Towards Learning Retina Implants: How to Induce Visual Percepts with Electrical Stimulation Patterns

Abstract

We studied the conditions for joint information processing of a learning retina implant and central visual system in humans with normal vision in preparation of future retina implant applications in the blind. The visual system was modeled by a retina module (RM) as a learning Retina Encoder (RE) with spatio-temporal (ST) filters and a central visual system module (VM). RE performs a mapping of an optical pattern P1 from the physical- onto a neural domain, whereas VM performs a mapping from the neural- onto the perceptual domain and yields a visual percept P2. Our simulation results suggest that the elicitation of ‘Gestalt’ percepts may be improved by dialog-based RE tuning with evolutionary algorithms and by simulated miniature eye movements. However, considerable efforts in neuroinformatics are still needed to elucidate not only the algorithmic representation of data in the neural domain but also its enigmatic mapping onto the perceptual domain.