Retina encoder tuning and data encryption for learning retina implants

A retina encoder (RE) as part of a visual prosthesis (retina implant) for blind subjects with retinal degenerative disorders was implemented by an array of 256 tunable spatio-temporal filters (ST) to map visual patterns P1 onto encoded output patterns as functional approximation of retina information processing. These RE-output signals were fed into a visual system module (VM) to simulate the mapping (by the central visual system) of the retinal output onto visual percepts P2, which were visualized on a monitor. Alternative tuning strategies for roaming within the large spatio-temporal state space of RE were developed and tested. Initially, VM as neural network was trained to generate a P2 very similar to a selected set of P1s by feeding the corresponding RE-outputs into VM. RE tuning was tested by presenting a given small set of P1s to the serial coupling of RE and VM and by monitoring a modified Hamming distance between P2 and P1, while the ST-parameters were tuned. Human volunteers with normal vision were asked to approximate P2 to P1 directly, with random search, or by means of a dialog-based tuning with an evolutionary algorithm (EA). Typically, dialog-based EA tuning reached the optimal similarity between P2 and P1 (limited by the quality of VM) within less than 200 iterations. In contrast, manual tuning required considerably more iteration steps and converged to a smaller similarity between P2 and P1. Structure and function of our tunable RE offer not only an optimization of visual perception, but may also be important to serve as technical encryption unit (EU) in connection with the human central visual system as biological decryption unit (DU); an important feature to meet the authentication requirements of active medical devices.