Here is a video that presents some ideas from ‘neuromorphic engineering’. Neuromorphic engineering attempts to mimic neural architectures on silicon chips. The idea, as the video communicates, is that the brain is really, really good at what it does. An equivalent computer uses vastly more power and is unable to do most of what the human brain can do, so maybe we should try to get some inspiration from the brain. A lot of people are working on this problem, including Gert Cauwenberghs here at UCSD.
Clearly, a lot of the difference has to do with the software of the brain: it is simply better than anything we have created. But it also has to do with the architecture. The reason the brain is so good at what it does is that it has evolved that way for a long, long time. Everything in it is optimal for the task at hand; that doesn’t mean it is perfect in every situation (see Brain Hacks), but it is usually the best at what it needs to do. Experiment after experiment show that the brain performs in the mathematically best and most efficient way possible. This makes sense, right? Why would the system evolve the way it did if there was a more efficient path? Since the body has a limited amount of energy, efficiency includes metabolic constraints. Although it still consumes roughly a quarter of our body’s energy it is still much more efficient than a computer.
Obviously, then, one would want to copy the brain to get the best possible computer! However, even simple systems haven’t been fully worked out. The retina is probably the most mechanical piece of neural architecture, and there is so much we don’t know about it that it would be impossible to replicate it. I am not sure how the guy in the video plans on making a functional retina without this knowledge. We know the gist of how a lot of it works, but we have a long way to go before we can fully realize the energy savings available.
[Via Balaji’s status message]