{
  "abstract": "Understanding how neuronal networks reorganize in response to stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. Existing methods, however, often struggle to capture the evolving structure of neural connectivity and its relationship to behavior, particularly in dynamic, uncertain, or high-dimensional settings with sufficient resolution and interpretability. We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a framework for modeling time-varying neuronal connectivity by integrating probabilistic graph learning with temporal attention. TAVRNN learns single-unit latent dynamics while maintaining interpretable population-level representations, enabling identification of behaviorally relevant connectivity patterns. It generalizes across neural systems differing in scale, behavioral structure, and supervision regime, achieving state-of-the-art classification and clustering performance. We validate TAVRNN on three datasets: (1) electrophysiological recordings from a freely behaving rat, (2) primate somatosensory cortex recordings during reaching task, and (3) biological neurons in the DishBrain platform interacting with a virtual game environment. TAVRNN outperforms state-of-the-art dynamic embedding techniques and reveals previously unreported relationships between adaptive behavior and evolving network topology. These findings establish TAVRNN as a powerful, scalable, generalizable, and modality-agnostic approach for modeling neural dynamics across experimental and synthetic biological systems, with applicability across neural recording platforms and behavioral paradigms.",
  "abstract_shown": true,
  "affiliations": [
    "Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia",
    "Cortical Labs, Melbourne, VIC, Australia",
    "Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada",
    "Neural Dynamics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia",
    "Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia",
    "CIFAR Global Scholars Program, Toronto, ON, Canada",
    "Queen Square Institute of Neurology, University College London, London, United Kingdom"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. Amin Khajehnejad",
      "orcid": "0000-0002-2185-4596"
    },
    {
      "affs": [
        1
      ],
      "name": "Forough Habibollahi",
      "orcid": "0000-0001-6059-3723"
    },
    {
      "affs": [
        2
      ],
      "name": "Ahmad Khajehnejad"
    },
    {
      "affs": [
        3
      ],
      "name": "Chris R. French",
      "orcid": "0000-0002-1445-8704"
    },
    {
      "affs": [
        1,
        4
      ],
      "name": "Brett J. Kagan",
      "orcid": "0000-0001-7604-7444"
    },
    {
      "affs": [
        5,
        6,
        0
      ],
      "name": "Adeel Razi",
      "orcid": "0000-0002-0779-9439"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e0pc6fpg2yhn9.bib",
  "cited_by": 5,
  "doi": "10.1038/s42003-026-10721-y",
  "fulltext_url": null,
  "id": "e0pc6fpg2yhn9",
  "issns": [
    "2399-3642"
  ],
  "landing_url": "https://doi.org/10.1038/s42003-026-10721-y",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7171700403",
  "pdf_url": "https://www.nature.com/articles/s42003-026-10721-y_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-29",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/e0pc6fpg2yhn9.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:49Z",
      "native_id": "W7171700403",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s42003-026-10721-y"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
    }
  ],
  "title": "Graph-based representation learning of neuronal dynamics and behavior",
  "title_text": "Graph-based representation learning of neuronal dynamics and behavior",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:49Z",
  "url": "https://research.draconitic.com/paper/e0pc6fpg2yhn9",
  "venue": "Communications Biology",
  "versions": []
}