Biology subjects

Pfeiffer, K.

Publications and source records attributed to Pfeiffer, K..

10 recordsLinked to original sources

bioRxiv2025

Honeybees acquire a spatial memory by learning landmarks and visual patterns in parallel

Spatial memory is vital for central-place foragers that navigate the same habitat on a daily basis. Computational models suggest that insects remember panoramic snapshots to return to goal locations. To what extent these snapshots represent retinotopic images or whether salient landmarks are stored isolated from the panorama remains unclear. With a newly designed spatial-memory task, we show that honeybees employ two different strategies for learning visual cues. In essence, they learn visual patterns as panoramic snapshots and three-dimensional objects as single entities. Freely walking honeybees were challenged to locate a rewarded feeder in the presence of visual patterns or objects. Selectively perturbing the panorama degraded the orientation behaviour only when visual patterns were present but not when objects were available. Presenting visual patterns and objects simultaneously and setting these cues in conflict revealed that honeybees primarily rely on a landmark-based navigation strategy. Making the spatial information of the objects ambiguous did not induce disorientation because the honeybees then relied on the visual patterns for finding the feeder. Altogether, our results suggest two independent visual memories operating in parallel, one dedicated to local landmarks and a second one for visual patterns that could be interpreted as distal cues. Notably, in rats, the spatial tuning of some hippocampal cells has been shown to be either anchored to local or distal cues. Our results provide the first hints that a distinction between local and distal cues may also be present in the insect brain.

animal behavior and cognition↗

bioRxiv2025

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and celltype annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate celltype annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

bioinformatics↗

bioRxiv2024

History-dependent spiking facilitates efficient encoding of polarization angles in neurons of the central complex

Many insects use the polarization pattern of the sky for spatial orientation. Since flying insects perform rapid maneuvers, including saccadic yaw turns which alternate with translational flight, they perceive highly dynamic polarization input to their navigation system. The tuning of compass-neurons in the central complex of insects, however, has been mostly investigated with polarized-light stimuli that rotated at slow and constant velocities, and thus were lacking these natural dynamics. Here we investigated the dynamic response properties of compass-neurons, using intracellular recordings in the central complex of bumblebees. We generated naturalistic stimuli by rotating a polarizer either according to a sequence of head orientations that have been reported from freely flying bumblebees, or at constant velocities between 30{degrees}/s and 1920{degrees}/s, spanning almost the entire range of naturally occurring rotation velocities. We found that compass neurons responded reliably across the entire range of the presented stimuli. In their responses, we observed a dependency on spiking history. We further investigated this dependency using a rate code model taking spiking history into account. Extending the model to a neuronal population with different polarization tuning, which mirrored the neuronal architecture of the central complex, suggests that spiking history has a directly impact on the overall population activity, which has two effects: First, it facilitates faster responses to stimulus changes during highly dynamic flight maneuvers, and increases sensitivity for course deviations during straight flight. Second, population activity during phases of constant polarization input is reduced, which might conserve energy during straight flight. Significance StatementMany insects use the pattern of polarized light, which arises from scattering of sunlight, for spatial orientation. Neurons in the central complex, a brain structure, which governs spatial orientation, encode the orientation of the insects head with respect to polarized light. To investigate the dynamic properties of these neurons, we recorded intracellularly in the central complex of bumblebees and stimulated with naturalistic polarized-light stimuli. We found that neuronal activity was not only dictated by the angle of polarization, but also by the amount of previous activity. Using a modelling approach we show, that this dependency on spiking history can facilitate faster adjustment of the heading signal and leads to reduced overall activity and therefore reduced energy consumption during straight flight.

