[Hplusroadmap] Fwd: [neuro] out of the blue

[Bryan Bishop]

On Monday 03 March 2008, Eugen Leitl wrote:
> http://www.seedmagazine.com/news/2008/03/out_of_the_blue.php?page=all
>&p=y
>
> Out of the Blue
>
> Can a thinking, remembering, decision-making, biologically accurate
> brain be built from a supercomputer?
>
> by Jonah Lehrer • Posted March 3, 2008 05:50 AM
>
> A computer simulation of the upper layer of a rat brain neocortical
> column. Here neurons light up in a "global excitatory state" of blues
> and yellows. Courtesy of Alain Herzog/EPFL
>
> In the basement of a university in Lausanne, Switzerland sit four
> black boxes, each about the size of a refrigerator, and filled with
> 2,000 IBM microchips stacked in repeating rows. Together they form
> the processing core of a machine that can handle 22.8 trillion
> operations per second. It contains no moving parts and is eerily
> silent. When the computer is turned on, the only thing you can hear
> is the continuous sigh of the massive air conditioner. This is Blue
> Brain.
>
> The name of the supercomputer is literal: Each of its microchips has
> been programmed to act just like a real neuron in a real brain. The
> behavior of the computer replicates, with shocking precision, the
> cellular events unfolding inside a mind. "This is the first model of
> the brain that has been built from the bottom-up," says Henry
> Markram, a neuroscientist at Ecole Polytechnique Fédérale de Lausanne
> (EPFL) and the director of the Blue Brain project. "There are lots of
> models out there, but this is the only one that is totally
> biologically accurate. We began with the most basic facts about the
> brain and just worked from there."
>
> Before the Blue Brain project launched, Markram had likened it to the
> Human Genome Project, a comparison that some found ridiculous and
> others dismissed as mere self-promotion. When he launched the project
> in the summer of 2005, as a joint venture with IBM, there was still
> no shortage of skepticism. Scientists criticized the project as an
> expensive pipedream, a blatant waste of money and talent.
> Neuroscience didn't need a supercomputer, they argued; it needed more
> molecular biologists. Terry Sejnowski, an eminent computational
> neuroscientist at the Salk Institute, declared that Blue Brain was
> "bound to fail," for the mind remained too mysterious to model. But
> Markram's attitude was very different. "I wanted to model the brain
> because we didn't understand it," he says. "The best way to figure
> out how something works is to try to build it from scratch."
>
> The Blue Brain project is now at a crucial juncture. The first phase
> of the project—"the feasibility phase"—is coming to a close. The
> skeptics, for the most part, have been proven wrong. It took less
> than two years for the Blue Brain supercomputer to accurately
> simulate a neocortical column, which is a tiny slice of brain
> containing approximately 10,000 neurons, with about 30 million
> synaptic connections between them. "The column has been built and it
> runs," Markram says. "Now we just have to scale it up." Blue Brain
> scientists are confident that, at some point in the next few years,
> they will be able to start simulating an entire brain. "If we build
> this brain right, it will do everything," Markram says. I ask him if
> that includes selfconsciousness: Is it really possible to put a ghost
> into a machine? "When I say everything, I mean everything," he says,
> and a mischievous smile spreads across his face.
>
> Henry Markram is tall and slim. He wears jeans and tailored shirts.
> He has an aquiline nose and a lustrous mop of dirty blond hair that
> he likes to run his hands through when contemplating a difficult
> problem. He has a talent for speaking in eloquent soundbites, so that
> the most grandiose conjectures ("In ten years, this computer will be
> talking to us.") are tossed off with a casual air. If it weren't for
> his bloodshot, blue eyes—"I don't sleep much," he admits—Markram
> could pass for a European playboy.
>
> But the playboy is actually a lab rat. Markram starts working around
> nine in the morning, and usually doesn't leave his office until the
> campus is deserted and the lab doors are locked. Before he began
> developing Blue Brain, Markram was best known for his painstaking
> studies of cellular connectivity, which one scientist described to me
> as "beautiful stuff...and yet it must have been experimental hell."
