Design of an Integrated Subretinal Implant using Cellular Neural Networks for Binary Image Generation in a 130 nm...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Computer Engineering

Background:

• Retinitis Pigmentosa (RP) and Age-Related Macular Degeneration (AMD) cause photoreceptor degeneration, leading to blindness.
• Remaining postsynaptic retinal cells offer a potential target for visual prosthetics.

Purpose of the Study:

• To propose a new concept for retinal implants that generates binary edge-focused images.
• To stimulate only the essential edge points of visual information to preserve retinal function.

Main Methods:

• Development of a novel retinal implant strategy using binary image generation.
• Design of an integrated test circuit using cellular neural networks (CNNs).
• Implementation in a 130 nm BiCMOS process for efficient hardware realization.

Main Results:

• The proposed circuit successfully generates binary images highlighting visual edges.
• Simulated power consumption is low, with a maximum of 2.61 mW for 1000 processing cells.
• This method offers a power-efficient approach for visual prosthetics.

Conclusions:

• The novel binary edge-detection approach is a promising strategy for retinal implants.
• This technology could lead to improved visual prosthetics for RP and AMD patients.
• The low power consumption makes it suitable for long-term implantation.