{
  "abstract": "We introduce neuro-distillation (Neuro-Dist), a technique designed to directly transfer computationally useful neural population structure from biological brains into artificial controllers. Using three macaque motor-cortical datasets as neuro-distillation teachers, we align controller internal states with cortical neural population geometry during training; controller deployment requires no neural data. We evaluate gated recurrent unit (GRU) networks and Transformers controlling a simulated arm. Neuro-Dist achieves the lowest median error among five methods, including distillation from movement kinematics, in 10 of 18 comparisons across trained, student-held-out, and new target directions. Seed-split aggregate statistical comparisons significantly favor Neuro-Dist over all six controls on trained and new targets, and five on student-held-out targets (Holm-corrected familywise p<0.05). We furthermore find that Neuro-Dist can substantially improve controller robustness to sensory feedback noise. Mechanistically, Neuro-Dist induced low tangling of trajectories in internal controller state space---a functionally important characteristic of motor cortex, demonstrating successful transfer of a known motor-cortical population geometry feature. These findings establish neuro-distillation as a complement to behavioral distillation and point to a novel class of neuro-interfaces that tap biological neural population structure as a computational resource for improving the control of goal-directed behavior in robotics and embodied AI.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06931",
  "author_total": 2,
  "authors": [
    {
      "name": "Maryna Kapitonova"
    },
    {
      "name": "Tonio Ball"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5sqt38y8ce0kp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06931",
  "fulltext_url": null,
  "id": "5sqt38y8ce0kp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06931",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06931",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5sqt38y8ce0kp.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:35Z",
      "native_id": "2611.06931",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06931"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Distilling Neural Population Geometry into Transformers and GRUs for Motor Control",
  "title_text": "Distilling Neural Population Geometry into Transformers and GRUs for Motor Control",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:50Z",
  "url": "https://research.draconitic.com/paper/5sqt38y8ce0kp",
  "venue": "arXiv",
  "versions": []
}