acm-header
Sign In

Communications of the ACM

ACM News

Lego Robot with an Organic 'Brain' Learns to Navigate a Maze


View as: Print Mobile App Share:

The organic neuromorphic chip also has the advantage of requiring less power than a standard chip; it also is relatively cheap to produce, and comparatively simpler than a silicon system,

Credit: Sean Gladwell/Getty Images

In the winter of 1997 Carver Mead lectured on an unusual topic for a computer scientist: the nervous systems of animals, such as the humble fly. Mead, a researcher at the California Institute of Technology, described his earlier idea for an electronic problem-solving system inspired by nerve cells, a technique he had dubbed "neuromorphic" computing. A quarter-century later, researchers have designed a carbon-based neuromorphic computing device—essentially an organic robot brain—that can learn to navigate a maze.

A neuromorphic chip memorizes information similarly to the way an animal does. When a brain learns something new, a group of its neurons rearrange their connections so they can communicate more quickly and easily. As a common saying in neuroscience goes, "Neurons that fire together wire together." When a neuromorphic chip learns, it rewires its electric circuits to save the new behavior like a brain does to save a memory.

The idea of brainlike computation has been around for a while. But Paschalis Gkoupidenis of the Max Planck Institute for Polymer Research in Mainz, Germany, and his neuromorphic research team are pioneers in crafting this technology from organic materials. To build their chip, the researchers used long chains of carbon-based molecules called polymers, which are soft and, in some ways, behave similarly to living tissues. In order to let their material carry an electric charge like real neurons, which are energy-efficient and operate in a watery medium, the scientists coated the organic material with an ion-rich gel. This provided "more degrees of freedom to mimic biological processes," Gkoupidenis says.

From Scientific American
View Full Article

 


 

No entries found