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.

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.

BionicVisionXR is an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to “see through the eyes” of a bionic eye recipient.

Rather than aiming to one day restore natural vision, we might be better off thinking about how to create practical and useful artificial vision now.

Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?

What do visual prosthesis users see, and why? Clinical studies have shown that the vision provided by current devices differs substantially from normal sight.

pulse2percept is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.