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The behavior of honeybees and bumblebees relies on a constant sensory integration of abiotic or biotic stimuli. As eusocial insects, a sophisticated intraspecific communication as well as the processing of multisensory cues during foraging is of utter importance. To tackle the arising challenges, both honeybees and bumblebees have evolved a sophisticated olfactory and visual processing system.
In both organisms, olfactory reception starts at the antennae, where olfactory sensilla cover the antennal surface in a sex-specific manner. These sensilla house olfactory receptor neurons (ORN) that express olfactory receptors. ORNs send their axons via four tracts to the antennal lobe (AL), the prime olfactory processing center in the bee brain. Here, ORNs specifically innervate spheroidal structures, so-called glomeruli, in which they form synapses with local interneurons and projection neurons (PN). PNs subsequently project the olfactory information via two distinct tracts, the medial and the lateral antennal-lobe tract, to the mushroom body (MB), the main center of sensory integration and memory formation. In the honeybee calyx, the sensory input region of the MB, PNs synapse on Kenyon cells (KC), the principal neuron type of the MB. Olfactory PNs mainly innervate the lip and basal ring layer of the calyx. In addition, the basal ring receives input from visual PNs, making it the first site of integration of visual and olfactory information. Visual PNs, carrying sensory information from the optic lobes, send their terminals not only to the to the basal ring compartment but also to the collar of the calyx. Receiving olfactory or visual input, KCs send their axons along the MB peduncle and terminate in the main output regions of the MB, the medial and the vertical lobe (VL) in a layer-specific manner. In the MB lobes, KCs synapse onto mushroom body output neurons (MBON). In so far barely understood processes, multimodal information is integrated by the MBONs and then relayed further into the protocerebral lobes, the contralateral brain hemisphere, or the central brain among others.
This dissertation comprises a dichotomous structure that (i) aims to gain more insight into the olfactory processing in bumblebees and (ii) sets out to broaden our understanding of visual processing in honeybee MBONs.
The first manuscript examines the olfactory processing of Bombus terrestris and specifically investigates sex-specific differences. We used behavioral (absolute conditioning) and electrophysiological approaches to elaborate the processing of ecologically relevant odors (components of plant odors and pheromones) at three distinct levels, in the periphery, in the AL and during olfactory conditioning. We found both sexes to form robust memories after absolute conditioning and to generalize towards the carbon chain length of the presented odors. On the contrary, electroantennographic (EAG) activity showed distinct stimulus and sex-specific activity, e.g. reduced activity towards citronellol in drones. Interestingly, extracellular multi-unit recordings in the AL confirmed stimulus and sex-specific differences in olfactory processing, but did not reflect the differences previously found in the EAG. Here, farnesol and 2,3-dihydrofarnesol, components of sex-specific pheromones, show a distinct representation, especially in workers, corroborating the results of a previous study. This explicitly different representation suggests that the peripheral stimulus representation is an imperfect indication for neuronal representation in high-order neuropils and ecological importance of a specific odor.
The second manuscript investigates MBONs in honeybees to gain more insights into visual processing in the VL. Honeybee MBONs can be categorized into visually responsive, olfactory responsive and multimodal. To clarify which visual features are represented at this high-order integration center, we used extracellular multi-unit recordings in combination with visual and olfactory stimulation. We show for the first time that information about brightness and wavelength is preserved in the VL. Furthermore, we defined three specific classes of visual MBONs that distinctly encode the intensity, identity or simply the onset of a stimulus. The identity-subgroup exhibits a specific tuning towards UV light. These results support the view of the MB as the center of multimodal integration that categorizes sensory input and subsequently channels this information into specific MBON populations.
Finally, I discuss differences between the peripheral representations of stimuli and their distinct processing in high-order neuropils. The unique activity of farnesol in manuscript 1 or the representation of UV light in manuscript 2 suggest that the peripheral representation of a stimulus is insufficient as a sole indicator for its neural activity in subsequent neuropils or its putative behavioral importance. In addition, I discuss the influence of hard-wired concepts or plasticity induced changes in the sensory pathways on the processing of such key stimuli in the peripheral reception as well as in high-order centers like the AL or the MB. The MB as the center of multisensory integration has been broadly examined for its olfactory processing capabilities and receives increasing interest about its visual coding properties. To further unravel its role of sensory integration and to include neglected modalities, future studies need to combine additional approaches and gain more insights on the multimodal aspects in both the input and output region.
