{
  "abstract": "Whole-brain connectomes are increasingly used as wiring diagrams for spiking simulations and as structural priors for artificial networks, usually after an ad hoc synapse-count threshold whose effect on validated function has never been measured. We define function-preserving sparsification - removing edges under a fixed budget while preserving validated behaviour and population responses - and study it in the leaky integrate-and-fire whole-brain model of Shiu et al. (2024). Six operators - weak-synapse pruning, uniform and weight-proportional random removal, degree-preserving rewiring, weight shuffling and a task-aware criterion - were compared at identical edge budgets across three validated circuits (sugar-evoked proboscis extension, its bitter suppression, grooming pathway specificity), silent and spontaneously active network states, and multiple seeds. Weak-synapse pruning preserves 79% of feeding output with 6% of edges, with a cliff at k* of approximately 9 synapses, whereas random, degree-preserving and weight-shuffled controls at the same budget abolish output, and weight-proportional random sampling survives only while it retains the strong-edge set: function resides in the conjunction of wiring placement and strength, not in degree statistics or synaptic mass. The cliff is task-dependent - grooming specificity collapses below k = 2 through disinhibition. In the active state failure changes character: output over-excites instead of vanishing, and bitter suppression is lost progressively from k = 5 and reverses at k = 31. A task-aware criterion preserves its training stimulus at 0.4% of edges but loses held-out inhibition first. These results provide a first benchmark for connectome sparsification and show that what must be kept depends on task and network state.",
  "abstract_shown": true,
  "affiliations": [
    "Yeungnam University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. An"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4awpp1sr7qr0j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.19.752860",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752860v1.full",
  "id": "4awpp1sr7qr0j",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752860v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7214331022",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752860v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4awpp1sr7qr0j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:28Z",
      "native_id": "10.64898/2026.09.19.752860",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752860v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "How sparse can a fly-brain model be? Connection strength, wiring placement, network state and task jointly determine function-preserving compression of the Drosophila connectome",
  "title_text": "How sparse can a fly-brain model be? Connection strength, wiring placement, network state and task jointly determine function-preserving compression of the Drosophila connectome",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:37Z",
  "url": "https://research.draconitic.com/paper/4awpp1sr7qr0j",
  "venue": "bioRxiv",
  "versions": []
}