Retina implant adjustment with reinforcement learning

A tuning method with reinforcement learning (RL) for the retina encoder (RE) of a retina implant (RI) as a visual prosthesis for blind subjects with retinal degenerations is proposed. The RE simulates retinal information processing in real time by means of spatio-temporal receptive field (RF) filters and generates electrical signals for stimulation of several hundreds of ganglion cells (GC) to regain a modest amount of vision. For each contacted GC, the RE has to be optimized with regard to the patient´s perception. The patient´s (for the present simulated) evaluative feedback is applied here in a dialog module as a reinforcement signal to train several RL agents in a neural network learning process