Analog Computing: The Future of Neural Information Processing?

Ask pretty much any audiophile their preference between analog vinyl albums and digital compact disks and, odds are, the answer will almost unanimously be record albums produced from analog recordings. However, ask Author and Neuroscientist Dr. Bruce MacLennan about the key to understanding neural information processing and you might be surprised when he answers, “analog computing.”

According to MacLennan, that “back to the future” idea of using analog computing to understand the brain has returned to fashion after falling out of favor with early artificial intelligence researchers in the mid-1980s. That’s because modern researchers have recognized that, if we’re going to achieve artificial intelligence comparable to what humans or even other ma

mmals possess, we must first understand the human brain, which basically functions like an analog computer, he added.

“The analog processing of information is more efficient than digital processing of information. We’re so enchanted with the flexibility and speed of digital technology, but the tradeoffs are different,” MacLennan said. “Look at the brain, which uses components which are orders of magnitude slower than the transistors in our current technology, but yet it’s still able to do things we can’t do very well with our digital technology. Part of the reason is that it’s using low precision analog computing, but in a massively parallel scale.”

MacLennan cited Carver Mead, who was an innovator in VLSI (very large scale integration) digital circuitry, and his statement from the 1980s that the future of electronics is in analog VLSI. Mead based that conclusion on his studies, which indicated the brain is primarily an analog information processor, MacLennan said, and over the past 10 or 15 years, there has been increasing recognition of that and a subsequent rediscovery of the value of analog electronics. Much of that, he added, has been inspired by brain-oriented computing in general.


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