{
  "abstract": "Whole-brain models of the fruit fly built from the FlyWire connectome can predict how neurons respond to a stimulus, but they keep every synapse fixed and so cannot learn, while models that do learn simulate only the mushroom body. I made the synapses from Kenyon cells (KCs) to mushroom body output neurons (MBONs) plastic in a whole-brain leaky integrate-and-fire model of the FlyWire connectome, under a rule in which dopamine weakens each synapse in proportion to how strongly its KC fired. Odours reached the KCs through the connectome's mapped olfactory wiring, and punishment was delivered by driving the 12 PPL1 dopamine neurons at 100 Hz. Pairing an odour with punishment lowered the response of approach MBONs to that odour from 11.3 to 0.3 Hz in all five noise replicates of one simulated brain, while unpaired and never-paired odours and controls with the rule switched off or scrambled changed little. A second odour paired on the already trained brain was learned at 85.5% of the strength reached by a naive brain, and approach MBONs ended with weak responses to both odours. With a timing rule in which dopamine arriving before the odour strengthens a synapse, a simulated fly body driven by the model unlearned and relearned in four of four cycles, whereas the first rule got stuck after one; backward pairing alone was too weak to reach behaviour. All results are for aversive learning; reward learning could not be tested because, at the synaptic weight and synapse floor used here, sugar input does not recruit the reward dopamine neurons. Learning depended on an authored plasticity rule, an injected punishment signal and other authored choices detailed in Methods; given these choices, the odour-specificity of learning followed from the structure of the connectome.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.09930",
  "author_total": 1,
  "authors": [
    {
      "name": "Daniel F. Asis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4jejck12853a1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.09930",
  "fulltext_url": null,
  "id": "4jejck12853a1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.09930",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.09930",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4jejck12853a1.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:38Z",
      "native_id": "2611.09930",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.09930"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Specific, cumulative and reversible odour learning in a whole-brain connectome model of Drosophila",
  "title_text": "Specific, cumulative and reversible odour learning in a whole-brain connectome model of Drosophila",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:38Z",
  "url": "https://research.draconitic.com/paper/4jejck12853a1",
  "venue": "arXiv",
  "versions": []
}