[tt] reverse engineering the brain

[Eugen Leitl]

http://www.spectrum.ieee.org/print/6268

Reverse Engineering The Brain

By Sally Adee

This is part of IEEE Spectrum's SPECIAL REPORT: THE SINGULARITY

PHOTO: Timothy Archibald

What do fruit-fly brains have in common with microchips? That's not the setup
for a bad joke; it's David Adler's life. Under Adler's ultra sophisticated
electron beam microscopes, advanced microprocessors with transistors far
smaller than red blood cells have been reduced to their wiring diagrams. Now
the noggin of the humble Drosophila melanogaster is next, as Adler is being
courted by researchers at a neurobiology wing of the Howard Hughes Medical
Institute to help them reverse engineer the human brain. They're starting
small, with the fruit fly.

Located in the green, rolling hills of Ashburn, in northern Virginia, the
campus, known as Janelia Farm, has been described as a kind of Bell Labs for
neuro-biology. Its task is solving what Adler calls the most important
question in science: How exactly does the human brain do what it does? Lots
of people are trying to answer this question, and there's a growing impetus
toward using high- definition brain scans to find out how the brain works.

“In a hundred years I'd like to know how human consciousness works,” says
Janelia director Gerry Rubin. “The 10‑ or 20-year goal is to understand the
fruit- fly brain.” It's this difference between consciousness and brain that
has neuro-science researchers stymied. The simplest system stores and
processes information the same way the most complex system does; a primitive
computer from 1986 works a lot like a supercomputer. Similarly, Rubin
suspects that the human brain and the fruit-fly brain are separated only by
degrees of complexity: “Just because it's much more advanced doesn't mean the
basic wiring rules are different.” Right now, Janelia is working on a circuit
diagram of the fruit-fly brain.

To that end, Rubin has stocked the Janelia campus with a collection of
neuro-scientists, biologists, physicists, engineers, and computer scientists.
The process resembles that of reverse engineering a microprocessor. It starts
with a full-scale, three-dimensional wiring diagram of the fly's brain, in
which the density of neurons is substantially higher—“but not infinitely
higher,” insists Adler—than the wiring in a high-end IC. “If we can get a
circuit diagram of the human brain,” says Adler, “then we can understand what
causes a lot of neurological disorders—depression, epilepsy, maybe even
Alzheimer's.”

Like an IC, the fruit-fly brain is subjected to logic and optical testing to
derive its circuit diagram. With one approach, called neuronal electro
physiology, researchers can record the electrical activity of neurons. “But
the fly brain is even more complicated than an integrated circuit,” says
engineer and group leader Eric Betzig. “With an IC, you know that every
transistor fires the same—it's either on or off. But the neurons in the brain
don't necessarily do that—they fire sometimes 20, sometimes 80, sometimes 100
percent.” So in addition to logic testing, the researchers also need to do
imaging, and that's where Adler and his amazing microscope come in.

A standard scanning electron microscope (SEM) images at about 10 million
pixels per second. For comparison, a high- definition TV screen runs at 30
million pixels per second. In 2005, the Pentagon gave funding to
California-based KLA‑Tencor Corp., where Adler was then working, to invent a
microscope that could operate at 1 billion pixels per second to verify
circuit patterns on defense chips. Shortly after that, Janelia lured Harald
Hess, a former colleague of Adler's, to the campus to direct its applied
physics and instrumentation group. When the Janelia team started looking into
imaging, Hess called Adler. When he found out what Janelia was working on,
Adler says, “it blew my mind.” Hess wasn't interested in a microscope that
could image at a paltry billion pixels per second. He wanted one that could
process 10 billion per second.

To image the fruit-fly brain, the researchers use what they all refer to,
gruesomely, as a “deli slicer”—the machine shaves 50-nanometer slices off the
top of the infinitesimal fruit-fly brain “like slices of prosciutto,” says
Betzig. (The same technique is used to reverse engineer microchips.) Then an
electron microscope takes images of the brain slices, and these images are
stacked carefully to form a 3‑D virtual wiring diagram.

Slice, image, slice, image. Easy, right? Wrong.

Compared with an IC, even a tiny fruit-fly brain is a mess. One major
bottleneck is the sample preparation: the brains must be sliced into
perfectly even slivers before they're imaged. Right now, that slice-and-image
routine takes a whopping 10 months. The real time-waster isn't the actual
imaging—it's the time it takes for each slice of brain to settle into place.
Any movement, however slight, will make that hard-won image blurry. The fly
brain is only about 300 micrometers on a side, but imaging one, even at 10
billion pixels per second, would take a whole day. You're trying to image
everything down to about 5 nm—about one-hundredth the size of what a regular
lab microscope can resolve.

The storage requirements for the raw data alone are staggering: Adler
estimates that scientists could rack up about a petabyte—that's 1000
terabytes—of data for every day of imaging. Bear in mind that 1000 terabytes
is for one fruit fly, with its sorry speck of a brain, and the biggest hard
drive you can buy from a commercial vendor today holds only one terabyte of
data. To get any good data, you'd have to compare hundreds of fruit-fly
brains. Imaging hundreds of them at the speed and resolution of Adler's
technology would require a warehouse. “If nothing else,” he says, “you're
going to run out of space.”

Anyone over 30 remembers when a gigabyte of storage in one place was
laughably sci-fi. It won't be long before a 10-PB hard drive is as boring as
today's 100-GB hard drives. But this project doesn't have as its goal merely
collecting data; it is trying to establish the exact connections among the
neurons and synapses of the tiny creature's brain. And therein lies the big
challenge. Each slice holds billions of pixels, and once every slice has been
imaged, scientists have to piece them all back together to generate a 3-D
wiring diagram. Adler compares the scale of the undertaking to trying to put
together a real-time traffic map of North America from high-resolution
satellite photos. “Now imagine that the United States is paved coast-to coast
as densely as New York City,” he says. At the resolution necessary to see
individual synapses, the data glut is crippling. “You have to turn that data
glut into a wiring diagram that doesn't take up 1000 hard drives,” says
Adler.  Photo: JULIE SIMPSON & PHUONG CHUNG/janelia farm

Sliced Brains: Confocal microscopy imagery of fluorescently labeled neurons
is one of the tools used to develop circuit diagrams of the fruit-fly brain.

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.  The brain is the ultimate
micro-machine. The fact that it’s made out of meat is a red herring”

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.”

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