The present study examined the perceptual consequences of learning arbitrary mappings between visual stimuli and hand movements. Participants moved a small cursor with their unseen hand twice to a large visual target object and then judged either the relative distance of the hand movements (Exp.1), or the relative number of dots that appeared in the two consecutive target objects (Exp.2) using a two-alternative forced choice method. During a learning phase, the numbers of dots that appeared in the target object were correlated with the hand movement distance. In Exp.1, we observed that after the participants were trained to expect many dots with larger hand movements, they judged movements made to targets with many dots as being longer than the same movements made to targets with few dots. In Exp.2, another group of participants who received the same training judged the same number of dots as smaller when larger rather than smaller hand movements were executed. When many dots were paired with smaller hand movements during the learning phase of both experiments, no significant changes in the perception of movements and of visual stimuli were observed. These results suggest that changes in the perception of body states and of external objects can arise when certain body characteristics co-occur with certain characteristics of the environment. They also indicate that the (dis)integration of multimodal perceptual signals depends not only on the physical or statistical relation between these signals, but on which signal is currently attended.
To safely navigate their environment, flying insects rely on visual cues, such as optic flow. Which cues insects can extract from their environment depends closely on the spatial and temporal response properties of their visual system. These in turn can vary between individuals that differ in body size. How optic flow-based flight control depends on the spatial structure of visual cues, and how this relationship scales with body size, has previously been investigated in insects with apposition compound eyes. Here, we characterised the visual flight control response limits and their relationship to body size in an insect with superposition compound eyes: the hummingbird hawkmoth Macroglossum stellatarum. We used the hawkmoths' centring response in a flight tunnel as a readout for their reception of translational optic flow stimuli of different spatial frequencies. We show that their responses cut off at different spatial frequencies when translational optic flow was presented on either one, or both tunnel walls. Combined with differences in flight speed, this suggests that their flight control was primarily limited by their temporal rather than spatial resolution. We also observed strong individual differences in flight performance, but no correlation between the spatial response cutoffs and body or eye size.
Rhodopsins are the major photopigments in the fruit fly Drosophila melanogaster. Drosophila express six well-characterized Rhodopsins (Rh1–Rh6) with distinct absorption maxima and expression pattern. In 2000, when the Drosophila genome was published, a novel Rhodopsin gene was discovered: Rhodopsin 7 (Rh7). Rh7 is highly conserved among the Drosophila genus and is also found in other arthropods. Phylogenetic trees based on protein sequences suggest that the seven Drosophila Rhodopsins cluster in three different groups. While Rh1, Rh2 and Rh6 form a “vertebrate-melanopsin-type”–cluster, and Rh3, Rh4 and Rh5 form an “insect-type”-Rhodopsin cluster, Rh7 seem to form its own cluster. Although Rh7 has nearly all important features of a functional Rhodopsin, it differs from other Rhodopsins in its genomic and structural properties, suggesting it might have an overall different role than other known Rhodopsins.
Mushroom bodies (MBs) are multisensory integration centers in the insect brain involved in learning and memory formation. In the honeybee, the main sensory input region (calyx) of MBs is comparatively large and receives input from mainly olfactory and visual senses, but also from gustatory/tactile modalities. Behavioral plasticity following differential brood care, changes in sensory exposure or the formation of associative long-term memory (LTM) was shown to be associated with structural plasticity in synaptic microcircuits (microglomeruli) within olfactory and visual compartments of the MB calyx. In the same line, physiological studies have demonstrated that MB-calyx microcircuits change response properties after associative learning. The aim of this review is to provide an update and synthesis of recent research on the plasticity of microcircuits in the MB calyx of the honeybee, specifically looking at the synaptic connectivity between sensory projection neurons (PNs) and MB intrinsic neurons (Kenyon cells). We focus on the honeybee as a favorable experimental insect for studying neuronal mechanisms underlying complex social behavior, but also compare it with other insect species for certain aspects. This review concludes by highlighting open questions and promising routes for future research aimed at understanding the causal relationships between neuronal and behavioral plasticity in this charismatic social insect.
