Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules.
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
29 Sep 2016
|
| In: |
Nature Communications
Year: 2016, Volume: 7 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/ncomms12611 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/ncomms12611 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/ncomms12611 
|
| Author Notes: | Alexander Serb, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein & Themis Prodromakis |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1697223087 | ||
| 003 | DE-627 | ||
| 005 | 20220818070320.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200505s2016 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/ncomms12611 |2 doi | |
| 035 | |a (DE-627)1697223087 | ||
| 035 | |a (DE-599)KXP1697223087 | ||
| 035 | |a (OCoLC)1341318504 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 29 |2 sdnb | ||
| 100 | 1 | |a Serb, Alexander |e VerfasserIn |0 (DE-588)1210561972 |0 (DE-627)1698609566 |4 aut | |
| 245 | 1 | 0 | |a Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses |c Alexander Serb, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein & Themis Prodromakis |
| 264 | 1 | |c 29 Sep 2016 | |
| 300 | |a 9 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Gesehen am 05.05.2020 | ||
| 520 | |a Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules. | ||
| 700 | 1 | |a Bill, Johannes |e VerfasserIn |0 (DE-588)1138256773 |0 (DE-627)895583453 |0 (DE-576)492447241 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nature Communications |d [London] : Springer Nature, 2010 |g 7(2016) Artikel-Nummer 12611, 9 Seiten |h Online-Ressource |w (DE-627)626457688 |w (DE-600)2553671-0 |w (DE-576)331555905 |x 2041-1723 |7 nnas |a Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses |
| 773 | 1 | 8 | |g volume:7 |g year:2016 |g extent:9 |a Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses |
| 856 | 4 | 0 | |u https://doi.org/10.1038/ncomms12611 |x Verlag |x Resolving-System |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u https://www.nature.com/articles/ncomms12611 |x Verlag |z lizenzpflichtig |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20200505 | ||
| 993 | |a Article | ||
| 994 | |a 2016 | ||
| 998 | |g 1138256773 |a Bill, Johannes |m 1138256773:Bill, Johannes |d 130000 |d 130700 |e 130000PB1138256773 |e 130700PB1138256773 |k 0/130000/ |k 1/130000/130700/ |p 2 | ||
| 999 | |a KXP-PPN1697223087 |e 3658977302 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"recId":"1697223087","language":["eng"],"type":{"bibl":"article-journal","media":"Online-Ressource"},"note":["Gesehen am 05.05.2020"],"person":[{"display":"Serb, Alexander","roleDisplay":"VerfasserIn","role":"aut","family":"Serb","given":"Alexander"},{"display":"Bill, Johannes","roleDisplay":"VerfasserIn","role":"aut","family":"Bill","given":"Johannes"}],"title":[{"title_sort":"Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses","title":"Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses"}],"relHost":[{"title":[{"title":"Nature Communications","title_sort":"Nature Communications"}],"part":{"year":"2016","extent":"9","volume":"7","text":"7(2016) Artikel-Nummer 12611, 9 Seiten"},"pubHistory":["2010-"],"recId":"626457688","language":["eng"],"type":{"bibl":"periodical","media":"Online-Ressource"},"note":["Gesehen am 13.06.24"],"disp":"Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapsesNature Communications","id":{"issn":["2041-1723"],"zdb":["2553671-0"],"eki":["626457688"]},"origin":[{"publisher":"Springer Nature ; Nature Publishing Group UK","dateIssuedDisp":"[2010]-","publisherPlace":"[London] ; [London]"}],"physDesc":[{"extent":"Online-Ressource"}]}],"physDesc":[{"extent":"9 S."}],"name":{"displayForm":["Alexander Serb, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein & Themis Prodromakis"]},"id":{"eki":["1697223087"],"doi":["10.1038/ncomms12611"]},"origin":[{"dateIssuedKey":"2016","dateIssuedDisp":"29 Sep 2016"}]} | ||
| SRT | |a SERBALEXANUNSUPERVIS2920 | ||