A representation of a mammalian neocortical column, the basic building block of the cortex. The representation shows the complexity of this part of the brain, which has now been modeled using a supercomputer.

The Blue Brain Project

By Duncan Graham-Rowe
MIT Technology Review, November 28, 2007

Edited by Andy Ross

An ambitious project to create an accurate computer model of the brain has reached an impressive milestone. Scientists in Switzerland working with IBM researchers have shown that their computer simulation of the neocortical column, arguably the most complex part of a mammal's brain, appears to behave like its biological counterpart.

The project began with the initial goal of modeling the 10,000 neurons and 30 million synaptic connections that make up a rat's neocortical column, the main building block of a mammal's cortex. The neocortical column was chosen as a starting point because it is widely recognized as being particularly complex.

The model itself is based on 15 years' worth of experimental data on neuronal morphology, gene expression, ion channels, synaptic connectivity, and electrophysiological recordings of the neocortical columns of rats. Software tools were developed to reconstruct accurate 3D models of neurons and their interconnections.

The neuronal circuits were tested by simulating specific input stimuli and seeing how the circuits behaved, compared with those in biological experiments. Where gaps in knowledge appeared about how parts of the model were supposed to behave, the scientists went back to the lab and performed experiments.

The level of detail of the model can be taken further. It is still at a cellular level, but the scientists want to look at the molecular level. Doing so would enable simulation-based drug testing to be carried out by showing how specific molecules affect proteins, receptors, and enzymes.
"What we're doing is reverse-engineering the brain"
Henry Markram, Codirector, Brain Mind Institute
Ecole Polytechnique Fédérale de Lausanne, Switzerland

"This is an evolutionary process rather than a revolutionary one"
Christof Koch, Professor of Biology and Engineering
California Institute of Technology, Pasadena, CA

Blue Brain

By Jonah Lehrer
Seed, March 3, 2008

Edited by Andy Ross

Four black boxes, each about the size of a refrigerator, form the processing core of a machine that can handle 22.8 trillion operations per second. When the computer is turned on, all you can hear is the air conditioner. This is Blue Brain.

The behavior of the computer replicates 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.

When Markram launched the project in the summer of 2005, as a joint venture with IBM, scientists criticized the project as an expensive pipedream. Neuroscience didn't need a supercomputer, it needed more molecular biologists. But Markram's attitude was different: "I wanted to model the brain because we didn't understand it. 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. 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 they will be able to start simulating an entire brain.

Before he began developing Blue Brain, Markram was best known for his painstaking studies of cellular connectivity. His 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 how billions of discrete cells weave themselves into functional networks.

When Markram looked 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."

Neuroscience is a reductionist science. It describes the brain in terms of its physical details, dissecting the mind into the smallest possible parts. Over the last 50 years, scientists have managed to uncover a seemingly endless list of molecules, enzymes, pathways, and genes. According to Markram, however, this scientific approach has exhausted itself. "I think that reductionism peaked five years ago," he says.

Markram hopes the Blue Brain project represents a whole new kind of neuroscience: "You need to look at the history of physics. 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."

Neuroscience is a thoroughly empirical discipline, rooted in the manual labor of molecular biology. The sole exception is computational neuroscience, a relatively new field. But Markram is dismissive of most computational neuroscience: "It's not interested enough in the biology. What they typically do is begin with a brain function they want to model 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."

Before the Blue Brain team could start constructing their model, the collected works of modern neuroscience had to be painstakingly programmed into the supercomputer. The problem is that neuroscience is still woefully incomplete. Even the simple neuron remains a mostly mysterious entity.

Markram focused on 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 cortex consists of thousands of these columns, each with a very precise purpose 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.

Felix Schürmann, the project manager of Blue Brain, oversees this daunting process. Schürmann shares a workspace with a diverse group. The 20 or so scientists working full-time on Blue Brain's software originate from 14 different countries.

Neurons are electrical processors that represent information as bursts of voltage. They control the flow of electricity by opening and closing different ion channels. 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 generated contradictory results. After several frustrating failures, the team realized that they needed to generate the data themselves.

Schürmann led me down the hall to Blue Brain's wet lab. The room looks like a generic neuroscience lab. But tucked in the corner of the room is a small robot, about the size of a microwave, filled with a variety of test tubes. A delicate metal claw is constantly moving back and forth across the tray, taking tiny sips from the different liquids. Schürmann says the robot is recording from a cell.

The Blue Brain team genetically engineers Chinese hamster ovary cells to express a single type of ion channel, then they subject the cells to a variety of physiological conditions. The robot 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."

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 were able to construct a precise map of ion channels. 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."

After assembling a three-dimensional model of 10,000 virtual neurons, the scientists began feeding the simulation electrical impulses. Because the model focused on 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.

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. "This all happened on its own," Markram says. "It was entirely spontaneous."

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. "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."

"We have already shown that the model can scale up," Markram says. "What is holding us back now are the computers." Markram estimates that to simulate the human brain, you'd need to process about 500 petabytes of data. That's about 200 times more information than is stored on all of Google's servers. But if computing speeds continue to develop at their current exponential pace, Markram believes that he'll be able to model a complete human brain on a single machine in ten years or less.

Neuroscience describes the brain from the outside. The paradox is that we don't experience our matter. "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."

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."

Markram's 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.

Installing Blue Brain in a robot will 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 with a mind of its own.