> He trained under Dr. Bert Sakmann, who won a Nobel Prize for
> pioneering the patch clamp technique, allowing scientists to monitor
> the flux of voltage within an individual brain cell, or neuron, for
> the first time. (This involves piercing the membrane of a neuron with
> an invisibly sharp glass pipette.) Markram's technical innovation was
> "patching" multiple neurons at the same time, so that he could
> eavesdrop on their interactions. This experimental breakthrough
> promised to shed light on one of the enduring mysteries of the brain,
> which is how billions of discrete cells weave themselves into
> functional networks. In a series of elegant papers published in the
> late 1990s, Markram was able to show that these electrical
> conversations were incredibly precise. If, for example, he delayed a
> neuron's natural firing time by just a few milliseconds, the entire
> sequence of events was disrupted. The connected cells became
> strangers to one another.
>
> When Markram looked closer at the electrical language of neurons, he
> realized that he was staring at a code he couldn't break. "I would
> observe the cells and I would think, 'We are never going to
> understand the brain.' Here is the simplest possible circuit—just two
> neurons connected to each other—and I still couldn't make sense of
> it. It was still too complicated."
>
> Cables running from the Blue Gene/L supercomputer to the storage
> unit. The 2,000-microchip Blue Gene machine is capable of processing
> 22.8 trillion operations per second—just enough to model a 1-cubic-mm
> column of rat brain. Courtesy of Alain Herzog/EPFL
>
> Neuroscience is a reductionist science. It describes the brain in
> terms of its physical details, dissecting the mind into the smallest
> possible parts. This process has been phenomenally successful. Over
> the last 50 years, scientists have managed to uncover a seemingly
> endless list of molecules, enzymes, pathways, and genes. The mind has
> been revealed as a Byzantine machine. According to Markram, however,
> this scientific approach has exhausted itself. "I think that
> reductionism peaked five years ago," he says. "This doesn't mean
> we've completed the reductionist project, far from it. There is still
> so much that we don't know about the brain. But now we have a
> different, and perhaps even harder, problem. We're literally drowning
> in data. We have lots of scientists who spend their life working out
> important details, but we have virtually no idea how all these
> details connect together. Blue Brain is about showing people the
> whole."
>
> In other words, the Blue Brain project isn't just a model of a neural
> circuit. Markram hopes that it represents a whole new kind of
> neuroscience. "You need to look at the history of physics," he says.
> "From Copernicus to Einstein, the big breakthroughs always came from
> conceptual models. They are what integrated all the facts so that
> they made sense. You can have all the data in the world, but without
> a model the data will never be enough."
>
> Markram has good reason to cite physics—neuroscience has almost no
> history of modeling. It's a thoroughly empirical discipline, rooted
> in the manual labor of molecular biology. If a discovery can't be
> parsed into something observable—like a line on a gel or a recording
> from a neuron—then, generally, it's dismissed. The sole exception is
> computational neuroscience, a relatively new field that also uses
> computers to model aspects of the mind. But Markram is dismissive of
> most computational neuroscience. "It's not interested enough in the
> biology," he says. "What they typically do is begin with a brain
> function they want to model"—like object detection or sentence
> recognition—"and then try to see if they can get a computer to
> replicate that function. The problem is that if you ask a hundred
> computational neuroscientists to build a functional model, you'll get
> a hundred different answers. These models might help us think about
> the brain, but they don't really help us understand it. If you want
> your model to represent reality, then you've got to model it on
> reality."
>
> Of course, the hard part is deciphering that reality in the first
> place. You can't simulate a neuron until you know how a neuron is
> supposed to behave. Before the Blue Brain team could start
> constructing their model, they needed to aggregate a dizzying amount
> of data. The collected works of modern neuroscience had to be
> painstakingly programmed into the supercomputer, so that the software
> could simulate our hardware. The problem is that neuroscience is
> still woefully incomplete. Even the simple neuron, just a sheath of
> porous membrane, remains a mostly mysterious entity. How do you
> simulate what you can't understand?
