{
  "abstract": "Here, we present a protocol for the machine-learning-based Python package Neuro-MINE. We provide instructions for user interfaces that control model training and model prediction. Model training links experimental predictors to neural activity using a convolutional neural network that captures both linear and non-linear relationships. Taylor decomposition characterizes key aspects of these relationships, and we provide instructions for tweaking relevant parameters. Model prediction then computes the most likely neural response to hypothetical inputs, making it a powerful hypothesis generator.\n\nFor complete details on the use and execution of this protocol, please refer to Costabile et al.1",
  "abstract_shown": true,
  "affiliations": [
    "Neuroscience Graduate Program, The Ohio State University, Columbus, OH 43210, USA",
    "Department of Neuroscience, The Ohio State University, Columbus, OH 43210, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Danica Matovic"
    },
    {
      "affs": [
        1
      ],
      "name": "Martin Haesemeyer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cayghy281h779.bib",
  "cited_by": 0,
  "doi": "10.1016/j.xpro.2026.104913",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734871/",
  "id": "cayghy281h779",
  "issns": [
    "2666-1667"
  ],
  "landing_url": "https://doi.org/10.1016/j.xpro.2026.104913",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Protocol"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7223656459",
  "pdf_url": "https://www.cell.com/star-protocols/pdf/S2666-1667(26)00566-6.pdf",
  "pmcid": "PMC13734871",
  "pmid": "42945206",
  "published": "2026-10-27",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/cayghy281h779.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:41Z",
      "native_id": "PMC13734871",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.xpro.2026.104913"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
    }
  ],
  "title": "Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis",
  "title_text": "Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:20:41Z",
  "url": "https://research.draconitic.com/paper/cayghy281h779",
  "venue": "STAR Protocols",
  "versions": []
}