Biology subjects

MaBouDi, H.

Publications and source records attributed to MaBouDi, H..

10 recordsLinked to original sources

bioRxiv2026

Associative learning reconfigures colour-guided behaviour in Drosophila larvae

Colour-guided behaviour can be innate or learned, yet how a numerically simple nervous system assigns value to spectral cues remains unresolved. We developed an automated differential-conditioning assay in which third-instar Drosophila larvae were trained with blue and green light, one of which was paired with electric shock. Wild-type larvae subsequently avoided the shock-paired colour, whereas naive controls showed no strong blue-green preference. This learned avoidance persisted when the relative intensity cue was transferred to the alternative colour, indicating that behavioural choice followed spectral identity rather than simply tracking the dimmer stimulus. Disruption of either the blue-sensitive Rh5 or green-sensitive Rh6 photoreceptor pathway significantly weakened the training-induced behavioural shift relative to wild type, with Rh5 mutants showing only a small residual shift. Complementary extracellular recordings confirmed light-evoked activity in the larval visual system, with robust responses to ultraviolet and broadband white illumination but smaller and more variable responses to blue and green light. Together, these results show that larvae can discriminate blue from green, demonstrating elementary colour vision; that associative experience reconfigures their colour-guided behaviour; and that both photoreceptor pathways contribute to this learned response. This paradigm separates learned spectral preference from baseline light preference and provides a tractable framework for linking defined sensory inputs to the circuit mechanisms of visual memory.

neuroscience↗

bioRxiv2025

Synaptic high-frequency jumping synchronises vision to high-speed behaviour

During high-speed behaviour, animals must synchronise perception and action despite rapid environmental and self-generated motion. How neural systems achieve such precision remains unclear. Here we show how the housefly (Musca domestica) maintains visual accuracy during fast motion. Using intracellular and photomechanical recordings during saccade-like stimulation, we traced information flow from photoreceptors to large monopolar cells (LMCs). Visual neurons achieved record-high information sampling ([~]2,500 bits{middle dot}s-1) and synaptic transmission ([~]4,100 bits{middle dot}s-1), far exceeding previous estimates. We identify a previously unknown mechanism - synaptic high-frequency jumping - in which photoreceptor-LMC synapses dynamically shift transmission toward higher frequencies during saccades, extending visual bandwidth to [~]1,000 Hz, effectively eliminating synaptic delays, and quadrupling classical flicker-fusion limits ([~]230 Hz). Behavioural experiments show flies respond synchronously within [~]13-20 ms, even before photoreceptor responses peak. A biophysically realistic model reveals how photomechanical-stochastic-refractory quantal sampling co-adapts with saccadic behaviour: through self-motion, flies efficiently translate image motion into temporally-precise, predictive high-speed vision.

neuroscience↗

bioRxiv2024

Bumblebee visual learning: simple solutions for complex stimuli

Natural visual stimuli are typically complex. This presents animals with the challenge of learning the most informative aspects of these stimuli while not being confused by variable elements. How animals might do this remains unclear. Here, we tested bumblebees ability to learn multicomponent visual stimuli composed of a simple constant bar element and a grating element that was consistent in orientation but varied in width and number of gratings. Bees rapidly and successfully learned these compound stimuli. Tests revealed learning of the single bar element was more robust than learning of the grating element. Our study highlights how even small-brained invertebrates can rapidly learn multicomponent stimuli and prioritise the most consistent elements within them. We discuss how the learning phenomena of generalisation and overshadowing may be sufficient to explain these findings, and caution that complex cognitive concepts are not necessary to explain the learning of complex stimuli. HighlightsO_LIBumblebees are highly efficient in prioritising the most consistent elements in multicomponent visual stimuli. C_LIO_LIBees trained on horizontal and vertical cues exhibit differences in how they memorise visual cues. C_LIO_LITwo phenomena can explain how bees preferentially select, memorise and use visual cues in this experiment: generalisation and overshadowing. C_LIO_LIBumblebees as generalist foragers are well-suited to study visual cognition. C_LI

animal behavior and cognition↗

bioRxiv2023

A neuromorphic model of active vision shows spatio-temporal encoding in lobula neurons can aid pattern recognition in bees

Bees' remarkable visual learning abilities make them ideal for studying active information acquisition and representation. Here, we develop a biologically inspired model to examine how flight behaviours during visual scanning shape neural representation in the insect brain, exploring the interplay between scanning behaviour, neural connectivity, and visual encoding efficiency. Incorporating non-associative learning, adaptive changes without reinforcement, and exposing the model to sequential natural images during scanning, we obtain results that closely match neurobiological observations. Active scanning and non-associative learning dynamically shape neural activity, optimising information flow and representation. Lobula neurons, crucial for visual integration, self-organise into orientation-selective cells with sparse, decorrelated responses to orthogonal bar movements. They encode a range of orientations, biased by input speed and contrast, suggesting co-evolution with scanning behaviour to enhance visual representation and support efficient coding. To assess the significance of this spatiotemporal coding, we extend the model with circuitry analogous to the mushroom body, a region linked to associative learning. The model demonstrates robust performance in pattern recognition, implying a similar encoding mechanism in insects. Integrating behavioural, neurobiological, and computational insights, this study highlights how spatiotemporal coding in the lobula efficiently compresses visual features, offering broader insights into active vision strategies and bio-inspired automation.

neuroscience↗

bioRxiv2023

How honey bees make fast and accurate decisions

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

neuroscience↗

bioRxiv2022

The establishment and transmission of novel foraging techniques indicates a capacity for culture in bumblebees (Bombus terrestris).

