{
  "abstract": "Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler. Here we ask how observational resolution shapes the apparent structure of neural computation. We introduce a scale-indexed decomposition that separates the predictive information captured by direct dependencies from the residual outside that model family. Exact constructions show that projection can convert higher-order dependence into pairwise structure. Projection can increase the direct fraction of surviving information even as total predictive information decreases. A schematic branch-to-soma cascade grounds these effects in local coincidence detection, saturating integration and somatic mixing. Resolving organized nonlinear subunits improves approximation and raises effective representational rank. In a nested hierarchy, these gains occur without increasing information about the target. This distinction separates information availability from access by a restricted readout. A point-neuron or direct population model therefore establishes an effective description at a specified scale. It does not uniquely identify the finer computation that generated the observed relation. Which resolution is adequate depends on the observable and perturbation of interest.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19487",
  "author_total": 1,
  "authors": [
    {
      "name": "Nima Dehghani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/03eeacbcgnqaq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19487",
  "fulltext_url": null,
  "id": "03eeacbcgnqaq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19487",
  "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",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "cs.NE"
    ],
    [
      "arxiv",
      "physics.bio-ph"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7222329983",
  "pdf_url": "https://arxiv.org/pdf/2610.19487",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/03eeacbcgnqaq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.19487",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19487"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.bio-ph",
      "subfield": "biological-medical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity",
  "title_text": "How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:36Z",
  "url": "https://research.draconitic.com/paper/03eeacbcgnqaq",
  "venue": "arXiv",
  "versions": []
}