Retina implants are currently being developed by several interdisciplinary research consortia worldwide for blind humans with various retinal degenerative diseases. It is the aim of our retina implant project to develop a novel type of visual prosthesis to regain a moderate amount of vision such as perception of location and shape of large objects in the first stage and to approach reading quality in a subsequent stage. In our planned retina implant, a retina encoder (RE) outside the eye has to replace the information processing of the retina. A retina stimulator (RS), implanted adjacently to the retinal ganglion cell layer, has to contact a sufficient number of retinal ganglion cells/fibers for electrical elicitation of spikes. A wireless signal and energy transmission system has to provide the communication between the RE and RS. This paper outlines the retina implant project of our consortium of 14 expert groups and describes first results of the learning RE. The RE approximates the typical receptive field (RF) properties of primate retinal ganglion cells by means of individually tunable spatiotemporal RF filters. The RE as a cluster of RF filters maps visual patterns onto spike trains for a number of contacted ganglion cells. A concept is presented to train the individual RF filters in an unsupervised learning process, which employs neural networks in a dialog with the individual human subject. The desired aim of this dialog is an optimization of the visual perception by matching the various RF filter properties with those 'expected' by the central visual system for each contacted ganglion cell.