Neuromorphic computing finds new life in machine learning

Efforts have been underway for forty years to build computers that might emulate some of the structure of the brain in the way they solve problems. To date, they have shown few practical successes. But hope for so-called neuromorphic computing springs eternal, and lately, the endeavor has gained some surprising champions. The research lab of Terry Sejnowski at The Salk Institute in La Jolla this year proposed a new way to train "spiking" neurons using standard forms of machine learning, called "recurrent neural networks," or "RNNs." And Hava Siegelmann, who has been doing pioneering work on alternative computer designs for decades, proposed along with colleagues a system of spiking neurons that would perform what's called "unsupervised" learning.