{
  "abstract": "Maximum-entropy models of neural recordings can be treated as Boltzmann distributions at a fictitious temperature $T$, with the fitted model at $T=1$. Their specific-heat peak grows with population size and approaches that temperature, which has been read as a sign of criticality. The case rests on pairwise models, yet retinal codes require higher-order interactions. The thermodynamics of a model with such interactions fitted cell by cell has not been computed. Here I compute it exactly for pairwise and third-order models of the same populations of up to $20$ retinal ganglion cells, in salamander and marmoset. The triplet terms, required by the held-out likelihood, shift the peak by more than the finite-sample bias, with opposite sign in the two retinas. The peak is lower and grows more slowly in salamander, higher and grows faster in marmoset, and lies closer to $T=1$ in both. An exact decomposition traces most of the shift to the energy variance across population spike counts. A constraint on the spike-count distribution alone reproduces all of it in salamander and over half in marmoset. A second recording of each species, re-binning, and a common-input surrogate show that the sign is set by the spike-count structure of a recording at its temporal resolution, not by species. The thermodynamic signature of a neural code is therefore set not by the data alone but by the interaction order of the model used to read it.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05336",
  "author_total": 1,
  "authors": [
    {
      "name": "Narendra N. Hegade"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bka5dpg4fekex.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05336",
  "fulltext_url": null,
  "id": "bka5dpg4fekex",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05336",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.stat-mech"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05336",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bka5dpg4fekex.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.05336",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05336"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Higher-order interactions renormalize the thermodynamic signature of neural population codes",
  "title_text": "Higher-order interactions renormalize the thermodynamic signature of neural population codes",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:25Z",
  "url": "https://research.draconitic.com/paper/bka5dpg4fekex",
  "venue": "arXiv",
  "versions": []
}