Markram wants to simulate what that brain experiences. If he can really find a way to see the brain from the inside,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.

Schürmann leads me into a large room. A lone Silicon Graphics supercomputer hums loudly in the center of the room. This computer translates the simulation into visual form. The vast data sets generated by the IBM supercomputer are rendered as short films. Schürmann starts the digital projector. 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.

Markram says the first step will be to decipher the connection between the sensations entering the robotic rat and the flickering voltages of its brain cells. The supercomputer should be able to take its map of the cortex and generate a movie of experience. "There is nothing inherently mysterious about the mind or anything it makes. 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."

Blue Brain Twitches

By Gautam Naik
Wall Street Journal, July 14, 2009

Edited by Andy Ross

For the last four years, Henry Markram has been building a biologically accurate artificial brain. Dubbed Blue Brain, the project is based in Switzerland. "We're building the brain from the bottom up, but in silicon," says Markram, the leader of Blue Brain, which is powered by an IBM supercomputer.

Markram has decided to work out the blueprint of its wiring and then use that map to rebuild the brain in an artificial form. He focused on a rat's neocortical column, or NCC, an elementary building block of the brain's neocortex, which is responsible for higher functions and thought.

A rat's NCC, comprised of about 10,000 neurons and their 10 million connections, functions much like a computer microprocessor. All mammals have NCCs, and the ones in humans aren't all that different from the ones in rats. However, humans have far more NCCs.

Markram began by collecting detailed information about the rat's NCC, down to the level of genes, proteins, molecules and the electrical signals that connect one neuron to another. These complex relationships were then turned into millions of equations, written in software. He then recorded data directly from rat brains to test the accuracy of the software.

At the Lausanne lab one recent afternoon, a pink sliver of rat brain sat in a beaker containing a colorless liquid. The neurons in the brain slice were still alive and actively communicating with each other. Nearby, a modified microscope recorded some of this inner activity in another brain slice. "We're intercepting the electro-chemical messages," said Markram, then testing the software against them for accuracy.

It takes the power of one microprocessor to mimic the behavior of a single neuron. To model the rat's NCC, the IBM computer can perform 22.8 trillion operations a second. The simulation is rendered as a 3D object. When zapped by a simulated electrical current, the neurons start to signal to each other and their wiring lights up. Tests indicate the same areas light up in the model as do in a real rat's brain, suggesting that Blue Brain is accurate.

The NCC's wiring fills up with a cascade of myriad signals. The NCC looks like an incredibly dense tangle of undergrowth. The neurons have synchronized their behavior. "The cells start to take on a life of their own," says Markram.

AR  Next step: The Human Brain Project, 2013

Billion-Euro Brain

By Jonathon Keats
Wired, May 2013

Edited by Andy Ross

At a TED conference in Oxford in 2009, Henry Markram announced a plan to deliver a sentient hologram within a decade. He hoped to wipe out all mental disorders and create a self-aware AI. And he said he would do all this by building a complete model of a human brain and running it on a supercomputer.

His Human Brain Project aims to simulate the functions of all 86 billion neurons in the human brain, and the 100 trillion connections that link them. Once he has a plug-and-play brain, anything is possible. You could take it apart to figure out the causes of brain diseases. You could rig it to robotics and develop a whole new range of intelligent technologies. You could strap on VR glasses and experience a brain other than your own.

Markram and his team have simulated the behavior of a million-neuron portion of the rat neocortex. They hope their methods can scale. They don't know if they can build out the rest of the rat brain, let alone the vastly more complex human brain. In January 2013, the European Commission awarded him a billion euros to try.

There are three other big brain initiatives:

1 The Allen Brain Atlas maps the correlation between specific genes and specific structures and regions in both human and mouse brains.

2 The Human Connectome Project uses noninvasive imaging techniques that show where wires are bundled and how those bundles are connected in human brains.

3 The Brain Activity Map aims to show circuits firing in real time. At present this is feasible only for fruit flies. Scaled up to human dimensions, such a map would chart a web of activity but leave out much of what is known of brain function at a molecular and functional level.

Markram: "The Brain Activity Map and other projects are focused on generating more data. The Human Brain Project is about data integration."

As a student, Markram worked in a lab in Cape Town where he did hundreds of experiments recording the effect of a neurotransmitter on neurons in the brain stem. He got his PhD at the Weizmann Institute of Science in Israel and went on to consecutive postdocs in Bethesda, Maryland, and in Heidelberg, Germany.

In 1995, back at Weizmann, Markram discovered 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. He published his results, became a tenured professor, and saw that he had to aim higher.

Neuroscientist Patrick Aebischer, the president of the Swiss Federal Institute of Technology, recruited Markram in 2002, and in 2005 bought him an IBM Blue Gene supercomputer.

Markram uses predictive reverse engineering to leapfrog data from wet labs. He needed good experimental data for detailed modeling, but without modeling and simulation the data would be useless. With a multilevel model of the rat brain as a template, scientists can make progress.

Caltech neuroscientist Christof Koch: "I like his vision. The guy has cojones."

Markram thinks he can determine the causes of all 600 known brain disorders: "It's not about understanding one disease. It's about understanding a complex system that can go wrong in 600 different ways. It's about finding the weak points."

He plans to make his first brain model soon and "open up this new telescope to the scientific community" by 2016.