New research from Washington University in St. Louis finds that the ability of silicon neurons to learn to communicate and establish networks is key to producing artificial intelligence systems that are as energy-efficient as biological ones.
Researchers previously found that neurons in a computational system act as though they are embedded in a rubber sheet; the new study demonstrates how neurons learn to choose the most energy-efficient perturbations and wave patterns in the rubber sheet. Each neuron adjusts the electrical stiffness of the rubber sheet to vibrate the entire network in the most energy-efficient manner, using only local information.
A silicon neuron, researchers discovered, can test all communications routes at once and identify the most efficient way to connect to complete a specific task.
From Washington University in St. Louis
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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