{
  "abstract": "Hodgkin-Huxley equations as a monumental breakthrough in neuroscience theory had been distilled into many simplified models to study, but this mathematical model itself was not fully investigated in terms of global and asymptotic dynamics due to its strong nonlinearity and higher dimensionality. In this paper a new model called Hodgkin-Huxley-Wilson neural networks is proposed and investigated. This model captures the essential features of the nonlinearity and the conductances of two dominant ionic current channels coupled with the neuron membrane voltage in gated firing and bursting functionality of biological neural networks in the original Hodgkin-Huxley model. Through the uniform and sharp \\emph{a priori} estimates purely by mathematical hard analysis on the solutions of the model equations and then the interneuron differencing equations of gap functions, it is rigorously proved that global solution dynamics are robustly dissipative with a sharp ultimate bound and that exponential synchronization of the Hodgkin-Huxley-Wilson neural networks at a uniform convergence rate occurs if the interneuron coupling strength satisfies an explicitly computable threshold condition. The synchronization result with fractional-power convergence rate is also proved for time-fractional memristive Hodgkin-Huxley-Wilson neural networks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.07803",
  "author_total": 1,
  "authors": [
    {
      "name": "Yuncheng You"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7cj9k9hck3ynv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.07803",
  "fulltext_url": null,
  "id": "7cj9k9hck3ynv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.07803",
  "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",
      "math.AP"
    ],
    [
      "openalex",
      "1705|Neural Networks Stability and Synchronization"
    ]
  ],
  "openalex": "W7160858150",
  "pdf_url": "https://arxiv.org/pdf/2605.07803",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7cj9k9hck3ynv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:42Z",
      "native_id": "2605.07803",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.07803"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.AP",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Global Dynamics and Synchronization of Hodgkin-Huxley-Wilson Neural Networks",
  "title_text": "Global Dynamics and Synchronization of Hodgkin-Huxley-Wilson Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:42Z",
  "url": "https://research.draconitic.com/paper/7cj9k9hck3ynv",
  "venue": "arXiv",
  "versions": []
}