  Visual neuroprostheses are the only FDA-approved technology for the treatment
of retinal degenerative blindness. Although recent work has demonstrated a
systematic relationship between electrode location and the shape of the
elicited visual percept, this knowledge has yet to be incorporated into retinal
prosthesis design, where electrodes are typically arranged on either a
rectangular or hexagonal grid. Here we optimize the intraocular placement of
epiretinal electrodes using dictionary learning. Importantly, the optimization
process is informed by a previously established and psychophysically validated
model of simulated prosthetic vision. We systematically evaluate three
different electrode placement strategies across a wide range of possible
phosphene shapes and recommend electrode arrangements that maximize visual
subfield coverage. In the near future, our work may guide the prototyping of
next-generation neuroprostheses.
