Now, researchers at Arizona State University have come up with a model that could help unlock some of the secrets of how humans process patterns and possibly lead to smarter robots. The advance concerns oscillatory associative memory networks, basically the ability to see a pattern, store it and then retrieve that pattern when needed. A good example is how humans can recognize faces.
"It is still a really big mystery as to how human beings can remember so many faces, but that it is extremely difficult for a computer to do," said Ying-Cheng Lai, an ASU professor of mathematics and a professor of electrical engineering in the Ira A. Fulton School of Engineering.
Lai, along with former post-doctoral fellow Takashi Nishikawa (now at Southern Methodist University), and former ASU professor Frank Hoppenstaedt (now at New York University), published their research, "Capacity of Oscillatory Associative Memory Networks with Error-Free Retrieval," in a recent issue of American Physical Society's Physical Review Letters.
Although what the team developed is a mathematical and computational model for oscillatory networks that can be used associated memory devices, implementation of the model is possible by using electronic circuits as phase-locked loops.
"Computers can do very fast computation that humans cannot do, but humans can recognize patterns so much better than computers," Lai said. "The question is why. What is the fundamental mechanism that a biological system like us can make use of and try to memorize patterns."
A key to pattern recognition is the use of oscillatory associative memory networks. Lai said the human brain
Contact: Skip Derra
Arizona State University