{
  "abstract": "Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and drug-response studies to closed-loop experimentation. Existing approaches, however, typically assume a fixed set of sorted neurons, whereas high-density microelectrode arrays produce extremely sparse, array-wide binary spike volumes in which the observed subset of electrodes varies across assays. We introduce a discrete generative model that represents this activity using a shared vocabulary of spatiotemporal motifs. A residual vector-quantized autoencoder learns the motif vocabulary, while a factorized masked transformer predicts where activity occurs and which motif appears at each active location. We evaluate the model on 31 assays spanning human brain organoids and acute \\emph{ex vivo} human hippocampal tissue. The learned motifs are broadly reused: assay identity explains only $9%$ of the entropy in motif use, and motif overlap across tissue types is comparable to overlap within them. When representation quality is evaluated independently of the generative prior, our approach achieves $5.2\\times$ the voxel-level reconstruction average precision of a matched flat tokenizer. For masked completion and free generation, the full model achieves $1.4$--$2.6\\times$ the site-level average precision of the matched generative baseline and outperforms it across all four families of generation metrics. These results establish a compact, reusable representation for array-wide spiking activity without learned assay-specific parameters, providing a scalable foundation for generative modeling across diverse neural preparations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23907",
  "author_total": 3,
  "authors": [
    {
      "name": "Md Sayed Tanveer"
    },
    {
      "name": "Mohammed A. Mostajo-Radji"
    },
    {
      "name": "Ge Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/55kprdyjsg6ag.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23907",
  "fulltext_url": null,
  "id": "55kprdyjsg6ag",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23907",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7214024322",
  "pdf_url": "https://arxiv.org/pdf/2609.23907",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/55kprdyjsg6ag.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:28Z",
      "native_id": "2609.23907",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23907"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "A discrete generative model of neuronal spiking activity on microelectrode arrays",
  "title_text": "A discrete generative model of neuronal spiking activity on microelectrode arrays",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:28Z",
  "url": "https://research.draconitic.com/paper/55kprdyjsg6ag",
  "venue": "arXiv",
  "versions": []
}