Artificial Intelligence

UC Santa Barbara Researchers Create Functioning Artificial Neural Circuit

• By Leila Meyer
• 05/20/15

Researchers at the University of California, Santa Barbara have created a circuit with about 100 artificial synapses and demonstrated its ability to classify images.

As part of the demonstration, the artificial neural network successfully classified images of the letters "z", "v" and "n", each of which was stylized differently or saturated with visual noise.

The achievement is an early step toward the eventual goal of developing artificial neural circuitry on the scale of a human brain, which contains one quadrillion synaptic connections. According to information from UC Santa Barbara, "the human brain remains a model of computational power and efficiency for engineers" because it "can accomplish certain functions in a fraction of a second what computers would require far more time and energy to perform."

The artificial neural network uses electronic components called memristors — a combination of memory and resistors — that change electrical resistance depending on the direction of the charge. While conventional transistors are based on the movement of electrons through semiconductors, memristors are based on ionic movement, which is similar to the generation of electrical signals in the human brain. According to information from the university, memristors are much more efficient than transistors at creating human brain-type functionality, but many more memristors will be required to build more complex neural networks.

The next step for the researchers is to continue improving the performance of the memristors and eventually integrate a memristor neural network with conventional semiconductor technology in order to complete more complex tasks. Practical applications for this technology could include improved medical imaging, navigation systems and database searches based on images rather than text.

The researchers have published their findings in the journal Nature.