{
  "abstract": "In biological systems, neural circuits compute through directed, short-latency interactions whose effects unfold across multiple time scales and behavioral contexts. We address the problem of inferring these local, lag-specific interactions from sampled neural population activity under varying stimuli, without assuming a parametric form for the underlying dynamics. Our approach leverages denoising score models by estimating joint-window scores over consecutive activity snapshots (i.e., brain states) and converting these scores into calibrated, directed edge tests via cross-block score products. The key insight is that these products recover the Jacobian of the transition map between brain states under nonlinear dynamics. To cleanly separate lag-specific effects, we introduce minimal multi-block windows that condition on intermediate time points, avoiding the omitted-lag bias inherent in pairwise analyses. The resulting method, Score--Block Time Graphs (SBTG), identifies lag-specific directed interactions in sampled neuronal population data. We specifically apply SBTG to whole-brain C. elegans calcium imaging data to recover lag-specific circuit structure not resolved by current methods, including improved alignment with independent connectomes, cell-type-specific temporal organization, and neuromodulatory profiles consistent with known receptor kinetics. These findings highlight the potential for SBTG to serve as a practical ``AI for science'' tool by turning high-dimensional neural population recordings into statistically testable circuit hypotheses.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.02852",
  "author_total": 5,
  "authors": [
    {
      "name": "Savik Kinger"
    },
    {
      "name": "Johannes Bertram"
    },
    {
      "name": "Luciano Dyballa"
    },
    {
      "name": "Eviatar Yemini"
    },
    {
      "name": "Steven W. Zucker"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6n8wmrj0gebmy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.02852",
  "fulltext_url": null,
  "id": "6n8wmrj0gebmy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.02852",
  "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",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7160257525",
  "pdf_url": "https://arxiv.org/pdf/2605.02852",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6n8wmrj0gebmy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:02Z",
      "native_id": "2605.02852",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.02852"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Inferring Active Neural Circuits Using Diffusion Scores",
  "title_text": "Inferring Active Neural Circuits Using Diffusion Scores",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:02Z",
  "url": "https://research.draconitic.com/paper/6n8wmrj0gebmy",
  "venue": "arXiv",
  "versions": []
}