{
  "abstract": "A major challenge in neuroscience and machine learning is to connect single-neuron influence, population dynamics, and circuit connectivity in a causal account of computation. Much previous work has shown that low-rank connectivity can generate low-dimensional dynamics in trained artificial neural networks, but this leaves unclear the functional relevance of the higher-rank structure of biological neural circuits and many artificial neuronal networks. Here we analyse recurrent neural networks trained to locate rewards by integrating continuously varying speed inputs in one- and two-dimensional spatial tasks. We find that dominant low-dimensional dynamics encode task locations, and can be causally manipulated to instruct behavioural outcomes. However, after decomposing the underlying circuitry we found that while low-rank connectivity accounts for the low-dimensional dynamics, accurate performance and generalisation to novel speed distributions requires high-rank connectivity. We demonstrate that this is achieved through distributed signalling that corrects errors in low-dimensional location representations. Thus, combined perturbation-, representation-, and circuit-level analyses demonstrate a novel mechanism for robust spatial computation and show how high-rank connectivity in neural circuits can provide a scaffold that supports precision and generalisation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.35207",
  "author_total": 2,
  "authors": [
    {
      "name": "Ian Hawes"
    },
    {
      "name": "Matt Nolan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cbqdvrtgks5qt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.35207",
  "fulltext_url": null,
  "id": "cbqdvrtgks5qt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.35207",
  "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": "W7214793954",
  "pdf_url": "https://arxiv.org/pdf/2609.35207",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cbqdvrtgks5qt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:21Z",
      "native_id": "2609.35207",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.35207"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "High-rank connectivity scaffolds support precision and generalisation in recurrent neural networks",
  "title_text": "High-rank connectivity scaffolds support precision and generalisation in recurrent neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:21Z",
  "url": "https://research.draconitic.com/paper/cbqdvrtgks5qt",
  "venue": "arXiv",
  "versions": []
}