{
  "abstract": "Neuroscience aims to uncover the algorithms by which the brain builds and manipulates rich internal representations of objects, agents, and places. Existing reverse-engineering frameworks remain ineffective for identifying these algorithms. Here, we present Dynamical Structure-Preserving Manifolds (dSPMs)---a novel multilevel reverse-engineering framework that programs, without training, recurrent neural networks to enable hypothesis-driven identification of the brain's algorithms that generate symbolic, structure-preserving representations of the world around. We apply dSPM to identify an algorithm for physical scene prediction in the dorsomedial frontal cortex (DMFC) of macaques intercepting a ball on a Pong-like virtual gameboard. dSPM better explained DMFC than alternatives, made predictions confirmed in neural data, and revealed an algorithm of DMFC that we term 'manifold-based simulation': perceived initial conditions rapidly collapse neural activity onto a low-dimensional manifold whose geometry encodes a physics-based representation of the scene, supporting far-out predictions without traditional next-step simulation. dSPM supports effective exploration of the computational foundations of biological intelligence.",
  "abstract_shown": true,
  "affiliations": [
    "Yale University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. Calbick"
    },
    {
      "name": "J. Z. Kim"
    },
    {
      "name": "H. Sohn"
    },
    {
      "name": "I. Yildirim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/exqwwarh3nv3f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.755459",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.755459v1.full",
  "id": "exqwwarh3nv3f",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.755459v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7222719332",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.755459v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/exqwwarh3nv3f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:12Z",
      "native_id": "10.64898/2026.10.09.755459",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.09.755459v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Hypothesis-Driven Identification of Neural Algorithms With Dynamical Structure-Preserving Manifolds.",
  "title_text": "Hypothesis-Driven Identification of Neural Algorithms With Dynamical Structure-Preserving Manifolds.",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:12Z",
  "url": "https://research.draconitic.com/paper/exqwwarh3nv3f",
  "venue": "bioRxiv",
  "versions": []
}