Motor Control and Adaptation

The paper uses Utah array recordings to show that the human motor cortex encodes handwriting through a sequence of primitive neural states, improving trajectory reconstruction accuracy by 84%.

The paper introduces muSim, a framework that uses RNNs and reinforcement learning to model motor cortex control of movement through musculoskeletal simulations, showing it can decode neural activity and generalize to new conditions.

This paper investigates how the nervous system represents arm movements in the presence of kinematic redundancies, finding that absolute reference frames better represent joint space for inverse kinematics.

The paper uses supervised deep learning models to decode forelimb movement from neuronal ensembles in CFA and RFA, finding distinct activity patterns between the two areas.

The paper shows that motor cortex maintains similar neural dynamics during motor execution and imagery by recreating missing components in a unique imagery subspace.

This paper proposes a perceptual error framework based on Bayesian cue combination to explain implicit motor adaptation, showing it outperforms sensory error-based models.

This study examines how motor cortical activity in mice adapts during movement preparation based on the predictability of go timing, finding that predictable timing leads to more accurate movements with longer reaction times.

This study investigates whether early-stage Alzheimer's disease affects the fast, declarative component of motor adaptation, finding that AD patients have reduced retention rates in this process while the slow, non-declarative process remains unaffected.

The paper investigates Essential Tremor by testing a hypothesis that impaired delay compensation in feedback control mechanisms causes the tremor, using experimental and computational methods.

This study investigates how retinal acceleration error influences catch-up saccade timing and amplitude during tracking of accelerating targets.

This study investigates the role of climbing fiber transmission in different phases of motor memory formation and finds that it is critical for memory acquisition but not for consolidation or retrieval.

This study uses optogenetic stimulation to investigate cerebellar roles in saccade production, finding directional differences in how the oculomotor vermis contributes to forward and inverse models of eye movement.

The paper shows that Purkinje cells in the cerebellum use population-level modulation of complex spike activity to encode error magnitude and direction during motor adaptation.

This study shows that Purkinje cells in larval zebrafish use expectation and error signals to update internal models of optic flow stimuli, leading to faster behavioral responses.

This study investigates how spinal cord dynamics influence cerebellar motor learning and control using computational models of the cerebellum and spinal cord integrated into a musculoskeletal control loop.

This study shows that increasing dopamine availability improves aiming accuracy and extends deliberation time during explicit motor skill learning in humans.

This study investigates how the relevance of visual feedback affects implicit sensorimotor adaptation, finding that task-relevant feedback enhances aftereffects while distracting feedback reduces them.

The paper investigates how mice encode and use rule information in a sensory selection task, showing that preparatory activity in motor cortex reflects task rules and is necessary for rule-guided behavior.

This paper models spinal sensorimotor circuits to show they can generate triphasic motor commands for arm movements without brain input, using a Goal Exploration Process to refine commands.

This study identifies corticocortical connections of the rostral forelimb area in rats, showing similarities to primate ventral premotor cortex connections.

AngoraPy enables training of complex recurrent convolutional neural networks for sensorimotor systems, facilitating goal-driven modeling in computational neuroscience.

The paper proposes a divisive normalization mechanism to explain how the motor system processes uncertain visual feedback during visuomotor learning.

This study investigates the role of kinesthetic and visuospatial cues in pain-induced movement avoidance by separating these cues in experimental contexts.

This study shows that under time pressure, humans rely more on prior target location information when sensory target precision is low, suggesting Bayesian integration of uncertainty in sensorimotor control.

The paper introduces a new device to image finger pad skin deformation during manipulation and reports low correlations between skin strain and grip force adaptation across participants.

The paper investigates how reward magnitude and target probability influence sensorimotor processing by decoding movement direction from EEG data, finding that reward magnitude enhances spatial precision of neural representations.

The paper develops a software tool for at-home sensorimotor adaptation studies, showing similar behavioral results to lab settings.

This paper uses a computational model to show that α-MN collaterals to γ-MNs reduce velocity-dependent stretch reflex disruptions during voluntary movement.

This study investigates how motor memory encoding in bimanual movements depends on the clarity of the relationship between limbs, showing differences in memory formation and generalization based on training conditions.

This study uses an RNN model of motor cortex to show that internal dynamics and proprioceptive feedback interact during movement execution, with feedback controlling movement termination and improving robustness.