A neuromorphic model of active vision shows how spatiotemporal 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 b...
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| Format: | Article |
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eLife Sciences Publications Ltd
2025-07-01
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| Online Access: | https://elifesciences.org/articles/89929 |
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| author | HaDi MaBouDi Mark Roper Marie-Geneviève Guiraud Mikko Juusola Lars Chittka James AR Marshall |
| author_facet | HaDi MaBouDi Mark Roper Marie-Geneviève Guiraud Mikko Juusola Lars Chittka James AR Marshall |
| author_sort | HaDi MaBouDi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5456bd07fa4e4dc2b9e75d0f38fe99a4 |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | eLife Sciences Publications Ltd |
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| series | eLife |
| spelling | doaj-art-5456bd07fa4e4dc2b9e75d0f38fe99a42025-08-20T03:14:49ZengeLife Sciences Publications LtdeLife2050-084X2025-07-011410.7554/eLife.89929A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in beesHaDi MaBouDi0https://orcid.org/0000-0002-7612-6465Mark Roper1https://orcid.org/0000-0003-1135-6187Marie-Geneviève Guiraud2https://orcid.org/0000-0001-5843-9188Mikko Juusola3https://orcid.org/0000-0002-4428-5330Lars Chittka4https://orcid.org/0000-0001-8153-1732James AR Marshall5https://orcid.org/0000-0002-1506-167XDepartment of Computer Science, University of Sheffield, Sheffield, United Kingdom; School of Biosciences, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom; School of Biological and Behavioural Sciences, Queen Mary University of London, London, United KingdomSchool of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom; Drone Development Lab, Ben Thorns Ltd, Colchester, United KingdomSchool of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom; School of Natural Sciences, Macquarie University, North Ryde, AustraliaSchool of Biosciences, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United KingdomSchool of Biological and Behavioural Sciences, Queen Mary University of London, London, United KingdomDepartment of Computer Science, University of Sheffield, Sheffield, United Kingdom; Opteran Technologies Ltd, Sheffield, United KingdomBees’ 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.https://elifesciences.org/articles/89929image statisticslobulamushroom bodiesnon-associative learningscanning behaviourvisual recognition |
| spellingShingle | HaDi MaBouDi Mark Roper Marie-Geneviève Guiraud Mikko Juusola Lars Chittka James AR Marshall A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees eLife image statistics lobula mushroom bodies non-associative learning scanning behaviour visual recognition |
| title | A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| title_full | A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| title_fullStr | A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| title_full_unstemmed | A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| title_short | A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| title_sort | neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees |
| topic | image statistics lobula mushroom bodies non-associative learning scanning behaviour visual recognition |
| url | https://elifesciences.org/articles/89929 |
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