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October 15, 2019 Tuesday 02:42:09 PM IST

How to Reduce Heat Generated in Artificial Retina?

Photo by Juraj Varga for Pixabay.com

Heat generated in the use of artificial retina can be reduced by significantly compressing the massive amounts of visual data that all the neurons in the eye create, according to E J Chichilnisky, a professor in the Neurosurgery and Opthalmology departments, who is on Stanford's artificial retina team. 
Research on artificial retina has been going on for a decade but the key issue was with respect to generated as it requires a very small computer chip with many metal electrodes poking out.  The electrodes first record the activity of the neurons around them to create a map of cell types. This information is then used to transmit visual data from a camera to the brain. Unfortunately, the eye produces so much data during recording that the electronics get too darn hot.
Members of the team, including Chichilnisky and his collaborators in Stanford’s Electrical Engineering and Computer Science departments, recently announced they have devised a way to solve that problem by significantly compressing the massive amounts of visual data that all those neurons in the eye create. They discuss their advance in a study published in the IEEE Transactions on Biomedical Circuits and Systems. 
To convey visual information, neurons in the retina send electrical impulses, known as spikes, to the brain. The problem is that the digital retina needs to record and decode those spikes to understand the properties of the neurons, but that generates a lot of heat in the digitization process, even with only a few hundred electrodes used in today’s prototypes. The first true digital retina will need to have tens of thousands of such electrodes, complicating the issue further. 
Boris Murmann, a professor of electrical engineering on the retina project, says the team found a way to extract the same level of visual understanding using less data. By better understanding which signal samples matter and which can be ignored, the team was able to reduce the amount of data that has to be processed. It’s a bit like being at a party trying to extract a single coherent conversation amid the din of a crowded room — a few voices matter a lot, but most are noise and can be ignored.
Source: Stanford University


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