Issue link: http://cp.revolio.com/i/1161
38 summer 2009 38 summer 2009 38 38 The edge of real Brain Complexity The robots in Jeff Krichmar's lab don't look like much. CArL-1, his latest model, is a squat, white trash can contraption with a couple of shopping cart wheels bolted to its side, a video camera wired to the lid, and a couple of bunny ears taped on for good measure. But open up that lid and you'll fi nd something remarkable — the beginnings of a truly biological nervous system. CArL-1 has thousands of neurons and millions of synapses that, he says, "are just about the edge of the amount of size and complexity found in real brains." Not surprisingly, robots built this way — using the same operating principles as our nervous system — are called neurobots. Krichmar emphasizes that these artifi cial nervous systems are based upon neurobiological principles rather than computer models of how intelligence works. The fi rst of those principles, as he describes it, is: "The brain is embodied in the body and the body is embedded in the environment — so we build brains and then we put these brains in bodies and then we let these bodies loose in an environment to see what happens," This has become something of a foundational principle — and the great and complex challenge — of neurobotics. When you embed a brain in a body, you get behavior not often found in other robots. Brain bots don't work like Aibo. You can buy a thousand different Aibos and they all behave the same. But brain bots, like real brains, learn through trial and error, and that changes things. "Put a couple of my robots inside a maze," says Krichmar, "let them run it a few times, and what each of those robots learns will be different. Those differences are magnifi ed into behavior pretty quickly." When psychologists defi ne personality, it's along the lines of "idiosyncratic behavior that's predictive of future behavior." What Krichmar is saying is that his brain bots are developing personalities — and they're doing it pretty quickly. Krichmar's bots develop personalities because, instead of pre- programming behaviors, these robots have neuro-modulatory systems or value judgment systems — move towards something good, move away from something bad — that are modeled around the human's dopaminergic system (for wanting or reward-based behaviors) and the noradrenergic system (for vigilance and surprise). When something salient occurs — in CArL-1's case that's usually bumping into a sensor in a maze — a signal is sent to its brain telling the bot to react to the event and remember the context for later. This is conditional learning and it mimics what occurs in real brains. It also allows Krichmar to examine one of the great puzzles in systems neuroscience — how do the brain's neurons work together? "We're pretty sure you need a certain brain size for the level of complexity we see in biological organisms," he says, "but we don't have the tools to make a network that big behave in any stable way. The biological brain is remarkably stable. We can alter it with drugs, we can put it into all sorts of varied environments, pretty much it still knows how to function. Our robots are still brittle by comparison." Besides personality, another thing these robots develop are types of episodic and categorical memory not found in other computers. After running early brain bots Darwin X and Darwin XI through a few mazes, edelman, working alongside Krichmar and a researcher named Jason Fleischer, found they'd naturally developed place cells — meaning they didn't program them in. These are cells in the Hippocampus that fi re whenever an animal passes through a specifi c location, essentially linking place with time. more than that, when edelman examined his bots' brains, he found these place cells would not only fi re based on where the robot had been, but also on where it was planning to go, "which," says Krichmar, "is exactly what you would see in the brain of a rat and nothing anyone's seen in a robot before."