Mehrdad Kashefi

This paper introduces DynISys, a model that infers neural dynamics and communication from multi-area recordings, revealing mechanisms for motor planning and execution.

This study shows that reward enhances auditory pattern encoding in the primate globus pallidus, with responses persisting even after reward is removed.

The paper investigates whether increased reaction time for longer movement sequences reflects sequence preparation or first-target ambiguity, finding that RT increases are fully explained by ambiguity, not sequence preparation.

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

This study investigates neural population dynamics in motor cortex during reaching, revealing a compositional structure that enables posture-dependent control.

This study disentangles the mechanisms of motor sequence learning by distinguishing between anticipation of next actions and optimization of movement trajectories using a continuous reaching task.

The paper investigates how the brain plans multiple future arm movements during sequential actions, finding that planning processes interact and influence each other.

The paper investigates neural mechanisms of online action preparation during sequential movements, finding increased activation in parietal and visual areas for overlapping actions, while premotor cortex remains unaffected.

The paper introduces Myomatrix arrays, a high-resolution electrode technology for recording muscle activity from motor units across species during natural behaviors.

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.