>
> Markram tried to get around "the mystery problem" by focusing on a
> specific section of a brain: a neocortical column in a two-week-old
> rat. A neocortical column is the basic computational unit of the
> cortex, a discrete circuit of flesh that's 2 mm long and 0.5 mm in
> diameter. The gelatinous cortex consists of thousands of these
> columns—each with a very precise purpose, like processing the color
> red or detecting pressure on a patch of skin, and a basic structure
> that remains the same, from mice to men. The virtue of simulating a
> circuit in a rodent brain is that the output of the model can be
> continually tested against the neural reality of the rat, a gruesome
> process that involves opening up the skull and plunging a needle into
> the brain. The point is to electronically replicate the performance
> of the circuit, to build a digital doppelganger of a biological
> machine.
>
> Felix Schürmann, the project manager of Blue Brain, oversees this
> daunting process. He's 30 years old but looks even younger, with a
> chiseled chin, lean frame, and close-cropped hair. His patient manner
> is that of someone used to explaining complex ideas in simple
> sentences. Before the Blue Brain project, Schürmann worked at the
> experimental fringes of computer science, developing simulations of
> quantum computing. Although he's since mastered the vocabulary of
> neuroscience, referencing obscure acronyms with ease, Schürmann
> remains most comfortable with programming. He shares a workspace with
> an impressively diverse group—the 20 or so scientists working
> full-time on Blue Brain's software originate from 14 different
> countries. When we enter the hushed room, the programmers are all
> glued to their monitors, fully absorbed in the hieroglyphs on the
> screen. Nobody even looks up. We sit down at an empty desk and
> Schürmann opens his laptop.
>
> In Markram's laboratory, state-of-the-art equipment allows for
> computer-controlled, simultaneous recordings of the tiny electrical
> currents that form the basis of nerve impulses. Here, a technique
> known as "patch clamp" provides direct access to seven individual
> neurons and their chemical synaptic interactions. The patch clamp
> robot—at work 24 hours a day, seven days a week—helped the Blue Brain
> team speed through 30 years of research in six months. Inset, a
> system integrates a bright-field microscope with computer-assisted
> reconstruction of neuron structure. The entire setup is enclosed
> inside a "Faraday cage" to reduce electromagnetic interference and
> mounted on a floating table to minimize vibrations. Courtesy of Alain
> Herzog/EPFL
>
> The computer screen is filled with what look like digitally rendered
> tree branches. Schürmann zooms out so that the branches morph into a
> vast arbor, a canopy so dense it's practically opaque. "This," he
> proudly announces, "is a virtual neuron. What you're looking at are
> the thousands of synaptic connections it has made with other
> [virtual] neurons." When I look closely, I can see the faint lines
> where the virtual dendrites are subdivided into compartments. At any
> given moment, the supercomputer is modeling the chemical activity
> inside each of these sections so that a single simulated neuron is
> really the sum of 400 independent simulations. This is the level of
> precision required to accurately imitate just one of the 100 billion
> cells—each of them unique—inside the brain. When Markram talks about
> building a mind from the "bottom-up," these intracellular
> compartments are the bottom. They are the fundamental unit of the
> model.
>
> But how do you get these simulated compartments to act in a realistic
> manner? The good news is that neurons are electrical processors: They
> represent information as ecstatic bursts of voltage, just like a
> silicon microchip. Neurons control the flow of electricity by opening
> and closing different ion channels, specialized proteins embedded in
> the cellular membrane. When the team began constructing their model,
> the first thing they did was program the existing ion channel data
> into the supercomputer. They wanted their virtual channels to act
> just like the real thing. However, they soon ran into serious
> problems. Many of the experiments used inconsistent methodologies and
> generated contradictory results, which were too irregular to model.
> After several frustrating failures—"The computer was just churning
> out crap," Markram says—the team realized that if they wanted to
> simulate ion channels, they needed to generate the data themselves.
>
> That's when Schürmann leads me down the hall to Blue Brain's "wet
> lab." At first glance, the room looks like a generic neuroscience
> lab. The benches are cluttered with the usual salt solutions and
> biotech catalogs. There's the familiar odor of agar plates and
> astringent chemicals. But then I notice, tucked in the corner of the
> room, is a small robot. The machine is about the size of a microwave,
> and consists of a beige plastic tray filled with a variety of test
> tubes and a delicate metal claw holding a pipette. The claw is
> constantly moving back and forth across the tray, taking tiny sips
> from its buffet of different liquids. I ask Schürmann what the robot
> is doing. "Right now," he says, "it's recording from a cell. It does
> this 24 hours a day, seven days a week. It doesn't sleep and it never
> gets frustrated. It's the perfect postdoc."
