Nicolas Schweighofer

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

This paper introduces a hierarchical Bayesian framework to estimate subject-specific muscle parameters using EMG and torque data, achieving high accuracy and robustness in musculoskeletal modeling.

CALM-VLM improves confidence and reliability in brain MRI classification by calibrating vision-language models and enabling selective prediction.

The paper investigates quantitative and qualitative differences in visuomotor adaptation by analyzing learning and forgetting rates, revealing two distinct learning strategies based on individual differences in fast and slow processes.

This study investigates locomotor learning in people with Parkinson's disease using a virtual obstacle task, finding faster learning rates in PD patients compared to controls.

This study investigates how perceived risk of forward versus backward balance disturbances during walking differs between young and older adults and finds no group difference in sensitivity to perturbation type.

This paper identifies distinct subtypes of post-stroke and neurotypical gait behaviors using neural network analysis of kinematic time series data, revealing five clusters with unique kinematic patterns.

The paper explains interindividual differences in motor learning by proposing a computational model with generalist and specialist memory modules that adapt based on training schedules.

The paper shows that a 40% shoulder force reserve predicts compensatory trunk movements during reaching with shoulder strength deficits, outperforming traditional control models.

This study develops a neuro-musculoskeletal model to show how compensatory motor areas are recruited post-stroke to maximize residual motor performance through homeostatic and reinforcement learning processes.

The paper identifies and generalizes walking impairment clusters in stroke survivors and controls using spatiotemporal and force data across multiple sites.

The paper analyzes the speed of learning inverse models in motor tasks using theoretical and experimental approaches, finding that task geometry and target distribution influence learning speed.

This study demonstrates that humans engage in reinforcement meta-learning to optimize visuomotor learning by adjusting their willingness to learn based on reward outcomes.

This study examines how fast walking affects gait kinematics in stroke survivors compared to neurotypical adults, finding varied biomechanical responses across metrics.

The paper predicts anterior ground reaction force and step length in neurotypical adults using gait speed and individual characteristics.

This study finds that corpus callosum microstructure predicts bimanual motor performance in chronic stroke survivors, with associations beyond primary sensorimotor regions.

This study finds that smaller ipsilesional hippocampal volume is associated with worse upper limb sensorimotor impairment in chronic stroke patients, with a stronger effect in women.

This study shows that global brain health, measured by brain age, influences post-stroke sensorimotor outcomes and mediates the effect of lesion damage.

This study finds that smaller spared subcortical nuclei are linked to worse sensorimotor outcomes after stroke, varying by time since stroke and type of impairment.

This study investigates how training dose affects motor training efficacy, efficiency, and retention in chronic stroke patients, finding increased efficacy with higher doses but decreased efficiency and mixed retention effects.

This study shows that residual muscle activity not explained by synergy extraction significantly affects motor task performance, challenging the assumption that it is noise.

This study compares the effects of high-definition tDCS over the motor hotspot and premotor cortex on motor network excitability, finding greater responsiveness and fMRI complexity changes with premotor stimulation.

This study models how electrical coupling between inferior olive neurons affects firing dimensionality and chaos, supporting the hypothesis that coupling controls learning efficiency in the cerebellum.

The paper shows that sensory prediction errors, not performance errors, update the stable component of motor memory during visuomotor adaptation.

This paper introduces a computational model of motor adaptation that explains individual differences in how the brain updates or creates memories during learning.