Computer science: Nanoscale connections for brain-like circuits

Tiny circuit elements called memristors have been used as connections in an artificial neural network – enabling the system to learn to recognize letters of the alphabet from imperfect images. See Letter p.61 Building neuromorphic networks matching the cognitive complexity of their biological prototypes but with higher performance is one of the great challenges in computing. One promising approach to such devices — potentially simpler than those based on complex silicon circuits — combines complementary metal-oxide-semiconductors (CMOSs) with adjustable two-terminal resistive devices (memristors). Here Dmitri Strukov and colleagues demonstrate a transistor-free metal-oxide memristor network with low device variability that works as a single-layer perceptron. That is, it can learn to recognize imperfect 3 × 3 pixel black-and-white patterns as one of three letters of the alphabet. The strength of this approach is its scalability so that larger neuromorphic networks capable of more challenging tasks should be possible.