{
  "abstract": "Cognition is implemented by distributed neural populations whose collective activity evolves through structured, low-dimensional state spaces. In this review, we survey the analytical and conceptual tools used to study latent population dynamics in biological neural networks. We begin with single-unit coding, which remains essential for interpreting neuronal selectivity, and then review population-level approaches, including population vectors, cosine similarity, principal component analysis and its mnemonic variants, multivariable linear regression axes, demixed principal component analysis, and single-trial latent-variable models, and distinguish operationally defined population subspaces from neural manifolds with reproducible geometry or topology. We use these methods to discuss sensory coding, working memory, decision-making, temporal processing, and sensorimotor transformations. Two organizing principles are emphasized throughout. First, cognitive variables can be expressed in separable or near-orthogonal population dimensions, reducing interference between sensory, mnemonic, decision-related, and motor signals. Second, such geometric separation does not require anatomically or functionally discrete cell classes; separable population readouts can coexist with a continuum of mixed-selective single-neuron responses within a shared biological substrate. We then consider recurrent neural networks as testbeds for generating and evaluating mechanistic hypotheses about how circuits produce low-dimensional dynamics, and intracortical brain-computer interfaces as a translational domain in which population geometry becomes an operational control signal. Overall, this review argues that population dynamics now provide a common language for linking neurophysiology, computational modelling, and neurotechnology.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36541",
  "author_total": 3,
  "authors": [
    {
      "name": "Lucas Bayones"
    },
    {
      "name": "Devika Narain"
    },
    {
      "name": "Roman Rossi-Pool"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8j3sfyxpzrt9j.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36541",
  "fulltext_url": null,
  "id": "8j3sfyxpzrt9j",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36541",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Neural dynamics and brain function"
    ]
  ],
  "openalex": "W7224092383",
  "pdf_url": "https://arxiv.org/pdf/2610.36541",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8j3sfyxpzrt9j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:56Z",
      "native_id": "2610.36541",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36541"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "The geometry of cognition: from neural populations to neurotechnology",
  "title_text": "The geometry of cognition: from neural populations to neurotechnology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:56Z",
  "url": "https://research.draconitic.com/paper/8j3sfyxpzrt9j",
  "venue": "arXiv",
  "versions": []
}