aspects of ethology. It is also heavily investigated with regard to neuronal circuits involved in vision, olfaction and memory. The aim of the current study is to correlate for the first time those peculiar and complex behaviours with the activity of neurons in certain brain areas. There is no knowledge yet on how social interactions are represented in the brain of an insect. To find answers in this regard, a mini colony of worker bees and one honeybee queen was situated in an arena. The behaviour within the artificial hive was monitored with a video camera by the aid of infra-red illumination. A custom written code was used to convert the video data into coordinates and head directions. For an experiment, a bee of interest was taken from the colony. The bee was then equipped with twisted one-meter-long two channel copper wire electrodes. The tips of the electrodes were plated with gold to lower the impedance by two orders of magnitude. Once the electrodes were situated at the beta exit of the mushroom body (MB), the electrodes were attached to the bee’s head using non-toxic silicone. After this procedure, the immobilised bee was placed back into the hive. We recorded the bee’s behaviour using a video and the neuronal activity with the extracellular setup. For the full duration of the experiment, the animal was free to move and behave as it does naturally. I could establish that the social behaviour of the whole colony is not restricted or modified in any way that might be of relevance in the current study. The bees exhibit, qualitatively, natural brood care, foraging waggle dancing, sleep and circadian rhythm. To some degree, the total amount or proportion does diverge due to the small number of initial worker bees of around 1 000. The extracellular recordings did not differ in any way from traditional recordings concerning the quality with regard to sortability and long-term stability for up to 24 hours. We found an overall low baseline activity when compared to experiments that were carried out with restrained honeybees. A multiverse analysis was created to search for any correlations between the neuronal activity of high-order MB output neurons and the behaviour extracted from the video recording. A result found over several of the successful experiments was an increase in spike rate variance for time windows in which social interactions occurred when compared to equal time windows in which the recorded bee was alone or random time windows. For the future I suggest to introduce a feeding machine into the arena that may motivate the recorded bee to show more repeated behaviour. Such devices can train bees in a classical or operant conditioning way.Die Honigbiene (Apis mellifera) ist ein ausgezeichneter Modellorganismus für soziale und navigatorische Aspekte der Ethologie. Es wird auch in Bezug auf neuronale Schaltkreise, die am She- und Geruchs-vermögen und am Gedächtnis beteiligt sind, intensiv untersucht. Das Ziel der vorliegenden Studie ist es, diese einzigartigen und komplexen Verhaltensweisen erstmals mit der Aktivität von Neuronen in bestimmten Hirnarealen zu korrelieren. Es gibt noch keine Erkenntnisse darüber, wie soziale Interaktionen im Gehirn eines Insekts representiert werden. Um diesbezüglich Antworten zu finden, befand sich in einer Arena eine Minikolonie von Arbeiterinnen und einer Honigbienenkönigin. Das Verhalten innerhalb des künstlichen Bienenstocks wurde mit Hilfe einer Videokamera und Infrarotbeleuchtung überwacht. Ein speziel hierfür geschriebener Code wurde verwendet, um die Videodaten in Koordinaten und Kopfrichtungen der Bienen umzuwandeln. Für ein Experiment wurde eine Biene von Interesse aus der Kolonie genommen. Die Biene wurde dann mit verdrillten, ein Meter langen, Zweikanal-Kupferdrahtelektroden ausgestattet. Die Spitzen der Elektroden wurden mit Gold plattiert, um die Impedanz um zwei Größenordnungen zu verringern. Sobald sich die Elektroden am Beta-Ausgang des Pilzkörpers (MB) befanden, wurden die Elektroden mit ungiftigem Silikon am Bienenkopf befestigt. Nach diesem Verfahren wurde die immobilisierte Biene in den Bienenstock zurückgebracht. Wir haben das Verhalten der Biene unter Verwendung eines Videos und die neuronalen Aktivität mit dem extrazellulären Aufbau aufgezeichnet. Während der gesamten Dauer des Experiments konnte sich das Tier frei bewegen und verhalten, wie es unter natürlichen Umständen der Fall ist. Ich konnte feststellen, dass das Sozialverhalten der gesamten Kolonie in keiner Weise eingeschränkt oder modifiziert wird, auf eine Weise die in der aktuellen Studie relevant sein könnte. Die Bienen zeigen qualitativ, natürliche Brutpflege, Schwänzeltanzen, Schlaf und zirkadianen Rhythmus. Bis zu einem gewissen Maß divergiert die Gesamtmenge oder der Gesamtanteil aufgrund der geringen Zahl von etwa 1 000 Arbeiterbienen. Die extrazellulären Aufzeichnungen unterschieden sich in keiner Weise von traditionellen Aufzeichnungen hinsichtlich der Qualität in Bezug auf Sortierbarkeit und Langzeitstabilität für bis zu 24 Stunden. Im Vergleich zu solchen klassischen Experimenten, fanden wir eine insgesamt niedrige Grundaktivität. Eine Multiverse- Analyse wurde erstellt, um nach Korrelationen zwischen der neuronalen Aktivität von MB-Ausgangsneuronen höherer Ordnung und dem aus der Videoaufzeichnung extrahierten Verhalten zu suchen. Ein Ergebnis, das in mehreren der Experimente gefunden wurde, war eine Zunahme der Spikeratenvarianz für Zeitfenster, in denen soziale Interaktionen im Vergleich zu gleichen Zeitfenstern auftraten, in denen die abgeleitete Biene alleine war oder zufällige Zeitfenster. Für die Zukunft schlage ich vor, eine Futtermaschine in die Arena zu bringen, die die abgeleitete Biene dazu motivieren kann, mehr wiederholtes Verhalten zu zeigen. Solche Vorrichtungen können Bienen in einer klassischen oder operanten Konditionierungsweise trainieren

