Stable motor-cortex behavioral manifolds can be supported by very few dimension…

{
  "n": null,
  "doi": "10.64898/2026.01.30.702162",
  "claim": "Stable motor-cortex behavioral manifolds can be supported by very few dimensions: a 2-D supervised latent capturing only 3–7% of variance outperforms common methods for offline decoding.",
  "cite_key": "Abdulla2026",
  "evidence": "Supervised latent variable framework on motor cortex population data; demonstrates that changes in the encoding map (neurons→latents) dominate drift while latent→behavior is stable over years.",
  "effect_size": "2 latent dimensions; 3-7% variance captured; long-term latent-to-behavior stability over years",
  "text_access": "abstract_only",
  "study_system": "Motor cortex during 2D reaching",
  "argument_role": "supporting",
  "replication_status": "replication_unknown",
  "claim_source_sentence": "Despite only capturing 3-7% of total variance and spanning two dimensions, this supervised method outperforms other common methods at an offline decoding task, and explains the long-term stability of neural manifolds observed in previous literature.",
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  "replication_evidence_dois": [],
  "effect_size_source_sentence": "We demonstrate that changes in the encoding map (from neurons to latents) is the dominant source of drift in motor cortex, while the latent map (from latents to behavior) remains stable over years."
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Despite only capturing 3-7% of total variance and spanning two dimensions, this supervised method outperforms other common methods at an offline decoding task, and explains the long-term stability of neural manifolds observed in previous literature.

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