Markram has the tall build and tousled hair of a fashion model. Seated behind a clean desk in an office devoid of anything more personal than his white MacBook, he spends most of his days meeting with administrators, technicians, and collaborators. The office is down the street from his wet lab and halfway across campus from the Blue Gene computer facility. Markram speaks of brain slices and microchips in detail, but he is not just a scientist in the conventional sense, stooped over a lab bench like Jonas Salk. He belongs to a new breed of telegenic research executives, a sort of J. Craig Venter of the head. “I love experiments,” he says in a South African accent tweaked by more than a decade living and researching in Israel. “But I very quickly see that what I’m doing can be done far more efficiently.” Once the procedures for data collection are set, he believes, experiments can be outsourced or automated.
Understanding the brain writ large is what drives Markram. It has been his only serious interest since the age of 13, when his mother sent him from the Kalahari game farm where he’d spent his childhood to a boarding school outside Durban. His first year there, he stumbled across some research on schizophrenia and other mental disorders and directed his youthful energy into studying the mind. “It was just amazing to me that you could have a little more or less of some chemical and your whole worldview would be different,” he recalls, smiling with boyish wonder. “If you can switch a chemical and your personality changes, who are you?”
To find out, he took up psychiatry at the University of Cape Town but swiftly grew impatient with the field. “I could see that this was not a science,” he says with a wave of his hand. “I didn’t see any future in it, grouping people by symptoms and prescribing whatever drug the pharmaceutical companies said.”
So he quit medicine and joined the only Cape Town lab doing experimental neuroscience, directed by a young researcher named Rodney Douglas. Even then—1985—Markram had formed his ambition to understand the whole brain. But he had to start at a much more granular level. Over a one-year period Markram performed nearly a thousand experiments recording the effect of a neurotransmitter on neurons in the brain stem.
It was the beginning of his meteoric rise as an experimental neuroscientist. He got his PhD at the Weizmann Institute of Science, one of the leading research universities in Israel—”it was like landing in toyland,” he remarks with a broad smile—and went on to consecutive postdocs at the National Institutes of Health in Bethesda, Maryland, and the Max Planck Institute for Medical Research in Heidelberg, Germany. “My mantra is diversity,” he says, explaining his peripatetic years. “I clone my mentors. I copy everything they do, and then I innovate on top of it.”
In 1995 he was recruited back to Weizmann as a senior scientist. In his new lab, Markram took up a technique that he’d learned from electrophysiologist Bert Sakmann at Max Planck, for which Sakmann and physicist Erwin Neher won the 1991 Nobel Prize in Medicine. The procedure called for a researcher to access a living neuron with a “patch clamp,” really just a micron-wide pipette, to directly monitor the neuron’s electrical activity. With his exceptionally steady hands, Markram was the first researcher to patch two connected neurons simultaneously, a feat that put him in a position to see how they interacted.
By sending electrical signals between neurons and measuring their electrical responses, he could test Hebb’s rule—neurons that fire together wire together—a fundamental neuroscience postulate. What Markram discovered was that the pattern of synaptic connections in a neural network is determined not only by whether neurons fire together but also by when they fire relative to one another. If an input spike of electrical current occurs before an output spike, the input connection is strengthened. If the input spike comes after the output spike, the connection weakens. In other words, Markram proved that the brain is attentive to cause and effect.
Markram published his groundbreaking results in more than a half-dozen scientific papers, enough to earn him a full professorship by the age of 40. The lesson he drew from that success: He needed to set his sights much higher. “I realized that I could keep doing this for the rest of my career and I still wouldn’t really understand how the brain works,” Markram says. There were approximately 60,000 neuroscience papers published every year, only increasing the field’s fragmentation. What neuroscience needed, he decided, was an enormous collaboration, with research protocols coordinated so that all the data would fire together—and naturally he thought he was the one to make it happen.
His vision matched the ambition of one man who could fund it: neuroscientist Patrick Aebischer, the newly appointed president of the Swiss Federal Institute of Technology, tasked with making the campus a leader in computer science and biomedicine. In 2002 he recruited Markram, and in 2005 he bought him an IBM Blue Gene—one of the world’s fastest supercomputers.
From his position in Lausanne, Markram is simultaneously doing four things. He is running a wet lab that amasses data through experiments on brain tissue. Since 2005, he has been building a small-scale model and simulation of the rat neocortex (his initial Blue Brain project). He is now the coordinator of the lavishly funded Human Brain Project, spearheading a global initiative to coordinate data-gathering across labs worldwide. On top of all that, Markram is responsible for the simulation aspects of the HBP, building a virtual human brain from all the incoming data.
Markram’s Blue Gene supercomputer is a 10-minute walk from the Blue Brain wet lab, in a whitewashed room behind a sliding glass door. This is the second multimillion-dollar supercomputer Switzerland has given him in 10 years, with eight times more memory than his first. There are four racks of processors, each enclosed in a metal locker about the size of a washer/dryer. The loud drone of air-conditioning serves as a constant reminder that computing has a lot to learn about efficiency from the 20-watt human brain.
The Blue Gene will simulate Markram’s brain model—the model that uses all the experimental results Markram has collected over 10 years of industrial-strength science at Lausanne, as well as all of the studies he did at Weizmann. But the model isn’t just a massive database. Markram understood that it would take trillions of dollars, not billions, to experimentally model every part of the human brain. “Other people in the field were saying that we didn’t know enough to start,” he says. (The Allen Brain Atlas’ Christof Koch, for one. Markram’s first mentor, Rodney Douglas, for another.) “What I realized was that you can get to the unknowns indirectly. It’s like putting together a puzzle with lots of missing pieces. If you can see the pattern, you can fill in the gaps.” Markram calls the process predictive reverse-engineering, and he claims that it has already allowed him to anticipate crucial data that would have taken years to generate in a wet lab. For example, only about 20 of the 2,970 synaptic pathways in one small part of the rat neocortex have been experimentally measured. Detecting a pattern, he was able to fill in parameters for the remaining 2,950 pathways and to observe them working together in a simulation. Then he measured several in the wet lab to validate his reverse-engineered data. The simulation proved correct.
Markram is a man seemingly mired in contradiction. He wants to know mankind by studying the rat. He wants to industrialize experimentation and one day make lab work obsolete. He insists on exhaustive biological detail yet strives to make the most general models possible. But if you listen carefully—filtering out his relentless boasting—the apparent contradictions resolve into complementary strategies: Without a dependable experimental base—focused on one species to which researchers have unlimited laboratory access—detailed modeling wouldn’t be possible. And without modeling and simulation, all that knowledge about the brain would amount to an incoherent storehouse of trivia. But with a multilevel model of the rat brain as a template, scientists might find a rule governing how neurons connect and chart only a few, on the basis of which they could fill in the remainder. “A unifying model is a powerful accelerator, since it helps you prioritize experiments,” he says. “I’m very pragmatic. The question is, what’s the minimum I need to know about the brain to reconstruct all of it?”