>
> The science behind the robotic experiments is straightforward. The
> Blue Brain team genetically engineers Chinese hamster ovary cells to
> express a single type of ion channel—the brain contains more than 30
> different types of channels—then they subject the cells to a variety
> of physiological conditions. That's when the robot goes to work. It
> manages to "patch" a neuron about 50 percent of the time, which means
> that it can generate hundreds of data points a day, or about 10 times
> more than an efficient lab technician. Markram refers to the robot as
> "science on an industrial scale," and is convinced that it's the
> future of lab work. "So much of what we do in science isn't actually
> science," he says, "I say let robots do the mindless work so that we
> can spend more time thinking about our questions."
>
> According to Markram, the patch clamp robot helped the Blue Brain
> team redo 30 years of research in six months. By analyzing the
> genetic expression of real rat neurons, the scientists could then
> start to integrate these details into the model. They were able to
> construct a precise map of ion channels, figuring out which cell
> types had which kind of ion channel and in what density. This new
> knowledge was then plugged into Blue Brain, allowing the
> supercomputer to accurately simulate any neuron anywhere in the
> neocortical column. "The simulation is getting to the point,"
> Schürmann says, "where it gives us better results than an actual
> experiment. We get the same data, but with less noise and human
> error." The model, in other words, has exceeded its own inputs. The
> virtual neurons are more real than reality.
>
> A simulated neuron from a rat brain showing "spines"—tiny knobs
> protruding from the dendrites that will eventually form synapses with
> other neurons. Pyramidal cells such as these (so-called because of
> their triangular shape) comprise about 80 percent of cerebral cortex
> mass. Courtesy of BBP/EPFL
>
> Every brain is made of the same basic parts. A sensory cell in a sea
> slug works just like a cortical neuron in a human brain. It relies on
> the same neurotransmitters and ion channels and enzymes. Evolution
> only innovates when it needs to, and the neuron is a perfect piece of
> design.
>
> In theory, this meant that once the Blue Brain team created an
> accurate model of a single neuron, they could multiply it to get a
> three-dimensional slice of brain. But that was just theory. Nobody
> knew what would happen when the supercomputer began simulating
> thousands of brain cells at the same time. "We were all emotionally
> prepared for failure," Markram says. "But I wasn't so prepared for
> what actually happened."
>
> After assembling a three-dimensional model of 10,000 virtual neurons,
> the scientists began feeding the simulation electrical impulses,
> which were designed to replicate the currents constantly rippling
> through a real rat brain. Because the model focused on one particular
> kind of neural circuit—a neocortical column in the somatosensory
> cortex of a two-week-old rat—the scientists could feed the
> supercomputer the same sort of electrical stimulation that a newborn
> rat would actually experience.
>
> It didn't take long before the model reacted. After only a few
> electrical jolts, the artificial neural circuit began to act just
> like a real neural circuit. Clusters of connected neurons began to
> fire in close synchrony: the cells were wiring themselves together.
> Different cell types obeyed their genetic instructions. The
> scientists could see the cellular looms flash and then fade as the
> cells wove themselves into meaningful patterns. Dendrites reached out
> to each other, like branches looking for light. "This all happened on
> its own," Markram says. "It was entirely spontaneous." For the Blue
> Brain team, it was a thrilling breakthrough. After years of hard
> work, they were finally able to watch their make-believe brain
> develop, synapse by synapse. The microchips were turning themselves
> into a mind.
>
> But then came the hard work. The model was just a first draft. And so
> the team began a painstaking editing process. By comparing the
> behavior of the virtual circuit with experimental studies of the rat
> brain, the scientists could test out the verisimilitude of their
> simulation. They constantly fact-checked the supercomputer, tweaking
> the software to make it more realistic. "People complain that Blue
> Brain must have so many free parameters," Schürmann says. "They
> assume that we can just input whatever we want until the output looks
> good. But what they don't understand is that we are very constrained
> by these experiments." This is what makes the model so impressive: It
> manages to simulate a real neocortical column—a functional slice of
> mind—by simulating the particular details of our ion channels. Like a
> real brain, the behavior of Blue Brain naturally emerges from its
> molecular parts.
