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. RE simulates retinal information processing in real time by means of spatio-temporal receptive eld (RF) lters and generates electrical signals for stimulation of several hundreds of ganglion cells (GC) to regain a modest amount of vision. For each contacted GC, 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 (see also http://www.nero.uni-bonn.de).