Biology subjects

Gkanias, E.

Publications and source records attributed to Gkanias, E..

5 recordsLinked to original sources

bioRxiv2026

From eye anatomy to navigation: a biologically accurate model of bees polarisation vision

Skylight polarisation patterns provide a critical navigational cue for many insects. Bees perceive these patterns through specialised ommatidia in the dorsal rim area of their compound eyes, enabling them to estimate the suns direction and navigate between food sources and the hive. Although polarisation-based navigation has been extensively studied behaviourally, computational models that link DRA anatomy with navigational performance are lacking. Here, we simulate polarisation vision in honeybees (Apis mellifera) and bumblebees (Bombus terrestris) using real sky polarisation images to capture biologically relevant skylight properties. Our biologically grounded simulation incorporates species-specific DRA anatomy, including ommatidial optical axis directions, photoreceptor receptive fields, and microvillar orientations. We evaluate navigational accuracy and consistency across sun elevations under two distinct, potentially complementary navigational models: the matched filter, which requires scanning across body orientations to identify the solar axis, and the vector-sum model, which generates instantaneous sun azimuth estimates from a single body orientation, making it independent of active scanning. Matched filter errors in estimating solar axis are below 5{degrees} across most sun elevations and in both species. Absolute errors in the vector-sum model are lower for honeybees than bumblebees (median [~]10{degrees} and [~]30{degrees}, respectively), reflecting differences in DRA anatomy, particularly viewing direction and microvillar arrangement. Both models allow stable course control across most sun elevations in both species, yet the matched filter, being limited to solar axis alignment, only enables positive or negative phototaxis. Overall, this work provides a mechanistic and comparative framework based on realistic DRA anatomy to study polarisation-based navigation, generating testable predictions for insect navigation under natural sky conditions. Author SummaryMany insects, including bees, navigate with the help of skylight polarisation patterns which hold information about the suns position even when it is not visible. Bees detect these patterns through the dorsal rim area (DRA) of their complex eyes. How differences in DRA anatomy between bee species translate into differences in navigational ability has remained unclear. Here, we built a biologically realistic simulation of polarisation vision in honeybees and bumblebees. We used real sky images to examine what polarisation information is available to each species. We then tested two models of sun position estimation based on the polarisation pattern: one that requires the bee to actively scan the sky, and one that generates an instantaneous estimate from a single body orientation. In both species, both models show that accurate sun position estimation and stable navigation are possible using just polarisation information under a wide range of sun elevations. Differences in navigational performance between honeybees and bumblebees arise because the two DRAs look at different parts of the sky. Our results provide a robust framework for understanding how DRA anatomy shapes polarisation-based navigation in bees.

neuroscience↗

bioRxiv2024

A computational model for angular velocity integration in a locust heading circuit

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We propose a novel model for angular velocity integration to update the representation of heading in the central complex of the desert locust. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360{degrees} heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust. Author summaryIn both fruit flies and locusts, a specific brain region has been observed to have an activity pattern that resembles a compass, with an activity peak moving across an array of neurons as the animal rotates through 360 degrees. However, some apparent differences in the properties of this pattern between the two species suggest there may be differences in how this internal compass is implemented. Here we focus on the locust brain, building a computational model that is based on observed neural connections and using machine learning to tune the system. Turning by the simulated locust provides modulatory input to the neural circuit that keeps activity in the array aligned to its heading direction. We simulate a migrating locust that tries to keep the same heading despite perturbances and show this circuit can steer it back on course. Our model differs from existing models of the fruit fly compass, showing how similar computations could have different implementations in different species. O_TBL View this table: org.highwire.dtl.DTLVardef@109a8b3org.highwire.dtl.DTLVardef@12298eborg.highwire.dtl.DTLVardef@65a654org.highwire.dtl.DTLVardef@18ae048org.highwire.dtl.DTLVardef@8ace20_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1.C_FLOATNO O_TABLECAPTIONAbbreviations for neuron types and brain regions in the desert locust (Schistocerca gregaria) and homologues in the fruit fly (Drosophila melanogaster). C_TABLECAPTION C_TBL

neuroscience↗

bioRxiv2021

The incentive circuit: memory dynamics in the mushroom body of Drosophila melanogaster

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short-term to long-term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

neuroscience↗

bioRxiv2019

How do backward walking ants (Cataglyphis velox) cope with navigational uncertainty?

Current opinion in insect navigation assumes that animals need to align with the goal direction to recognise familiar views and approach it. Yet, ants sometimes drag heavy food items backward to the nest and it is still unclear to what extent they rely on visual memories while doing so. In this study displacement experiments and alterations of the visual scenery reveal that ants do indeed recognise and use the learnt visual scenery to guide their path while walking backward. In addition, the results show that backward homing ants estimate their directional certainty by combining visual familiarity with other cues such as their path integrator and the time spent backward. A simple model that combines path integration with repulsive and attractive visual memories captures the results.

animal behavior and cognition↗

bioRxiv2018

From skylight input to behavioural output: a computational model of the insect polarised light compass

Many insects navigate by integrating the distances and directions travelled on an outward path, allowing direct return to the starting point. Fundamental to the reliability of this process is the use of a neural compass based on external celestial cues. Here we examine how such compass information could be reliably computed by the insect brain, given realistic constraints on the sky polarisation pattern and the insect eye sensor array. By processing the degree of polarisation in different directions for different parts of the sky, our model can directly estimate the solar azimuth and also infer the confidence of the estimate. We introduce a method to correct for tilting of the sensor array, as might be caused by travel over uneven terrain. We also show that the confidence can be used to approximate the change in sun position over time, allowing the compass to remain fixed with respect to true north during long excursions. We demonstrate that the compass is robust to disturbances and can be effectively used as input to an existing neural model of insect path integration. We discuss the plausibility of our model to be mapped to known neural circuits, and to be implemented for robot navigation.\n\nAuthor summaryWe propose a new hypothesis for how insects process polarised skylight to extract global orientation information that can be used for accurate path integration. Our model solves the problem of solar/anti-solar meridian ambiguity by using a biologically constrained sensor array, and includes methods to deal with tilt and time, providing a complete insect celestial compass output. We analyse the performance of the model using a realistic sky simulation and various forms of disturbances, and compare the results to both engineering approaches and biological data.

animal behavior and cognition↗