A. Aldo Faisal

This study investigates how error-based and reward-based motor learning mechanisms interact in real-world tasks using virtual reality, finding that reward feedback does not exclusively engage specific learning mechanisms.

The paper develops a topographic autoencoder to model proprioceptive cortical coding and predicts neural manifold structures from natural movement data.

This study evaluates how motor coordination predicts performance with a robotic third thumb in piano playing, showing individual differences in augmentation capability.

This study compares motor control of artificial arms between congenital one-handers and amputees, finding that early experience with an artificial arm improves motor control.

This paper presents a spiking neural model of the cerebellum that reproduces human motor adaptation behaviors and predicts effects of cerebellar damage.

This paper develops an embodied virtual reality setup for studying real-world motor learning, showing comparable learning trends to physical tasks and enabling controlled feedback manipulations.

The paper shows that neuroinflammation in MS brains causes structural changes at the node of Ranvier, leading to axonal conduction failure.

This study investigates motor learning in real-world pool billiards, finding that learning affects whole-body movement and that initial variability in specific joints correlates with learning rates.

The paper examines movement-invariant and movement-specific signaling in layer 5B of motor cortex in mice, finding that most neurons show movement-invariant responses while a minority display movement specificity.

The paper examines PMBR dynamics in real-world motor learning and finds opposing changes in PMBR magnitude between subjects, suggesting different learning mechanisms.

The paper develops a model to predict visual attention shifts from full-body kinematics in natural tasks, expanding understanding of visuomotor behavior.

The paper studies neurobehavioural patterns in a Formula E Champion during extreme driving conditions using multimodal data to characterise human expertise.

The paper investigates how human motor coordination strategies change with metabolic state, finding that fasting reduces task-related energy expenditure by 20%.

The paper introduces a method to quantify hierarchical structure in human behaviors and applies it to analyze the complexity of stone tool-making sequences across different time periods.

This paper develops a BCI system using EEG and ConvNets for real-time, self-paced control mode switching in robotic systems, achieving 47.6% accuracy in 4-way classification.

This study uses natural eye movements to predict joystick actions for wheelchair navigation, showing that gaze-based decoding can enable mobility without artificial interfaces.

This study quantifies sensorimotor variability in elderly individuals and links it to increased fall risk through biomechanical tasks and sensory psychophysics.