Machine Learning

We develop methods for choosing stimulation patterns that produce a desired percept or neural response, using predictive models, optimization, and feedback from the user or the brain.

We developed a data-driven neural control framework for a visual cortical prosthesis in a blind human, showing that deep learning can synthesize efficient, stable stimulation patterns that reliably evoke percepts and outperform conventional calibration methods.