Olivier Codol

The paper shows that recurrent neural networks (RNNs) exhibit motor learning savings without explicit contextual cues, suggesting a neural basis for memory retention through high-dimensional activity shifts.

The paper shows that neural dynamics during movement are conserved across mice, monkeys, and humans, despite phylogenetic differences.

This study shows that sensory expectations influence motor preparation and corrective responses in humans and monkeys, with neural activity scaling with perturbation probabilities.

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

This paper introduces exponentiated gradient (EG) as a learning algorithm for artificial neural networks that better aligns with biological principles than gradient descent.

This study shows that reinforcement learning produces neural dynamics in artificial networks that match biological motor cortex activity, suggesting RL underlies behavioral control development.

MotorNet is a Python toolbox that enables training artificial neural networks on differentiable biomechanical effectors for motor control tasks.

This study investigates how reward affects seven sensorimotor feedback loops in humans, finding that faster loops are insensitive to reward while slower loops show latency reductions.

This study uses a computational model of the basal ganglia to show that greater D1 receptor sensitivity compared to D2 improves action selection.

The paper investigates how reward affects motor control by disrupting brain areas involved in reward processing and motor control, finding that transcranial magnetic stimulation did not alter reward-driven improvements in movement.

This study shows reward improves motor performance by enhancing feedback control and reducing motor noise through increased arm stiffness.

This study shows that working memory capacity, not dopamine-related genetics, predicts success in reward-based motor learning tasks.

This study investigates how explicit strategies influence reward-based motor learning, finding that strategy use is critical for successful learning and that its disruption impairs learning.

This study investigates how reinforcement and explicit strategies interact during visuomotor adaptation, finding that strategy-use is critical for reinforcement-based learning.