>
> In fact, the model is so successful that its biggest restrictions are
> now technological. "We have already shown that the model can scale
> up," Markram says. "What is holding us back now are the computers."
> The numbers speak for themselves. Markram estimates that in order to
> accurately simulate the trillion synapses in the human brain, you'd
> need to be able to process about 500 petabytes of data (peta being a
> million billion, or 10 to the fifteenth power). That's about 200
> times more information than is stored on all of Google's servers.
> (Given current technology, a machine capable of such power would be
> the size of several football fields.) Energy consumption is another
> huge problem. The human brain requires about 25 watts of electricity
> to operate. Markram estimates that simulating the brain on a
> supercomputer with existing microchips would generate an annual
> electrical bill of about $3 billion . But if computing speeds
> continue to develop at their current exponential pace, and energy
> efficiency improves, Markram believes that he'll be able to model a
> complete human brain on a single machine in ten years or less.
>
> For now, however, the mind is still the ideal machine. Those
> intimidating black boxes from IBM in the basement are barely
> sufficient to model a thin slice of rat brain. The nervous system of
> an invertebrate exceeds the capabilities of the fastest supercomputer
> in the world. "If you're interested in computing," Schürmann says,
> "then I don't see how you can't be interested in the brain. We have
> so much to learn from natural selection. It's really the ultimate
> engineer."
>
> An entire neocortical column lights up with electrical activity.
> Modeled on a two-week-old rodent brain, this 0.5 mm by 2 mm slice is
> the basic computational unit of the brain and contains about 10,000
> neurons. This microcircuit is repeated millions of times across the
> rat cortex—and many times more in the brain of a human. Courtesy of
> Alain Herzog/EPFL
>
> Neuroscience describes the brain from the outside. It sees us through
> the prism of the third person, so that we are nothing but three
> pounds of electrical flesh. The paradox, of course, is that we don't
> experience our matter. Self-consciousness, at least when felt from
> the inside, feels like more than the sum of its cells. "We've got all
> these tools for studying the cortex," Markram says. "But none of
> these methods allows us to see what makes the cortex so interesting,
> which is that it generates worlds. No matter how much I know about
> your brain, I still won't be able to see what you see."
>
> Some philosophers, like Thomas Nagel, have argued that this divide
> between the physical facts of neuroscience and the reality of
> subjective experience represents an epistemological dead end. No
> matter how much we know about our neurons, we still won't be able to
> explain how a twitch of ions in the frontal cortex becomes the
> Technicolor cinema of consciousness.
>
> Markram takes these criticisms seriously. Nevertheless, he believes
> that Blue Brain is uniquely capable of transcending the limits of
> "conventional neuroscience," breaking through the mind-body problem.
> According to Markram, the power of Blue Brain is that it can
> transform a metaphysical paradox into a technological problem.
> "There's no reason why you can't get inside Blue Brain," Markram
> says. "Once we can model a brain, we should be able to model what
> every brain makes. We should be able to experience the experiences of
> another mind."
>
> When listening to Markram speculate, it's easy to forget that the
> Blue Brain simulation is still just a single circuit, confined within
> a silent supercomputer. The machine is not yet alive. And yet Markram
> can be persuasive when he talks about his future plans. His ambitions
> are grounded in concrete steps. Once the team is able to model a
> complete rat brain—that should happen in the next two years—Markram
> will download the simulation into a robotic rat, so that the brain
> has a body. He's already talking to a Japanese company about
> constructing the mechanical animal. "The only way to really know what
> the model is capable of is to give it legs," he says. "If the robotic
> rat just bumps into walls, then we've got a problem."
>
> Installing Blue Brain in a robot will also allow it to develop like a
> real rat. The simulated cells will be shaped by their own sensations,
> constantly revising their connections based upon the rat's
> experiences. "What you ultimately want," Markram says, "is a robot
> that's a little bit unpredictable, that doesn't just do what we tell
> it to do." His goal is to build a virtual animal—a rodent robot—with
> a mind of its own.
>
> But the question remains: How do you know what the rat knows? How do
> you get inside its simulated cortex? This is where visualization
> becomes key. Markram wants to simulate what that brain experiences.