The aim of this review is to use the multimedia aspects
of a purely digital online publication to explain and
illustrate the highly capable technique of m-sequences in
multifocal ophthalmic electrophysiology. M-sequences
have been successfully applied in clinical routines during
the past 20 years. However, the underlying mathematical
rationale is often daunting. These mathematical
properties of m-sequences allow one not only to
separate the responses from different fields but also to
analyze adaptational effects and impacts of former
events. By explaining the history, the formation, and the
different aspects of application, a better comprehension
of the technique is intended. With this review we aim to
clarify the opportunities of m-sequences in order to
motivate scientists to use m-sequences in their future
research.
A visual stimulus at a particular location of the visual field may elicit a behavior while at the same time equally salient stimuli in other parts do not. This property of visual systems is known as selective visual attention (SVA). The animal is said to have a focus of attention (FoA) which it has shifted to a particular location. Visual attention normally involves an attention span at the location to which the FoA has been shifted. Here the attention span is measured in Drosophila. The fly is tethered and hence has its eyes fixed in space. It can shift its FoA internally. This shift is revealed using two simultaneous test stimuli with characteristic responses at their particular locations. In tethered flight a wild type fly keeps its FoA at a certain location for up to 4s. Flies with a mutation in the radish gene, that has been suggested to be involved in attention-like mechanisms, display a reduced attention span of only 1s.
Visual Attention in Flies-Dopamine in the Mushroom Bodies Mediates the After-Effect of Cueing
(2016)
Visual environments may simultaneously comprise stimuli of different significance. Often such stimuli require incompatible responses. Selective visual attention allows an animal to respond exclusively to the stimuli at a certain location in the visual field. In the process of establishing its focus of attention the animal can be influenced by external cues. Here we characterize the behavioral properties and neural mechanism of cueing in the fly Drosophila melanogaster. A cue can be attractive, repulsive or ineffective depending upon (e.g.) its visual properties and location in the visual field. Dopamine signaling in the brain is required to maintain the effect of cueing once the cue has disappeared. Raising or lowering dopamine at the synapse abolishes this after-effect. Specifically, dopamine is necessary and sufficient in the αβ-lobes of the mushroom bodies. Evidence is provided for an involvement of the αβ\(_{posterior}\) Kenyon cells.
It has been argued that several reported non-visual influences on perception cannot be truly perceptual. If they were, they should affect the perception of target objects and reference objects used to express perceptual judgments, and thus cancel each other out. This reasoning presumes that non-visual manipulations impact target objects and comparison objects equally. In the present study we show that equalizing a body-related manipulation between target objects and reference objects essentially abolishes the impact of that manipulation so as it should do when that manipulation actually altered perception. Moreover, the manipulation has an impact on judgements when applied to only the target object but not to the reference object, and that impact reverses when only applied to the reference object but not to the target object. A perceptual explanation predicts this reversal, whereas explanations in terms of post-perceptual response biases or demand effects do not. Altogether these results suggest that body-related influences on perception cannot as a whole be attributed to extra-perceptual factors.
Honeybees learn color information of rewarding flowers and recall these memories in future decisions. For fine color discrimination, bees require differential conditioning with a concurrent presentation of target and distractor stimuli to form a long-term memory. Here we investigated whether the long-term storage of color information shapes the neural network of microglomeruli in the mushroom body calyces and if this depends on the type of conditioning. Free-flying honeybees were individually trained to a pair of perceptually similar colors in either absolute conditioning towards one of the colors or in differential conditioning with both colors. Subsequently, bees of either conditioning groups were tested in non-rewarded discrimination tests with the two colors. Only bees trained with differential conditioning preferred the previously learned color, whereas bees of the absolute conditioning group, and a stimuli-naïve group, chose randomly among color stimuli. All bees were then kept individually for three days in the dark to allow for complete long-term memory formation. Whole-mount immunostaining was subsequently used to quantify variation of microglomeruli number and density in the mushroom-body lip and collar. We found no significant differences among groups in neuropil volumes and total microglomeruli numbers, but learning performance was negatively correlated with microglomeruli density in the absolute conditioning group. Based on these findings we aim to promote future research approaches combining behaviorally relevant color learning tests in honeybees under free-flight conditions with neuroimaging analysis; we also discuss possible limitations of this approach.q