{
  "abstract": "We extend FLYNN, a leaky integrator recurrent network wired on the Drosophila melanogaster connectome, with a 32-ray 2D LiDAR interface that requires no learned fusion layer beyond a scalar input gain. A 270 degree frontal scan at 8 m range is injected as inverted proximity into the 37 BM Vib head-bristle neurons. Across 24 paired comparisons in a four- state vision matrix with LiDAR on or off, FLYNN-LiDAR has 17 significant wins, 5 nonsignificant comparisons, and 2 significant losses against three LiDAR-capable baselines. Both losses are to MobileNet with the left eye off. With full vision, FLYNN- LiDAR reaches 96.6% success. Under total vision loss, the scan raises success from 45.2% to 64.6%; the baselines reach 44.6% to 56.6% with the scan on. Under out of distribution textures, it is the only LiDAR-capable model whose success improves with the scan (44.2% to 58.4%); both CNNs score 0%, and SmallWorld declines from 26.8% to 7.4%. Under periodic vision dropout, FLYNN-LiDAR achieves 93.6% and 89.6% success at 25% and 50% duty with fewer collisions. Profiling the evaluated forward paths gives 3.93M multiply accumulate operations per step; EfficientNet and MobileNet require 4.60x and 2.94x as many. Final- state analysis shows near-linear sensory superposition (cosine 0.9955).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03897",
  "author_total": 2,
  "authors": [
    {
      "name": "Ali Mahzarnia"
    },
    {
      "name": "Homayoun Jamshidi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/abs8q7aszh6z6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03897",
  "fulltext_url": null,
  "id": "abs8q7aszh6z6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03897",
  "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/2611.03897",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/abs8q7aszh6z6.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:30Z",
      "native_id": "2611.03897",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03897"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Fly Whiskers Meet LiDAR: Robust Navigation under Sensor Failure",
  "title_text": "Fly Whiskers Meet LiDAR: Robust Navigation under Sensor Failure",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:04Z",
  "url": "https://research.draconitic.com/paper/abs8q7aszh6z6",
  "venue": "arXiv",
  "versions": []
}