Another computer is setting its wits to perform human tasks. But this computer is different. Instead of the tour de force processing of Deep Blue or Watson’s four terabytes of facts of questionable utility, Spaun attempts to play by the same rules as the human brain to figure things out. Instead of the logical elegance of a CPU, Spaun’s computations are performed by 2.3 million simulated neurons configured in networks that resemble some of the brain’s own networks. It was given a series of tasks and performed pretty well, taking a significant step toward the creation of a simulated brain.

Spaunstands for Semantic Pointer Architecture: Unified Network. It was given 6 different tasks that tested its ability to recognize digits, recall from memory, add numbers and complete patterns. Its cognitive network simulated the prefrontal cortex to handle working memory and the basal ganglia and thalamus to control movements. Like a human, Spaun can view an image and then give a motor response; that is, it is presented images that it sees through a camera and then gives a response by drawing with a robotic arm.

Will AI brains of the future look more like Watson or Spaun?

And its performance was similar to that of a human brain. For example, the simplest task, image recognition, Spaun was shown various numbers and asked to draw what it sees. It got 94 percent of the numbers correct. In a working memory task, however, it didn’t do as well. It was shown a series of random numbers and then asked to draw them in order. Like us with human brains, Spaun found the pattern recognition task easy, the working memory task not quite as easy.

The important thing here is not how well Spaun performed on the tasks – your average computer could find ways to perform much better than Spaun. But what’s important is that, in Spaun’s case, the task computations were carried out solely by the 2.3 million artificial neurons spiking in the way real neurons spike to carry information from one neuron to another. The visual image, for example, was processed hierarchically, with multiple levels of neurons successively extracting more complex information, just as the brain’s visual system does. Similarly, the motor response mimicked the brain’s strategy of combining many simple movements to produce an optimal, single movement while drawing.

Chris Eliasmith, from the University of Waterlook in Ontario, Canada and lead author of the study is happy with his cognitive creation. “It’s not as smart as monkeys when it comes to categorization,” he told CNN, “but it’s actually smarter than monkeys when it comes to recognizing syntactic patterns, structured patterns in the input, that monkeys won’t recognize.”

Watch Spaun work through its tasks in the following video.

One thing Spaun can’t do is perform tasks in realtime. Every second you saw Spaun performing tasks in the video actually requires 2.5 hours of numbers crunching by its artificial brain. The researchers hope to one day have it perform in realtime.
It’s important to note that Spaun isn’t actually learning anything by performing these tasks. Its neural nets are hardwired and are incapable of the modifications that real neurons undergo when we learn. But producing complex behavior from a simulated neuronal network still represents an important initial step toward building an artificial brain. Christian Machens, a neuroscientist at the Champalimaud Neuroscience Programme in Lisbon and was not involved in the study, writes in Science that the strategy for building a simulated brain is “to not simply incorporate the largest number of neurons or the greatest amount of detail, but to reproduce the largest amount of functionality and behavior.”

We’re still a long way from artificial intelligence that is sentient and self-aware. And there’s no telling if the robots of the future will have brains that look like ours or if entirely different solutions will be used to produce complex behavior. Whatever it looks like, Spaun is a noble step in the right direction.

Peter Murray was born in Boston in 1973. He earned a PhD in neuroscience at the University of Maryland, Baltimore studying gene expression in the neocortex. Following his dissertation work he spent three years as a post-doctoral fellow at the same university studying brain mechanisms of pain and motor control. He completed a collection of short stories in 2010 and has been writing for Singularity Hub since March 2011.