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

Alexander Serb (), Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein and Themis Prodromakis
Additional contact information
Alexander Serb: University of Southampton
Johannes Bill: Institute for Theoretical Computer Science, Graz University of Technology
Ali Khiat: University of Southampton
Radu Berdan: Imperial College
Robert Legenstein: Institute for Theoretical Computer Science, Graz University of Technology
Themis Prodromakis: University of Southampton

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: 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.

Date: 2016
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DOI: 10.1038/ncomms12611

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