Neurocomputers
http://www.inyouremail.com/computerezine/articles/20001005-2.shtml
Next the team wants to build a neurocomputer sophisticated enough to learn
tasks, such as how to move the legs of a robot walking over a boulder-strewn
landscape or to recognize abstract spatial patterns, including stick-figure
drawings of people. Either accomplishment will be difficult to pull off.
"Compared to learning how to walk, calculus is easy," DeWeerth says. And harder
problems require more neurons. "We need hundreds of thousands of neurons to
solve these complicated tasks," he says. Which presents a major challenge: "How
do we program them all?"
In one sense, it should be easy. "Very simple rules can generate complex
behavior," Ditto says. Forager ants, for example, create elaborate civilizations
out of a mere handful of very simple rules. But how do you figure out the
fundamental set of simple rules?
It is a question that may never have to be answered. "We don't know how a
biological system self organizes," DeWeerth says, "but we might not have to
understand it to exploit it:' Instead of linking every neuron via computer, the
team plans to connect a computer to a small number of neurons and allow them to
communicate with a much larger network of neurons. The computer interface will
stimulate the neurocomputer in the same way that our eyes, ears, noses, and
hands provide sensory stimulation to our brains. By sending information and
feedback through the interface, "we will teach the neurons to make the right
connections themselves," DeWeerth says.
Constant repetition may be the key "The brain adapts continuously, so we keep
getting better at tasks that we repeat," DeWeerth says. For example, when a
novice tennis player lofts a ball above his head and hits it, the brain
gradually learns to coordinate the muscles needed to serve the ball. But
teaching neurons takes time. Just imagine how many serves Pete Sampras had to
hit before he won at Wimbledon.
Fortunately, neurons love to practice, so Ditto's team is working hard to get
them started. "We are now gearing up to use two- and three-dimensional pieces of
neural tissue for computing; he says. Within seven years, he hopes to teach a
millimeter-sized cube of neurons to da arithmetic and recognize patterns.
Because it is impossible to insert a computer moderator between all the
different nerve layers, this will be the first attempt at letting the neurons
make their own interconnections. Ditto acknowledges that "there are still lots
of engineering headaches. But once you get the neurons started, you almost can't
stop them from computing."
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