How can a simulation crack the mystery of the brain? Don’t you need to understand it before you can simulate it?
To understand how a protein behaves in the context of a whole brain, you’re going to need to put it into a whole brain. You’re going to need to play with it, change its parameters, take it out, put it in, make a mutation. We need the flexibility to be able to explore thousands, hundreds of thousands, millions of parameters. A model is one way to do it—but not the kind of model that people have been using. I’m talking about real, biologically constrained models. The most serious misconception about Blue Brain is that people think we’re doing a modeling project. Actually Blue Brain is very much about reverse engineering, looking at all the data, standardizing the data, getting the information into a framework where we can even do correlation-based science on it, building automatic tools to synthesize those data into biological phenomena. I think of the process as virtualizing life, virtualizing the brain.
How do you even approach such a huge and radical project?
Before we did this, it was a three-year Ph.D. project to simulate even one neuron. And you needed an entire, very powerful computer to run that simulation of one neuron. You needed the whole process. Of course, today computers are a little more powerful, and I can run a simulation of 100 neurons. But there’s really no point in doing a 100-neuron simulation. The reason is simple: A neuron lives in an environment. It receives thousands of inputs. So actually you need to make a quantum leap from one neuron to 10,000 neurons. You need to make that leap into what we call a microcircuit. A circuit of five neurons is not what the mammalian brain is made up of. To simulate the neurocircuitry that is creating the mammalian brain, you need to make a quantum jump in complexity. You need at least 10,000 computers to do that. And that’s what Blue Gene is—16,000 processors squeezed into a space the size of four refrigerators. It was important for us to have that many processors, because on each one we’ve got 1,000 neurons. The processors themselves did not have to be extremely powerful. They just needed enough memory to hold the neurons.
You are modeling not a human brain or a monkey brain but something very specific—the neocortical column in a two-week-old rat. Why that particular set of neurons?
Blue Brain is a bio-driven project, meaning that we work to capture the biological elements, processes, and principles as mathematical models, and then run simulations to see how they mimic biology. We are trying to re-create biology in software as accurately as possible. I was not prepared to sacrifice a lot of cats or primates—this research requires dissecting brains and comparing the simulation to the real thing—so we had to choose either mice or rats. Then it was a question of which brain region to focus on. Even though the neocortex is the most advanced region, it’s got more order and organization and therefore actually is more tractable. If you go into the brain stem or into other subcortical areas of the brain, the neurons have no distinguishing features. They’re all kinds of shapes. A two-week-old animal is ideal, actually, because at that stage slices of the brain preserve extremely well for 24 to 48 hours. The circuit at that age is going to be in 75 to 80 percent of the final state it will be in during the adult stage. What it gives us is the template for brain circuitry. Once we have the template we look for variations, and then we can model development. We can now model younger columns, and we can model older columns. When we find out the key differences between species, we will be able to start simulating and modeling evolution, too.
Your experiment has been going on for more than four years now. What have you learned so far about the neural processes in the brain?
One very interesting property that emerged is a rhythm of electrical activity called gamma oscillations. It appeared one week when we added in the step of biological simulation. We did not try to build it in—it just showed up. Gamma oscillations are the basis for consciousness, according to a famous theory. The theory holds that when the brain goes into high-frequency (40- to 80-hertz) oscillations, those oscillations do perceptual binding, which is the foundation of consciousness. I don’t think that Blue Brain is conscious at this point, though.
It’s significant that we didn’t specifically try to model the phenomenon in the brain. All we have to do is pay attention to the fact that we are building it correctly, and these phenomena emerge. The whole circuit goes into this resonant state, which is an amazing state. Now we can dissect the circuit and find out exactly which neurons were crucial, which pathways, which receptors, and so forth.
As we’ve taken steps closer to the biology, the circuit has started to display more and more of the actual biological phenomena that we find in experiments, with more and more precision and accuracy and elegance. This is very encouraging because the model could have gone in any other direction. It could have just not worked. As you put in more and more fine-tuned parameters, it might start doing all kinds of things you would not want or expect it to do.
Can you also use Blue Brain to help with medical problems—to study the nature of neurological diseases?
Actually, this is the only way to study them. When you turn on this column and you run it, you think, my God, there’s not a single neurological disease today in which anybody knows what is malfunctioning in this circuit—which pathway, which synapse, which neuron, which receptor. Doctors don’t even know this for a single drug—I mean, this is a multibillion-dollar industry!—that they’re giving for Parkinson’s disease, for depression, schizophrenia, attention deficit, autism, dementia, Alzheimer’s. When they give a drug, they have no idea what it does to this processor. And the neocortical column is the elementary processor for human beings to have coherent perception, attention, and memory. This is shocking. I mean, we are living in such a primitive time of medicine, you cannot imagine.