Retina Encoder Inversion for Retina Implant Simulation

Abstract

To test tuning methods for the retina encoder (RE) of a retina implant (RI) as a visual prosthesis for blind subjects with retinal degenerations a suitable simulation of the patient's evaluative response to RE state alterations must be provided. RE simulates real time retinal information processing and consists of several hundreds of spatio-temporal receptive field (RF) filters to generate electrical signals for ganglion cell (GC) stimulation. We propose a neural network to reconstruct the RE input from a number of consecutive RE output frames. The network can be interpreted as a simulation of a part of the central visual system with GC signals as input and perception visualisation as output. We present first results using evolution strategies for neural network weight optimization