KIP publications

 
year 2016
author(s) Alexander Serb, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein and Themis Prodromakis
title Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
KIP-Nummer HD-KIP 16-88
KIP-Gruppe(n) F9
document type Paper
source Nature Communications 7 (2016) 12611
doi 10.1038/ncomms12611
Abstract (en)

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.

bibtex
@article{serb2016unsupervised,
  author   = {Serb, Alexander and Bill, Johannes and Khiat, Ali and Berdan, Radu and Legenstein, Robert and Prodromakis, Themis},
  title    = {Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses},
  journal  = {Nature Communications},
  year     = {2016},
  volume   = {7},
  pages    = {12611}
}
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