The astonishing behavioural repertoires of social insects have been thought largely innate, but these insects have repeatedly demonstrated remarkable capacities for both individual and social learning. Using the bumblebee Bombus terrestris as a model, we developed a two-option puzzle box task and used open diffusion paradigms to observe the transmission of novel, non-natural foraging behaviours through populations. Box-opening behaviour spread through colonies seeded with a demonstrator trained to perform one of the two possible behavioural variants, and the observers acquired the demonstrated variant. This preference persisted among observers even when the alternative technique was discovered. In control diffusion experiments that lacked a demonstrator, some bees spontaneously opened the puzzle boxes but were significantly less proficient than those that learned in the presence of a demonstrator. This suggested that social learning was crucial to proper acquisition of box-opening. Additional open diffusion experiments where two behavioural variants were initially present in similar proportions ended with a single variant becoming dominant, due to stochastic processes. This could lead to the emergence and maintenance of local cultural variation. These results suggest that bumblebees, like mammals and birds, may have the capacity for culture and to sustain cultural variation.

animal behavior and cognition↗

bioRxiv2021

Automated video tracking and flight analysis show how bumblebees solve a pattern discrimination task using active vision

Active vision, the ability of the visual system to actively sample and select relevant information out of a visual scene through eye and head movements, has been explored in a variety of animal species. Small-brained animals such as insects might rely even more on sequential acquisition of pattern features since there might be less parallel processing capacity in their brains than in vertebrates. To investigate how active vision strategies enable bees to solve visual tasks, here, we employed a simple visual discrimination task in which individual bees were presented with a multiplication symbol and a 45{degrees} rotated version of the same pattern ("plus sign"). High-speed videography of unrewarded tests and analysis of the bees flight paths shows that only a small region of the pattern is inspected before successfully accepting a target or rejecting a distractor. The bees scanning behaviour of the stimuli differed for plus signs and multiplication signs, but for each of these, the flight behaviour was consistent irrespective of whether the pattern was rewarding or unrewarding. Bees typically oriented themselves at ~{+/-}30{degrees} to the patterns such that only one eye had an unobscured view of stimuli. There was a significant preference for initially scanning the left side of the stimuli. Our results suggest that the bees movement may be an integral part of a strategy to efficiently analyse and encode their environment. Summary statementAutomated video tracking and flight analysis is proposed as the next milestone in understanding mechanisms underpinning active vision and cognitive visual abilities of bees.

animal behavior and cognition↗

bioRxiv2020

Bumblebees learn a relational rule but switch to a win-stay/lose-switch heuristic after extensive training

Mapping animal performance in a behavioural task to underlying cognitive mechanisms and strategies is rarely straightforward, since a task may be solvable in more than one manner. Here, we show that bumblebees perform well on a concept-based visual discrimination task, but spontaneously switch from a concept-based solution to a simpler heuristic with extended training, all while continually increasing performance. Bumblebees were trained in an arena to find reward on displays with shapes of different sizes where they could not use low-level visual cues. One group of bees was rewarded at displays with bigger shapes and another group at displays with smaller shapes. Analysis of total choices shows bees increased their performance over 30 bouts to above chance. However, analyses of first and sequential choices suggest that after approximately 20 bouts, bumblebees changed to a win-stay/lose-switch strategy. Comparing bees behaviour to a probabilistic model based on a win-stay/lose-switch strategy further supports the idea that bees changed strategies with extensive training. Analyses of unrewarded tests indicate bumblebees learned and retained the concept of relative size even after they had already switched to a win-stay, lost-shift strategy. We propose that the reason for this strategy switching may be due to cognitive flexibility and efficiency.

animal behavior and cognition↗

bioRxiv2020

Honey bees solve a multi-comparison ranking task by probability matching

Honey bees forage on a range of flowers, all of which can vary unpredictably in the amount and type of rewards they offer. In this environment bees are challenged with maximising the resources they gather for their colony. That bees are effective foragers is clear, but how bees solve this type of complex multi-choice task is unknown. Here we challenged bees with a five-comparison choice task in which five colours differed in their probability of offering reward and punishment. The colours were ranked such that high ranked colours were more likely to offer reward, and the ranking was unambiguous. Bees choices in unrewarded tests matched their individual experiences of reward and punishment of each colour, indicating bees solved this test not by comparing or ranking colours but by matching their preferences to their history of reinforcement for each colour. We used a computational model to explore the feasibility of this probability matching strategy for the honey bee brain. The model suggested a structure like the honey bee mushroom body with reinforcement-related plasticity at both input and output was sufficient for this cognitive strategy. We discuss how probability matching enables effective choices to be made without a need to compare any stimuli directly, and the utility and limitations of this simple cognitive strategy for foraging animals.

animal behavior and cognition↗

bioRxiv2017

Learning Complex Representations from Spatial Phase Statistics of Natural Scenes

Natural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure under the efficient coding hypothesis, here we introduce a generative model of natural scenes by assuming independent source signals in a complex domain and non-uniform phase priors for the complex signals. Parameters of the proposed model are then estimated under the maximum-likelihood principle. This approach extends existing methods of independent component analysis for complex-valued signals to the one that utilizes phase information. Using simulated data, we demonstrate that the proposed model outperforms conventional models with a uniform phase prior in blind source separation of complex-valued signals. We then apply the proposed model to natural scenes in the Fourier domain. Real and imaginary parts of the learned complex features exhibit a pair of Gabor-like filters in quadratic phase structure with a similar shape. The proposed model significantly improved the goodness-of-the-fit from the model with a uniform phase prior, indicating that the structured spatial phases are important for removing redundancy in natural scenes. These results predict the presence of phase sensitive complex cells in the visual cortex.

neuroscience↗