A neuromorphic computing device is essentially an organic robot brain. When it learns something, it rewires its electric circuits like a brain does to save a memory.
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.
Previously, some of the researchers who worked with Gkoupidenis’s Max Planck group on the new study had shown that organic polymers can record aspects of their past states. This finding had suggested that the polymers can “remember” certain information, such as the sequence of turns required to navigate a maze. So in the recent investigation, the team used organic material to construct transistors—power- and signal-switching devices—and arranged them into a circuit.
At each crossroad, the machine turned right by default. But each time it eventually hit a side wall, it received a “slap on the nose,” as van de Burgt puts it. “Well, that’s a fancy [phrase] for basically tuning the resistance a little bit,” he adds. This means that when the robot was given a light human tap or hit a wall, the sensors carried that touch signal to the organic circuit.
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