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

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
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.
1-9
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Johannes, Bill
508d4cdf-c1c7-4130-9c95-4ef0094daff5
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Berdan, Radu
e259cd5a-6e30-4439-94c0-9c44903e1e75
Legenstein, Robert
4b546ba3-87f7-4228-aa97-b3379597c695
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Johannes, Bill
508d4cdf-c1c7-4130-9c95-4ef0094daff5
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Berdan, Radu
e259cd5a-6e30-4439-94c0-9c44903e1e75
Legenstein, Robert
4b546ba3-87f7-4228-aa97-b3379597c695
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Serb, Alexander, Johannes, Bill, Khiat, Ali, Berdan, Radu, Legenstein, Robert and Prodromakis, Themis (2016) Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nature Communications, 7 (1261), 1-9. (doi:10.1038/NCOMMS12611). (PMID:27681181)

Record type: Article

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.

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ncomms12611.pdf - Version of Record
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More information

Accepted/In Press date: 15 July 2016
e-pub ahead of print date: 29 September 2016
Published date: 29 September 2016
Organisations: Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 397929
URI: https://eprints.soton.ac.uk/id/eprint/397929
PURE UUID: 52a66092-c5f0-451f-ab4c-ef2a564b3809
ORCID for Themis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 12 Jul 2016 13:31
Last modified: 03 Dec 2019 06:38

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