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Computer science

Nanoscale connections for brain-like circuits

Nature volume 521, pages 37–38 (2015)Cite this article

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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

The human brain is a network of billions of neurons that communicate through some 1015 synaptic connections. Our cognitive abilities result from computations performed in this vast network, which is shaped by experience as learning drives changes in the strengths of synaptic coupling. Synthetic neuromorphic circuits use the same massively parallel architecture in complementary metal-oxide-semiconductor (CMOS) technology, which underpins much of the circuitry in conventional computers. But designing neuromorphic chips that approach the connectivity of the human brain remains challenging. On page 61 of this issue, Prezioso et al.1 report a major advance in the field — an artificial neural network that learns to solve a visual-recognition task on the basis of artificial synapses formed from devices called memristors.

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Figure 1: A memristive neural network.

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References

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  1. Robert Legenstein is at the Institute for Theoretical Computer Science, Graz University of Technology, Graz 8010, Austria.,

    Robert Legenstein

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  1. Robert Legenstein
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Correspondence to Robert Legenstein.

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Legenstein, R. Nanoscale connections for brain-like circuits. Nature 521, 37–38 (2015). https://doi.org/10.1038/521037a

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