He's hoping that machine learning will compensate for the data glut. When American adventurer Steve Fossett disappeared in the Nevada desert last year, a virtual worldwide hunt ensued. People combed obsessively through Google Earth images for signs of the man and his plane. While telescopes and microscopes can image incredibly fine details, they still lack the all-important ability to interpret these images and throw away unnecessary data. Adler estimates that processing all the data from a 10-billion-pixel-per-second representation of the fruit-fly brain could take five years. So Mitya Chklovskii, Janelia's resident theoretical neurobiologist, is trying to teach his computers to discriminate neurons from synapses, and synapses from axons. A computer that could store an enormous image for 5 minutes while it decides which data is relevant, Janelia director Rubin says, is a far more elegant solution than a bigger hard drive. ”This would solve the data problem,” he says.
Let's say all the engineering problems can be solved in the next five or 10 years. Could researchers then actually reverse engineer the human brain, creating its functional duplicate in silicon? Would consciousness and all its attendant joy, pain, insanity, and genius be freed from biological containment? Adler sees no reason why not. ”The brain is the ultimate micromachine,” he insists. ”The fact that it's made out of meat is a red herring.”
His vision is a Google map of the human brain that incorporates not just Janelia's circuit diagrams but also other work in neuroscience. Adler cites the work of Stanford neuroscientist Stephen Smith as ”the first steps to finding the soul.” At Harvard's Center for Brain Science, neuro-scientist Jeff Lichtman mapped mouse neurons by ”painting” them with fluorescent proteins. Rubin believes he'll live long enough to see an MRI-like device that measures function with such high-resolution output that neurons in fruit flies, mice, or even humans can be observed taking in and processing information in real time.
How would all these different systems work together to show us how the brain does what it does? With his 10- billion- pixel-per-second microscope, Adler is confident he'll be able to produce brain-topography images like Google's satellite views, resolving fine details in sharp focus. Smith's cartography, on the other hand, he compares with Google's map views, including street names. Rubin's fMRI data would be like real-time traffic data. Layering these different maps atop each other, says Adler, could lead to a hybrid comparable to a Google map.
Such a Google-mapped brain, Adler says, could do more than let us understand and cure disease: it could lead to a map of human consciousness. And he believes that understanding the wiring of the brain could lead to transformative technologies. What are memories, he asks, but rewired patterns in our brains? ”If you can understand how memories are formed,” he says, ”you can create memories.” Just as today's sophisticated circuit-editing tools can modify microchips after they've been manufactured and packaged, a brain-editing tool could perhaps one day modify the brain. Adler jokes about an application straight out of Total Recall : buying fond memories of a vacation instead of taking the actual trip.
In this heady context, the leap from reverse engineering the human brain to building a thinking machine doesn't seem ridiculous. To Adler, the existence of human beings is proof enough that humans can be engineered. ”When we study biology, we're just studying a different version of nano technology--only it's a more advanced nano technology.” But he quickly qualifies that statement: silicon is the wrong material, he adds. The nano technology we use today is static; we can move electrons around but not atoms, which means the chip doesn't change when you use it. ”We may not ever be able to get there using the silicon technology of moving electrons,” he says. ”But someone could come along tomorrow and invent a different way of making a circuit that's closer to what the brain does. Then, within 50 to 100 years, we'll have something that can do what the brain does.”
But there's nothing like a little healthy competition to speed up this timetable. Janelia isn't the only player in the high-speed brain-imaging arena: both Harvard and the Max Planck Institute for Medical Research, in Heidelberg, Germany (where the 3-D SEM method of brain reconstruction was actually invented), are also working on the brain problem, and they compete heavily for milestones. The Harvard team may have solved the image-settling problem: they plan to adapt a conveyor-belt device used in the semi conductor industry as a continuously moving stage that allows an uninterrupted panoramic image, eliminating the need for time-wasting, steadying pauses.
Adler also consults for Harvard, helping its team push the limits of its existing SEMs by ”supercharging them” to hit their full potential. Before a 10-billion-pixel-per-second microscope can be useful, he says, many other roadblocks have to be negotiated. So in the meantime, he takes these souped-up SEMs to the limits imposed on them by physics, not factory settings. That means, for instance, that a lab microscope with a default rate of 10 million pixels per second can jump to 100 million pixels per second after Adler is finished tweaking it.
Despite all the obstacles, the good news, Adler says, is that the fundamental physics of the superhigh-throughput electron microscope has been resolved. It's no longer a science problem, he says: now it's an engineering problem. Hess agrees. ”Finding that one 65-nm shorted-wire defect in a Pentium chip and that one miswired neuron in a fruit-fly brain,” he says, are fundamentally similar problems. They're counting on the inexorable climb of Moore's curve to aid them in their process. Rubin describes the phenomenon in terms of his Ph.D. work sequencing a single yeast gene. ”Thirtyish years later, DNA-sequencing machines are at the point where students are doing 100 of my Ph.D.s per second,” he says, laughing. ”We're at millisecond data acquisition. These are the kinds of advances we'll need to make a map of the human mind.”
In Rubin's mind, solving the fruit-fly brain is a 20-year problem. ”After we solve this, I'd say we're one-fifth of the way to understanding the human mind.”
For more articles, videos, and special features, go to The Singularity Special Report.