neuroscience↗

bioRxiv2022

Walking bumblebees see faster

The behavioral state of an animal has profound effects on neuronal information processing. Locomotion changes the response properties of visual interneurons in the insect brain, but it is still unknown if it also alters the response properties of photoreceptors. Photoreceptor responses become faster at higher temperatures. It has therefore been suggested that thermoregulation in bees and other insects could improve temporal resolution in vision, but direct evidence for this idea has so far been missing. Here we compared electroretinograms (ERGs) from the compound eyes of tethered bumblebees that were either sitting still or were walking on an air supported ball. We found that the visual processing speed strongly increased when the bumblebees were walking. By monitoring the bumblebees eye temperature during our recordings, we saw that the increase in response speed was in synchrony with a rise of the eye temperature. By artificially heating the bees head, we show that the walking induced temperature increase of the visual system is sufficient to explain the rise in processing speed. We also show that walking accelerates the visual system to the equivalent of a 14-fold increase in light intensity. We conclude that the walking-induced rise in temperature accelerates the processing of visual information in bumblebees, which is an ideal strategy to process the increased information flow during locomotion.

neuroscience↗

bioRxiv2022

Weighting of celestial and terrestrial cues in the monarch butterfly central complex

Monarch butterflies rely on external cues for orientation during their annual long-distance migration from Northern US and Canada to Central Mexico. These external cues can be celestial cues, such as the sun or polarized light, which are processed in the internal compass of the brain, termed the central complex (CX). Previous research typically focused on how individual simulated celestial cues are encoded in the butterflys CX. However, in nature, the butterflies perceive several celestial cues at the same time and need to integrate them to effectively use the compound of all cues for orientation. In addition, a recent behavioral study revealed that monarch butterflies can rely on terrestrial cues, such as the panoramic skyline, for orientation and use them in combination with the sun to maintain a directed flight course. How the CX encodes a combination of celestial and terrestrial cues and how they are weighted in the butterflys CX is still unknown. Here, we examined how input neurons of the CX, termed TL neurons, combine celestial and terrestrial information. While recording intracellularly from the neurons, we presented a sun stimulus and polarized light to the butterflies as well as a simulated sun and a panoramic scene simultaneously. Our results show that celestial cues are integrated linearly in these cells, while the combination of the sun and a panoramic skyline did not always follow a linear integration of action potential rates. Interestingly, while the weighting between the sun and polarized light was invariant between individual input neurons, it varied strongly when the sun stimulus and the panoramic skyline were presented simultaneously. Taken together, this dynamic weighting between celestial and terrestrial cues may allow the butterflies to flexibly set their cue preference during navigation.

neuroscience↗

bioRxiv2021

Stimulus-dependent orientation strategies in monarch butterflies

Insects are well-known for their ability to keep track of their heading direction based on a combination of skylight cues and visual landmarks. This allows them to navigate back to their nest, disperse throughout unfamiliar environments, as well as migrate over large distances between their breeding and non-breeding habitats. The monarch butterfly (Danaus plexippus) for instance is known for its annual southward migration from North America to certain trees in Central Mexico. To maintain a constant flight route, these butterflies use a time-compensated sun compass for orientation which is processed in a region in the brain, termed the central complex. However, to successfully complete their journey, the butterflies brain must generate a multitude of orientation strategies, allowing them to dynamically switch from sun-compass orientation to a tactic behavior toward a certain target. To study if monarch butterflies exhibit different orientation modes and if they can switch between them, we observed the orientation behavior of tethered flying butterflies in a flight simulator while presenting different visual cues to them. We found that the butterflies behavior depended on the presented visual stimulus. Thus, while a dark stripe was used for flight stabilization, a bright stripe was fixated by the butterflies in their frontal visual field. If we replaced a bright stripe by a simulated sun stimulus, the butterflies switched their orientation behavior and exhibited compass orientation. Taken together, our data show that monarch butterflies rely on and switch between different orientation modes, allowing them to adjust orientation to the actual behavioral demands of the animal.

animal behavior and cognition↗

bioRxiv2021

Interactive, visual simulation of a spatio-temporal model of gas exchange in the human alveolus

