Nidhi Seethapathi

The paper shows that suboptimal locomotor performance in novel environments results from prioritizing fall risk avoidance, using an inverse adaptation model to infer internal learning parameters.

The paper investigates foot placement control across species, finding shared behavioral signatures and varying control mechanisms based on neuromechanical embodiment.

This study develops magnetoelectric nanodiscs (MENDs) for deep brain stimulation in Parkinson's disease models, showing motor improvements and reduced oxidative stress compared to traditional DBS.

This paper models locomotor adaptation as interactions between a stabilizing controller and a reinforcement learner, predicting adaptation in various scenarios and capturing learning phenomena.