For over two decades, his team have teased out the basic details of a rat’s neurons, and produced a virtual set of cylindrical brain slices called cortical columns. The current simulation has 100 of these columns, and each has around 10,000 neurons – less than 2% of a rat’s brain and just over 0.001% of ours. “You have to practice this first with rodents so you’re confident that the rules apply, and do spot checks to show that these rules can transfer to humans,” he says.
Eugene Izhikevich from the Brain Corporation, who helped to build a model with 100 billion neurons, is convinced that we should be able to build a network with all the anatomy and connectivity of a real brain. An expert could slice through it and not tell the difference. “It’d be like a Turing test for how close the model would be to the human brain,” he says.
But that would be a fantastic simulation of a dead brain in an empty vat. A living one pulses with electrical activity – small-scale currents that travel along neurons, and large waves that pass across entire lobes. Real brains live inside bodies and interact with environments. If we could simulate this dynamism, what would emerge? Learning? Intelligence? Consciousness?
“People think I want to build this magical model that will eventually speak or do something interesting,” says Markram. “I know I’m partially to blame for it – in a TED lecture, you have to speak in a very general way. But what it will do is secondary. We’re not trying to make a machine behave like a human. We’re trying to organise the data.”
That worries neuroscientist Chris Eliasmith from the University of Waterloo in Ontario, Canada. “The project is impressive but might leave people baffled that someone would spend a lot of time and effort building something that doesn’t do anything,” he says. Markram’s isn’t the only project to do this. Last November, IBM presented a brain simulation called SyNAPSE, which includes 530 billion neurons with 100 trillion synapses connecting them, and does... not very much. It’s basically a big computer. It still needs to be programmed. “Markram would complain that those neurons aren’t realistic enough, but throwing a ton of neurons together and approximately wiring them according to biology isn’t going to bridge this gap,” says Eliasmith.
Eliasmith has taken a completely different approach. He is putting function first. Last November, he unveiled a model called Spaun, which simulates a relatively paltry 2.5 million neurons but shows behaviour. It still simulates the physiology and wiring of the individual neurons, but organises them according to what we know about the brain’s architecture. It’s a top-down model, as well as a bottom-up one, and sets the benchmark for brain simulations that actually do something. It can recognise and copy lists of numbers, carry out simple arithmetic, and solve basic reasoning problems. It even makes errors in the same way we do – for example, it’s more likely to remember items at the start and end of a list.
But the point of Spaun is not to build an artificial brain either. It’s a test-bed for neuroscience – a platform that we can use to understand how the brain works. Does Region X control Function Y? Build it and see if that’s true. If you knock out Region X, will Spaun’s mental abilities suffer in a predictable way? Try it.