In interdisciplinary fields such as systems biology, close collaboration between experimentalists and theorists is crucial for the success of a project. Theoretical modeling in physiology usually describes complex systems with many interdependencies. On one hand, these models have to be grounded on experimental data. On the other hand, experimenters must be able to penetrate the model in its dependencies in order to correctly interpret the results in the physiological context. When theorists and experimenters collaborate, communicating results and ideas is sometimes challenging. We promote interactive, visual simulations as an engaging way to communicate theoretical models in physiology and to thereby advance our understanding of the process of interest. We defined a new spatio-temporal model for gas exchange in the human alveolus and implemented it in an interactive simulation software named Alvin. In Alvin, the course of the simulation can be traced in a three-dimensional rendering of an alveolus and dynamic plots. The user can interact by configuring essential model parameters. Alvin allows to run and compare multiple simulation instances simultaneously. The mathematical model was developed with the aim of visualization and the simulation software was engineered based on a requirements analysis. Our work resulted in an integrative gas exchange model and an interactive application that exceed the current standards. We exemplified the use of Alvin for research by identifying unknown dependencies in published experimental data. Employing a detailed questionnaire, we showed the benefits of Alvin for education. We postulate that interactive, visual simulation of theoretical models, as we have implemented with Alvin on respiratory processes in the alveolus, can be of great help for communication between specialists and thereby advancing research.

bioinformatics↗

bioRxiv2020

InsectBrainDatabase - A unified platform to manage, share, and archive morphological and functional data

Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable, despite open data mandates by funding bodies. We have therefore developed the InsectBrainDatabase (IBdb), an open platform for depositing, sharing and managing a wide range of insect neuroanatomical and functional data. It facilities biological insight by enabling effective cross-species comparisons, by intimately linking data on structure and function, and by serving as hub for information on insect neuroethology. The IBdb provides novel visualization and search tools, which are also available in a unique private mode of the database, before data is made public. This allows users to manage and visualize unpublished data, creating a strong incentive for data contribution and eliminating additional effort when publicly depositing the data at a later stage. These design principles could also serve as a blueprint for similar databases in other fields.

neuroscience↗

bioRxiv2020

Spatial orientation based on multiple visual cues in monarch butterflies

Monarch butterflies (Danaus plexippus) are prominent for their annual long-distance migration from North America to its overwintering area in Central Mexico. To find their way on this long journey, they use a sun compass as their main orientation reference but will also adjust their migratory direction with respect to mountain ranges. This indicates that the migratory butterflies also attend to the panorama to guide their travels. Here we studied if non-migrating butterflies - that stay in a more restricted area to feed and breed - also use a similar compass system to guide their flights. Performing behavioral experiments on tethered flying butterflies in an indoor LED flight simulator, we found that the monarchs fly along straight tracks with respect to a simulated sun. When a panoramic skyline was presented as the only orientation cue, the butterflies maintained their flight direction only during short sequences suggesting that they potentially use it for flight stabilization. We further found that when we presented the two cues together, the butterflies register both cues in their compass. Taken together, we here show that non-migrating monarch butterflies can combine multiple visual cues for robust orientation, an ability that may also aid them during their migration. SummaryNon-migrating butterflies keep directed courses when viewing a simulated sun or panoramic scene. This suggest that they orient based on multiple visual cues independent of their migratory context.

animal behavior and cognition↗

bioRxiv2019

The brain of a nocturnal migratory insect, the Australian Bogong moth

Every year, millions of Australian Bogong moths (Agrotis infusa) complete an astonishing journey: in spring, they migrate over 1000 km from their breeding grounds to the alpine regions of the Snowy Mountains, where they endure the hot summer in the cool climate of alpine caves. In autumn, the moths return to their breeding grounds, where they mate, lay eggs and die. These moths can use visual cues in combination with the geomagnetic field to guide their flight, but how these cues are processed and integrated in the brain to drive migratory behavior is unknown. To generate an access point for functional studies, we provide a detailed description of the Bogong moths brain. Based on immunohistochemical stainings against synapsin and serotonin (5HT), we describe the overall layout as well as the fine structure of all major neuropils, including the regions that have previously been implicated in compass-based navigation. The resulting average brain atlas consists of 3D reconstructions of 25 separate neuropils, comprising the most detailed account of a moth brain to date. Our results show that the Bogong moth brain follows the typical lepidopteran ground pattern, with no major specializations that can be attributed to their spectacular migratory lifestyle. These findings suggest that migratory behavior does not require widespread modifications of brain structure, but might be achievable via small adjustments of neural circuitry in key brain areas. Locating these subtle changes will be a challenging task for the future, for which our study provides an essential anatomical framework.

neuroscience↗