Motor Control and Adaptation

A Unified World Model in Human Posterior Parietal Cortex

The paper identifies the superior parietal lobule as a substrate for physical world modeling by showing neurons that predict physical events and encode intended hand actions.

Somatostatin-excitatory coordination emerges with dexterous motor skill learning

The paper shows that SST neurons become selectively coordinated with behaviorally relevant excitatory populations during motor skill learning.

Perceptual sensitivity supports online control, while perceptual errors drive motor memory formation during locomotor adaptation

This study investigates how perceptual sensitivity and errors contribute to locomotor adaptation, finding that sensitivity drives initial motor corrections while errors predict motor aftereffects.

Movement refinement and cholinergic regulation of outcome-dependent updating during skilled reaching.

This study investigates how movement refinement and cholinergic signaling influence skilled reaching in mice, finding that ACh levels and outcome-dependent adjustments play roles in updating movement execution.

Post-intervention high-beta synchronization tracks stable-manifold alignment during stick-balancing learning

This study shows that post-intervention high-beta EEG synchronization correlates with the quality of transitioning from active correction to intrinsic dynamics during stick-balancing learning.

Changes to task relevance in redundant motor environments distinguish structure learning from adaptation

This study investigates how changes in task relevance during redundant motor tasks distinguish between adaptation and structure learning mechanisms in monkeys.

Making sense of touch: spatial tactile cues promote efficient visuosomatosensory integration and restore motor accuracy under sensory conflict

The paper shows that spatial tactile cues improve motor accuracy and stability during sensory conflict by enhancing visuosomatosensory integration.

The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex

The paper shows that PPC neurons encode hand-eye movements additively, allowing modular decoding of coordinated movements.

Competition Between Task Accuracy and Habitual Locomotor Behavior Underlies the Speed-Accuracy Trade-Off in Walking

This study shows that walking speed-accuracy trade-off is driven by energetic optimization rather than motor noise or visual feedback.

An integrative computational approach for obstacle avoidance during action selection

The paper develops a computational model to explain action selection in cluttered environments by integrating goal pursuit and obstacle avoidance through a weighted combination of control policies.

Glutamatergic Neurons in caudal Pontine Reticular Nucleus (PRNc) Mediate Backward Locomotion in mice

This study identifies glutamatergic neurons in the PRNc as critical for backward locomotion in mice through optogenetic manipulations and anatomical tracing.

Corticospinal neuron subpopulations with opposing dynamics converge to shape spinal interneuron computations

The paper identifies corticospinal neuron subpopulations with opposing activity dynamics and shows they converge to shape spinal interneuron computations during skilled movement.

From Arm to Leg: A Shared Primitive Framework for Solving Inverse Kinematics in Human Movement

This study extends a modular control framework from upper-limb drawing to lower-limb gait kinematics to explore how CNS pathologies affect movement organization in a shared primitive space.

Toward naturalistic reaching: Intermediate reaches under uncertainty generalize to walking, but are not obligatory

Selectivity Dilution in Three-Factor Learning: A Finite-Population Model with Potential Implications for Motor Development and Post-Stroke Motor Rehabilitation

The paper proposes a framework to understand how the absence of a fluid low-resistance phase affects motor learning and recovery, using a three-factor learning model.

Neural populations in the dorsal and ventral cervical spinal cord share temporal dynamics during reach

The paper examines neural population dynamics in the dorsal and ventral cervical spinal cord during reaching, finding shared dynamics across these regions despite differences in single-neuron activity.

Abstract Representations of Sensorimotor Transformations in Human Premotor Cortex

This study investigates how human premotor cortex encodes sensorimotor transformations and finds that it represents both movement direction and transformation context, with performance linked to transformation context encoding.

Reach plans for grasping vs. placing objects evoke different neural dynamics within a shared cortico-cerebellar mechanism.

This study compares neural dynamics during grasping and placing tasks using EEG, finding shared cortico-cerebellar mechanisms with distinct spatiotemporal patterns.

Grasping Under Target Size and Location Uncertainty

The paper investigates how grasping is planned under target uncertainty using a GBYK paradigm and finds asymmetric interference from nontarget objects.

Eye versus hand effector-specificity in the parieto-frontal cortex and cerebellum in humans

This study uses fMRI to examine effector-specific neural activity during saccade and reach movements, finding that multivariate analyses reveal distinct spatial patterns despite overlapping activation.

Distributed neural dynamics underlie the shift from movement preparation to execution

Magnetoencephalography during memory-guided finger sequences shows preparation and execution occupy distinct, partly overlapping subspaces; execution patterns appear first in hippocampus and last in M1, 160 ms before the first press.

An Engineering Explanation of Human Vertical Reaching: Optimal Trajectory Planning of Asymmetric Velocity Profiles in the Vertical Plane via a Softplus-Based Generalized Dynamic Jerk Cost Function.

This paper introduces a Generalized Dynamic Jerk cost function to model human vertical reaching, showing it produces more realistic and biomechanically consistent trajectories by incorporating gravitational effects.

Motor automaticity in natural keyboard typing

This study investigates motor automaticity in keyboard typing by analyzing sequence frequency and performance metrics in natural and pseudo-word strings.

Flexible representation of dynamics allows precise tuning to the environment

The paper investigates how the sensorimotor control system represents external forces and shows that representations are flexible and adapt based on environmental demands.

Computer mouse weight is associated with one-week motor skill improvement in a large naturalistic video game cohort

The paper analyzes the association between computer mouse weight and one-week motor skill improvement in a large video game cohort using regression and permutation tests.

Learning source weights for target-position estimation: comparing fixed weights and an input-dependent rule

In simulation, under reduced accuracy or temporally correlated errors, recalibration or fixed-weight learning captured most gains; a disagreement-dependent rule beat fixed weights mainly under occasional large errors (4.05% lower MSE).

High-level visual and motor regions are doubly dissociated in controlled tasks but only at specific times during natural behaviors

The paper shows that high-level visual and motor brain regions show a double dissociation in controlled tasks, and this dissociation persists at specific times during natural behaviors.

Characterizing bilateral M1 excitability changes after learning a novel spatiotemporal coordination pattern

This study investigates how learning a novel coordination pattern affects motor cortex excitability, finding comparable behavioral improvements across bimanual and unimanual training without consistent neurophysiological correlates.

Innate sensorimotor policies constrain cerebellar adaptive correction

The paper shows that innate sensorimotor policies interact with cerebellar feedback to enable adaptive correction in zebrafish, revealing innate structure as a constraint on cerebellar adaptation.

Neural networks learn forward dynamics when freed from numerical integration

The paper shows that a two-stage model (APE) outperforms a direct-mapping neural network in predicting limb dynamics under novel conditions by explicitly modeling equations of motion and numerical integration.