Low cost open source hardware and software in behavioral and electrophysiological experiments: Arduino & Raspberry Pi

Abstract Behavioral science can be accelerated greatly by the use of digitally controlled devices capable of measuring relevant parameters and, if necessary, interact with the experiment. Those machines can be bought and they can do things in an implemented way. They are however neither cheap nor as adaptive as they should be. The focus of this poster is to demonstrate how to overcome the rather small barriers in order to develop their own machines. Recently, open hardware projects like the Arduino or the Raspberry Pi draw enormous community coding and building elements that can be fused together to fit the experimenters needs. We as scientists can benefit from large numbers of projects that are documented well in the internet, mostly by tutorial videos including component lists and code. Here we present a number of simple suggestions how to build yourself helpful devices for your behavioral and neurophysiological experiments

Efficient visual learning by bumble bees in virtual‐reality conditions: Size does not matter

International audienceRecent developments allowed establishing virtual‐reality (VR) setups to study multiple aspects of visual learning in honey bees under controlled experimental conditions. Here, we adopted a VR environment to investigate the visual learning in the buff‐tailed bumble bee Bombus terrestris . Based on responses to appetitive and aversive reinforcements used for conditioning, we show that bumble bees had the proper appetitive motivation to engage in the VR experiments and that they learned efficiently elemental color discriminations. In doing so, they reduced the latency to make a choice, increased the proportion of direct paths toward the virtual stimuli and walked faster toward them. Performance in a short‐term retention test showed that bumble bees chose and fixated longer on the correct stimulus in the absence of reinforcement. Body size and weight, although variable across individuals, did not affect cognitive performances and had a mild impact on motor performances. Overall, we show that bumble bees are suitable experimental subjects for experiments on visual learning under VR conditions, which opens important perspectives for invasive studies on the neural and molecular bases of such learning given the robustness of these insects and the accessibility of their brain

A Flying Platform to Investigate Neuronal Correlates of Navigation in the Honey Bee (Apis mellifera)

Navigating animals combine multiple perceptual faculties, learn during exploration, retrieve multi-facetted memory contents, and exhibit goal-directedness as an expression of their current needs and motivations. Navigation in insects has been linked to a variety of underlying strategies such as path integration, view familiarity, visual beaconing, and goal-directed orientation with respect to previously learned ground structures. Most works, however, study navigation either from a field perspective, analyzing purely behavioral observations, or combine computational models with neurophysiological evidence obtained from lab experiments. The honey bee (Apis mellifera) has long been a popular model in the search for neural correlates of complex behaviors and exhibits extraordinary navigational capabilities. However, the neural basis for bee navigation has not yet been explored under natural conditions. Here, we propose a novel methodology to record from the brain of a copter-mounted honey bee. This way, the animal experiences natural multimodal sensory inputs in a natural environment that is familiar to her. We have developed a miniaturized electrophysiology recording system which is able to record spikes in the presence of time-varying electric noise from the copter's motors and rotors, and devised an experimental procedure to record from mushroom body extrinsic neurons (MBENs). We analyze the resulting electrophysiological data combined with a reconstruction of the animal's visual perception and find that the neural activity of MBENs is linked to sharp turns, possibly related to the relative motion of visual features. This method is a significant technological step toward recording brain activity of navigating honey bees under natural conditions. By providing all system specifications in an online repository, we hope to close a methodological gap and stimulate further research informing future computational models of insect navigation

The Electronic Bee Spy: Eavesdropping on Honeybee Communication via Electrostatic Field Recordings

As a canary in a coalmine warns of dwindling breathable air, the honeybee can indicate the health of an ecosystem. Honeybees are the most important pollinators of fruit-bearing flowers, and share similar ecological niches with many other pollinators; therefore, the health of a honeybee colony can reflect the conditions of a whole ecosystem. The health of a colony may be mirrored in social signals that bees exchange during their sophisticated body movements such as the waggle dance. To observe these changes, we developed an automatic system that records and quantifies social signals under normal beekeeping conditions. Here, we describe the system and report representative cases of normal social behavior in honeybees. Our approach utilizes the fact that honeybee bodies are electrically charged by friction during flight and inside the colony, and thus they emanate characteristic electrostatic fields when they move their bodies. These signals, together with physical measurements inside and outside the colony (temperature, humidity, weight of the hive, and activity at the hive entrance) will allow quantification of normal and detrimental conditions of the whole colony. The information provided instructs how to setup the recording device, how to install it in a normal bee colony, and how to interpret its data