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Science 26 September 1997:
Vol. 277. no. 5334, p. 1935
DOI: 10.1126/science.277.5334.1935
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Special News Report

COMPUTER ENGINEERING:
A Subtler Silicon Cell for Neural Networks

Sunny Bains

Neural nets traditionally consist of so-called sigmoidal neurons, circuits that add up incoming signals until they reach a fixed threshold and then fire themselves. But a University of Pennsylvania research has now developed so-called bifurcation neurons, which switch between different modes of operation depending on subtler factors, including the interaction between many incoming signals and the neuron's recent history. This work could yield neural nets with more complex behavior than has been seen in networks to date, such as the ability to see, recognize, and even react to the world in real time.

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THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Exploiting the controlled responses of chaotic elements to design configurable hardware.
S. Sinha and W. L Ditto (2006)
Phil Trans R Soc A 364, 2483-2494
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