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Guiraud, M.-G.

Publications and source records attributed to Guiraud, M.-G..

2 recordsLinked to original sources

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↗

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↗