Flexible reorientation of conserved neural dynamics underlies grasp control
- 1The University of Chicago
Abstract
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 activity during grasping. Here, we re-analyzed multi-area neural recordings from rhesus monkeys performing a reach-to-grasp task to many objects and found two key population-level features. First, the component of the population trajectory that was common to all grasps formed a small angle with the subspace containing grasp condition-specific (object) tuning. Second, the neural activity was well fitted by a recent, flexible dynamical model developed for reaching, called location-dependent rotations (LDR). Neural activity in M1 and F5 during grasping exhibited conserved population-level rotational frequencies, but the state-space planes in which these rotations occurred varied systematically with grasp condition. The rotational center (the location) and the orientation of the rotations related both to each other and to the kinematics of movement. While these rotations were reoriented in high dimensional space, reflecting the high-dimensional nature of hand control, the extent of their tilt into additional dimensions were small, in contrast to reaching. This structure may reflect the nature of grasping as modulations of an overall open-close motif. Together, these results indicate that the LDR dynamics framework applies to grasping as well as reaching and provides an entry point to understanding how grasp commands are generated.
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BibTeX
@misc{ghulamjelani2026flexible,
title = {{Flexible reorientation of conserved neural dynamics underlies grasp control}},
author = {Z. Ghulam-Jelani and M. T. Kaufman},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.04.749537},
url = {https://www.biorxiv.org/content/10.64898/2026.09.04.749537v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Flexible reorientation of conserved neural dynamics underlies grasp control
AU - Z. Ghulam-Jelani
AU - M. T. Kaufman
PY - 2026
DA - 2026/09/04
PB - bioRxiv
DO - 10.64898/2026.09.04.749537
UR - https://www.biorxiv.org/content/10.64898/2026.09.04.749537v1
AB - 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 activity during grasping. Here, we re-analyzed multi-area neural recordings from rhesus monkeys performing a reach-to-grasp task to many objects and found two key population-level features. First, the component of the population trajectory that was common to all grasps formed a small angle with the subspace containing grasp condition-specific (object) tuning. Second, the neural activity was well fitted by a recent, flexible dynamical model developed for reaching, called location-dependent rotations (LDR). Neural activity in M1 and F5 during grasping exhibited conserved population-level rotational frequencies, but the state-space planes in which these rotations occurred varied systematically with grasp condition. The rotational center (the location) and the orientation of the rotations related both to each other and to the kinematics of movement. While these rotations were reoriented in high dimensional space, reflecting the high-dimensional nature of hand control, the extent of their tilt into additional dimensions were small, in contrast to reaching. This structure may reflect the nature of grasping as modulations of an overall open-close motif. Together, these results indicate that the LDR dynamics framework applies to grasping as well as reaching and provides an entry point to understanding how grasp commands are generated.
ER -