Retinal prostheses promise a potential to restore sight in millions of blind people, but new milestones in this technology have been difficult to achieve. Most advancements have improved signal gathering and transmission, but that’s like turning up the volume on a conversation in a language you poorly understand. One major stumbling block has been deciphering the code that is used for the retina to communicate with the brain.
Researchers at Weill Medical College of Cornell University focused on deciphering the signal and mimicking it to replicate a normal retina using a prosthesis. The research showed substantial improvement (see image) in the quality of the image delivered to the brains of rats. The team believes that with further improvement, it should be possible in the not too distant future to achieve a quality of vision that approaches that of healthy individuals.
From the study abstract in Proceedings of the National Academy of Sciences:
Efforts to improve prosthetic capabilities have focused largely on increasing the resolution of the device’s stimulators (either electrodes or optogenetic transducers). Here, we show that a second factor is also critical: driving the stimulators with the retina’s neural code. Using the mouse as a model system, we generated a prosthetic system that incorporates the code. This dramatically increased the system’s capabilities—well beyond what can be achieved just by increasing resolution. Furthermore, the results show, using 9,800 optogenetically stimulated ganglion cell responses, that the combined effect of using the code and high-resolution stimulation is able to bring prosthetic capabilities into the realm of normal image representation.
More from Weill Cornell: An Artificial Retina with the Capacity to Restore Normal Vision…
Abstract in PNAS: Retinal prosthetic strategy with the capacity to restore normal vision