Tuesday, August 21, 2012
Here Come the Neurobots
Can we build a brain from the ground up, one neuron (or so) at a time? That’s the goal of neurobotics, a science that sits at the convergence of robotics, artificial intelligence, computer science, neuroscience, cognitive psychology, physiology, mathematics and several different engineering disciplines. Computationally demanding and requiring a long view and a macroscopic perspective (qualities not often found in our world of impatient specialization), the field is so fundamentally challenging that there are only around five labs pursuing it worldwide.
Neurobotics is an outgrowth of a growing realization that, when it comes to understanding the brain, neither computer simulations nor top-down robotic models are getting anywhere close. As Dartmouth neuroscientist and Director of the Brain Engineering Lab Richard Granger puts it, “The history of top-down-only approaches is spectacular failure. We learned a ton, but mainly we learned these approaches don’t work.”
Gerald Edelman, a Nobel Prize-winning neuroscientist and Chairman of Neurobiology at Scripps Research Institute, first described the neurobotics approach back in 1978. In his “Theory of Neuronal Group Selection,” Edelman essentially argued that any individual’s nervous system employs a selection system similar to natural selection, though operating with a different mechanism. “It’s obvious that the brain is a huge population of individual neurons,” says UC Irvine neuroscientist Jeff Krichmar. “Neuronal Group Selection meant we could apply population models to neuroscience, we could examine things at a systems’ level.” This systems approach became the architectural blueprint for moving neurobotics forward.