Cynthia R. Steinhardt

This study compares galvanic and pulsatile stimulation effects on a decision-making neural network model, finding that low-amplitude galvanic stimulation produces more distributed responses, while high-amplitude galvanic stimulation can directly activate or block neurons.

This study uses computational modeling to explain how pulsatile electrical stimulation affects neural firing rates and shows that these effects can be predicted and corrected.

This paper introduces an adaptive closed-loop stimulation system that learns to generate specific neural activity patterns in real-time.