Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

Abstract

Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules.