Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.
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.
Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK.
Alexander
A
Serb
Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria.|Heidelberg University, Department of Physics and Astronomy, Kirchhoff Institute for Physics, 69120 Heidelberg, Germany.
Johannes
J
Bill
Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK.
Ali
A
Khiat
Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK.
Radu
R
Berdan
Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria.
Robert
R
Legenstein
Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK.
Themis
T
Prodromakis
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