{
  "abstract": "Biological neurons receive thousands of synaptic inputs on branching, electrically excitable dendrites, yet population activity is often modeled with direct input-output rules in which each input contributes independently to a scalar drive. We study what successful prediction by such models does, and does not, reveal about neural computation. For conditional maximum-entropy models that match output rates and pairwise output-input coactivities, the entropy explained by a direct model is a prediction measure under the sampled input distribution, not a mechanism-identification test. A restricted MaxEnt fit is an information projection: omitted interaction, temporal, or hidden-state terms can be absorbed into fitted first-order parameters whenever they are correlated with the included sufficient statistics. For sparse correlated binary inputs, this absorption has an explicit coskewness form. We introduce diagnostics that separate in-distribution prediction from recovery of the response rule: state reweighting that holds P(y|x) fixed while changing P(x), conditional log-odds contrasts for local additivity, and temporal leakage controls. In ground-truth simulations, purely higher-order responses can pass first-order entropy and raw coactivity tests under leakage-prone sampling, but are correctly classified after reweighting. Applied to selected, leakage-enriched local tables from CA1 hippocampal recordings, approximately half of tables that appear first-order under empirical weights become distribution-sensitive under balanced reweighting, far above a matched additive-surrogate null. Thus direct entropy-explained fractions and raw coactivity predictions should be interpreted as predictions under the observed state distribution, not as evidence that mechanisms outside the direct model are absent or small.",
  "abstract_shown": true,
  "affiliations": [
    "Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University",
    "Department of Neurobiology , Howard Hughes Medical Institute , Harvard Medical School"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Houman Safaai",
      "orcid": "0000-0002-8609-7397"
    },
    {
      "affs": [
        1,
        0
      ],
      "name": "Bernardo Luis Sabatini",
      "orcid": "0000-0003-0095-9177"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7n6ncf3wqa9j7.bib",
  "cited_by": 5,
  "doi": "10.48550/arxiv.2606.01661",
  "fulltext_url": null,
  "id": "7n6ncf3wqa9j7",
  "issns": [
    "2331-8422"
  ],
  "landing_url": "https://arxiv.org/abs/2606.01661",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7163150806",
  "pdf_url": "https://arxiv.org/pdf/2606.01661",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Cornell University",
  "ris": "https://research.draconitic.com/paper/7n6ncf3wqa9j7.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:56Z",
      "native_id": "W7163150806",
      "source": "openalex",
      "url": "https://arxiv.org/abs/2606.01661"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
    }
  ],
  "title": "Feature leakage and the identifiability of direct-dependency entropy models of neural activity",
  "title_text": "Feature leakage and the identifiability of direct-dependency entropy models of neural activity",
  "type": "preprint",
  "updated_at": "2026-11-09T13:21:14Z",
  "url": "https://research.draconitic.com/paper/7n6ncf3wqa9j7",
  "venue": "arXiv (Cornell University)",
  "versions": []
}