{
  "id": 6810133,
  "title": "Flexible reorientation of conserved neural dynamics underlies grasp control",
  "url": "https://urgent.news/2026/09/11/flexible-reorientation-of-conserved-neural-dynamics-underlies-grasp",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-11T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.04.749537v1?rss=1"
  },
  "original_language": "en",
  "account": "Grasping objects involves intricate hand movements and environmental interactions, yet the neural mechanisms controlling this behavior have not been thoroughly understood. Dynamical systems theories have offered valuable insights into the neural signals generated for arm movements such as reaching and cycling; however, they have fallen short in explaining the neural activity patterns during grasping. In a recent study, researchers revisited multi-area neural recordings from rhesus monkeys performing a reach-to-grasp task on various objects and identified two critical population-level features. Firstly, the shared aspect of the neural population trajectory during all grasps formed a small angle with the subspace responsible for grasp condition-specific (object) tuning. Secondly, the neural activity was accurately modeled by a recent flexible dynamical model, known as location-dependent rotations (LDR), which was initially developed for reaching tasks. The neural activity in the primary motor cortex (M1) and the supplementary motor area (F5) during grasping exhibited consistent rotational frequencies, but the planes in which these rotations occurred varied systematically depending on the grasp condition. The rotational center (the location) and the orientation of the rotations were closely linked to each other and to the movement kinematics. Although these rotations were reoriented in high-dimensional space, reflecting the complexity of hand control, the tilt of the rotations into additional dimensions was minimal, contrasting with the reaching task. This structural organization may stem from grasping being a modification of an overarching open-close motif. The findings collectively suggest that the LDR dynamics framework can be applied to grasping, akin to reaching, and offer a starting point for comprehending how grasp commands are generated.",
  "summary": "Grasping objects is a complex behavior requiring high-dimensional hand control and dynamic interaction with the environment, yet its neural mechanisms remain poorly understood. Dynamical systems approaches have provided key insights into how neural populations generate control signals for arm movements such as reaching and cycling but have proven insufficient to account for neural population…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}