> It's a typically audacious goal, a grand attempt to get around an
> ancient paradox. But if he can really find a way to see the brain
> from the inside, to traverse our inner space, then he will have given
> neuroscience an unprecedented window into the invisible. He will have
> taken the self and turned it into something we can see.
>
> A close-up view of the rat neocortical column, rendered in three
> dimensions by a computer simulation. The large cell bodies (somas)
> can be seen branching into thick axons and forests of thinner
> dendrites. Courtesy of Dr. Pablo de Heras Ciechomski/Visualbiotech
>
> Schürmann leads me across the campus to a large room tucked away in
> the engineering school. The windows are hermetically sealed; the air
> is warm and heavy with dust. A lone Silicon Graphics supercomputer,
> about the size of a large armoire, hums loudly in the center of the
> room. Schürmann opens the back of the computer to reveal a tangle of
> wires and cables, the knotted guts of the machine. This computer
> doesn't simulate the brain, rather it translates the simulation into
> visual form. The vast data sets generated by the IBM supercomputer
> are rendered as short films, hallucinatory voyages into the deep
> spaces of the mind. Schürmann hands me a pair of 3-D glasses, dims
> the lights, and starts the digital projector. The music starts first,
> "The Blue Danube" by Strauss. The classical waltz is soon accompanied
> by the vivid image of an interneuron, its spindly limbs reaching
> through the air. The imaginary camera pans around the brain cell,
> revealing the subtle complexities of its form. "This is a random
> neuron plucked from the model," Schürmann says. He then hits a few
> keys and the screen begins to fill with thousands of colorful cells.
> After a few seconds, the colors start to pulse across the network, as
> the virtual ions pass from neuron to neuron. I'm watching the
> supercomputer think.
>
> Rendering cells is easy, at least for the supercomputer. It's the
> transformation of those cells into experience that's so hard. Still,
> Markram insists that it's not impossible. The first step, he says,
> will be to decipher the connection between the sensations entering
> the robotic rat and the flickering voltages of its brain cells. Once
> that problem is solved—and that's just a matter of massive
> correlation—the supercomputer should be able to reverse the process.
> It should be able to take its map of the cortex and generate a movie
> of experience, a first person view of reality rooted in the details
> of the brain. As the philosopher David Chalmers likes to say,
> "Experience is information from the inside; physics is information
> from the outside." By shuttling between these poles of being, the
> Blue Brain scientists hope to show that these different perspectives
> aren't so different at all. With the right supercomputer, our lucid
> reality can be faked.
>
> "There is nothing inherently mysterious about the mind or anything it
> makes," Markram says. "Consciousness is just a massive amount of
> information being exchanged by trillions of brain cells. If you can
> precisely model that information, then I don't know why you wouldn't
> be able to generate a conscious mind." At moments like this, Markram
> takes on the deflating air of a magician exposing his own magic
> tricks. He seems to relish the idea of "debunking consciousness,"
> showing that it's no more metaphysical than any other property of the
> mind. Consciousness is a binary code; the self is a loop of
> electricity. A ghost will emerge from the machine once the machine is
> built right.
>
> And yet, Markram is candid about the possibility of failure. He knows
> that he has no idea what will happen once the Blue Brain is scaled
> up. "I think it will be just as interesting, perhaps even more
> interesting, if we can't create a conscious computer," Markram says.
> "Then the question will be: 'What are we missing? Why is this not
> enough?'"
>
> Niels Bohr once declared that the opposite of a profound truth is
> also a profound truth. This is the charmed predicament of the Blue
> Brain project. If the simulation is successful, if it can turn a
> stack of silicon microchips into a sentient being, then the epic
> problem of consciousness will have been solved. The soul will be
> stripped of its secrets; the mind will lose its mystery. However, if
> the project fails—if the software never generates a sense of self, or
> manages to solve the paradox of experience—then neuroscience may be
> forced to confront its stark limitations. Knowing everything about
> the brain will not be enough. The supercomputer will still be a mere
> machine. Nothing will have emerged from all of the information. We
> will remain what can't be known.
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________________________________________
Bryan Bishop
http://heybryan.org/