Tunable retina encoders for retina implants: why and how.

Current research towards retina implants for partial restoration of vision in blind humans with retinal degenerative dysfunctions focuses on implant and stimulation experiments and technologies. In contrast, our approach takes the availability of an epiretinal multi-electrode neural interface for granted and studies the conditions for successful joint information processing of both retinal prosthesis and brain. Our proposed learning retina encoder (RE) includes information processing modules to simulate the complex mapping operation of parts of the 5-layered neural retina and to provide an iterative, perception-based dialog between RE and human subject.

Learning retina implants with epiretinal contacts.

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.

Recognition and tracking of impulse patterns with delay adaptation in biology-inspired pulse processing neural net (BPN) hardware.

The application of an electronic real time emulator for biology-inspired pulse processing neural networks (BPN) to recognition and temporal tracking of discrete impulse patterns via delay adaptation is demonstrated. The electronic emulation includes biologically plausible features, such as asynchronous impulses, membrane potentials and adaptive weights, as well as a mechanism to modify signal delays. The rule for the adaptation of impulse propagation delays is as follows: 'error neurons' detect temporal differences between single impulses of other neurons and adjust corresponding signal delay parameters.

Coherent optical neural networks that have optical-frequency-controlled behavior and generalization ability in the frequency domain.

Coherent optical neural networks that have optical-frequency-controlled behavior are proposed as sophisticated optical neural systems. The coherent optical neural-network system consists of an optical complex-valued neural network, a phase reference path, and coherent detectors for selfhomodyne detection. The learning process is realized by adjusting the delay time and the transparency of neural connections in the optical neural network with the optical frequency as a learning parameter.