Visual Prostheses

We study how stimulation of the retina, LGN, and visual cortex produces artificial vision, and which biological details matter most for predicting the resulting percepts.

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.

Archived: the Smart Bionic Eye effort has grown into several of our current research areas. This page is kept so that existing links continue to work.

BionicVisionXR is an open-source virtual reality toolbox for studying simulated prosthetic vision during natural head and body movement.

pulse2percept is an open-source Python framework for modeling how retinal and cortical visual prostheses produce percepts.

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.

Rather than pursuing a (degraded) imitation of natural sight, bionic vision might be better understood as a form of neuroadaptive XR: a perceptual interface that forgoes visual fidelity in favor of delivering sparse, personalized cues shaped (at its full potential) by user intent, behavioral context, and cognitive state.