ArticlePloS one2018
Can Drosophila melanogaster tell who's who?
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification.Animals : an open access journal from MDPI · 2024Article
- The ortholog of human ssDNA-binding protein SSBP3 influences neurodevelopment and autism-like behaviors in Drosophila melanogaster.PLoS biology · 2023Article
- PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears.Animals : an open access journal from MDPI · 2023Article
- Review
- Flexibility of neural circuits regulating mating behaviors in mice and flies.Frontiers in neural circuits · 2022Review
- Perspectives on Individual Animal Identification from Biology and Computer Vision.Integrative and comparative biology · 2021Review
- Sensory perception of dead conspecifics induces aversive cues and modulates lifespan through serotonin in Drosophila.Nature communications · 2019Article
- MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drosophila melanogaster are known to live in a social but cryptic world of touch and odours, but the extent to which they can perceive and integrate static visual information is a hotly debated topic. Some researchers fixate on the limited resolution of D. melanogaster's optics, others on their seemingly identical appearance; yet there is evidence of individual recognition and surprising visual learning in flies. Here, we apply machine learning and show that individual D. melanogaster are visually distinct. We also use the striking similarity of Drosophila's visual system to current convolutional neural networks to theoretically investigate D. melanogaster's capacity for visual understanding. We find that, despite their limited optical resolution, D. melanogaster's neuronal architecture has the capability to extract and encode a rich feature set that allows flies to re-identify individual conspecifics with surprising accuracy. These experiments provide a proof of principle that Drosophila inhabit a much more complex visual world than previously appreciated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.