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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 BBP/EPFL; rendering by Visualbiotech
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.
Originally published March 3, 2008
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