Next-generation bionic eyes are practically here today. Imagine a blind person’s real-world conundrum trying to shop for one — they could schedule surgery for Nano Retina’s implant today and see their daughter’s wedding in 576-pixel clarity, but it would cost them their life’s savings. The Nano Retina 5000-pixel device could be ready tomorrow, or in another six months… and would be much more affordable. When the procedure involves assimilation of an electrode pincushion into the ganglionic tentacles of your retina, hardware upgrades are not as simple as popping in more RAM. What kind of decision matrix could be offered under such critical circumstances?

Cochlear implants, used to restore hearing, work phenomenally well when properly tuned and fitted. Most are refinements of the basic piece of hardware one might have sitting on their bookshelf — the graphic equalizer. The implant processes a single audio stream into bins of various sizes according to frequency, and then applies current to the corresponding frequency location in the cochlea, typically with a 16-spot linear electrode. The main function of these devices is to capture speech formants — the peaks in the frequency spectrum of the voice. The toughest challenge for the cochlear implant is to provide sound localization and source separation in noisy environments like a cocktail party.

Vision implants are much more complex. As any practiced photographer knows, the eye is more than a camera. The optic nerve does not feed the brain pixels. If you imagine your camera responding to auto-selected targets several times a second, gathering the full spectrum of light through its entire range of settings at each pause, and compressing the data onto a bandwidth- and energy-limited channel ideally matched to its receiver, you have some idea of what the retina accomplishes routinely.

The reason cochlear implants work so well is that the brain is just that good at making sense out of virtually any kind of signal it is given. If presented only with noise, or with nothing at all, the brain will eventually begin to manufacture hallucinations. If the implant signal contains even some distorted fragment of the original signal, it can be made to work convincingly. This is also the reason why retina implants can work without incorporating any knowledge of what the retina actually does in the healthy state.

These days researchers are trying to do a little better than the grainy images provided through our current implants. Signal processing techniques were developed in the Cold War era to track and target incoming missiles by extracting signals from noisy radar data. These same techniques are now used to convert the activity of groups of neurons in the motor cortex into a set of commands for moving a cursor, prosthetic device, or de-enervated limb in brain machine interfaces (BCIs). These methods and derivations of them can also be applied to incoming sensory data and can approximate what the retina actually does, without doing it in the same way.

Unfortunately, videos and TED talks are not the places where this kind of knowledge is typically transmitted in much depth. For that, one needs to look back to the work of the founding father of cybernetics, Norbert Wiener, and his eminently practical inspiration, Vito Volterra. After suggesting that helium be used instead of hydrogen in airships, to great success, Volterra shifted gears and came up with some methods to characterize complex systems. Wiener simplified Volterra’s equations and they are now widely used today in statistical techniques like linear regression analysis, and analysis of spike trains from neurons.

Next page: The future of high-res bionic eyes