Partitioned Temporal Dithering for Efficient Epiretinal Electrical Stimulation.

Retinal implants are designed to restore vision to people blinded by photoreceptor degeneration by evoking spiking activity in surviving retinal ganglion cells (RGCs) and thus conveying artificial visual signals to the brain. One approach is to implant a high-density micro-electrode array on the surface of the retina and apply electrical stimuli to reproduce the rich spatiotemporal patterns of visually evoked RGC responses. However, this requires stimulation through multiple electrodes, which is complicated by the non-linear superposition of single-electrode responses. A recently published approach to this problem, greedy temporal dithering (GTD), optimally approximates the visually evoked activity by rapidly interleaving stimulation at different locations on the array. However, the GTD algorithm is too computationally demanding to use with an implantable chip in real-time with present-day hardware, and it scales poorly with the number of cells addressed. Here we present a computationally efficient implementation of the GTD algorithm by exploiting the fundamental anatomical and morphological properties of the underlying neural circuitry. Efficiency is achieved by partitioning distinct groups of cells according to their overlapping sensitivity to electrical stimulation, which in turn allows for efficient parallel stimulation. Using high-density multi-electrode recording and stimulation in the primate retina as a lab prototype for a future implant, we demonstrate a 10-fold decrease in GTD active compute time using the partitioning scheme with less than a 10% reduction in the accuracy of visual reconstruction.