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 Blue Brain team is now developing a model of a complete rat brain—that should be done in 2010—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 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.
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?’”
As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments, analysis and visualization. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specification problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in its native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation.
Notes from Henry Markram TED Talk, July 2009
Henry Markram is leading the Blue Brain Project, which hopes to create a realistic digital 3D model of the whole human brain within the next 10 years. (The simulation promises to do all the things that real human brains can do, including consciousness.) He’s done a proof of concept by modeling half of a rodent brain. Now he’s scaling up the project to reach a human brain.
But why? It’s essential to understand the brain for us to get along in society. We can’t keep doing animal experimentation forever. We have to embody our data in a working digital model. We need better medicines that are more specific, more concrete, more precise. (Also, it’s just fascinating.)
Markram, for the first time, shares how he is addressing one theory of how the brain works. The theory is that the brain “builds” a version of the universe and projects this version, like a bubble, all around us. But Markram says we can directly address this philosophical question with science. Anesthetics don’t work by blocking receptors. They introduce a noise into the brain to confuse the neurons to prevent you from making “decisions.” You must make decisions to perceive anything. 99% of what you see in a room is not what comes in through the eyes — it’s what you infer about that room.
Instead of speculating or philosophizing, we can actually build something to test the theories.
It took the universe 11 billion years to build a brain. The big step was the neocortex. It allowed animals to cope with parenthood, social functions. So the neocortex is the ultimate solution, the pinnacle of complex design that the universe has produced. The neocortex continues to evolve rapidly. The neocortex uses the same basic unit for computation, over and over again, and built up so fast evolutionarily that the brain had to fold itself up to fit more of the stuff into the skull.
The holy grail for neuroscience is to understand the design of the neocortical column. It will help us understand not just the brain, but perhaps physical reality. Understanding the structures that make it up is extremely difficult, because beyond just cataloging the parts, you have to figure out how they actually work — and then build realistic digital models.
Mathematics underlies the models of the brain. Each neuron has a mathematical representation. Even though this simplifies things, you still need a huge computer to do the kinds of simulations Markram is talking about. You’d need one laptop for every single neuron in order to accurately model it. So what do you do? You go to IBM!