Biologically Inspired Algorithms for Restoring Vision to the Blind
Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Analogous to cochlear implants, retinal prostheses use a grid of electrodes to stimulate surviving retinal cells in order to evoke visual percepts. However, a major outstanding challenge in the use of these devices is translating electrode stimulation into a code that the visual system can interpret. A common misconception is that each electrode in the grid can be thought of as a 'pixel' in an image; to generate a complex visual experience, one then simply needs to turn on the right combination of pixels.
Contrary to this belief, I will present recent evidence showing that the generated visual experience includes nontrivial perceptual distortions caused by interactions between the implant electronics and the retinal neurophysiology. I will present a computational model based on clinical and psychophysical data that accurately predicts these distortions across a wide range of subjects and implant configurations. I will discuss how detailed knowledge of the visual system can be combined with data-driven techniques to develop novel encoding algorithms aimed at minimizing distortions and improving patient outcomes. I will outline future strategies for leveraging virtual/augmented reality to quickly and efficiently test novel stimulation strategies in real-world tasks using visually typical individuals as 'virtual patients'.