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

Alexander Serb*, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein, Themis Prodromakis

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.
Original languageEnglish
Article number12611
JournalNature Communications
Volume7
DOIs
Publication statusPublished - 2016

Fields of Expertise

  • Information, Communication & Computing

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