{
  "abstract": "Electrical stimulation of brain activity has emerged as a key therapeutic intervention for neurological diseases. The success of these approaches relies on precisely tuned stimulation parameters. A major challenge is that these parameters should ideally be adjusted in real-time based on changes in brain activity. Closed-loop controllers address this challenge by monitoring brain activity and adapting stimulation according to ongoing changes in neural dynamics. While machine learning has been helpful in this context, closed-loop controllers typically do not adapt in real-time to fluctuations in brain activity. Here, we propose a novel framework based on reservoir computing to train a closed-loop, real-time neural controller. This approach, termed CLEAR-FORCE, trains the output weights of a recurrent network based on a combination of target signals and feedback from brain activity. Two series of simulations were performed, where the controller was successful at (1) inducing neural oscillations in a population of spiking neurons and (2) suppressing pathological oscillations. This framework provides a promising avenue for the implementation of neural controllers in low-powered, real-time, and adaptive hardware that harnesses machine learning for the personalized treatment of brain disorders.",
  "abstract_shown": true,
  "affiliations": [
    "University of Ottawa"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "A. Moreno-Gris"
    },
    {
      "name": "C. Godin"
    },
    {
      "affs": [
        0
      ],
      "name": "J.-P. Thivierge"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/acdt7z1c1spsd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.05.756709",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756709v1.full",
  "id": "acdt7z1c1spsd",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756709v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7221556416",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756709v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/acdt7z1c1spsd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:14Z",
      "native_id": "10.64898/2026.10.05.756709",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756709v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "CLEAR-FORCE: A reservoir computing framework for closed-loop brain stimulation",
  "title_text": "CLEAR-FORCE: A reservoir computing framework for closed-loop brain stimulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:14Z",
  "url": "https://research.draconitic.com/paper/acdt7z1c1spsd",
  "venue": "bioRxiv",
  "versions": []
}