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dc.contributor.authorKumar, Ankiten_US
dc.contributor.authorMohanty, Pritirajen_US
dc.coverage.spatialEnglanden_US
dc.date2017-02-28
dc.date.accessioned2018-04-05T18:30:38Z
dc.date.available2018-04-05T18:30:38Z
dc.date.issued2017-02-28
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/28341856
dc.identifier.citationAnkit Kumar, Pritiraj Mohanty. 2017. "Autoassociative Memory and Pattern Recognition in Micromechanical Oscillator Network." Scientific Reports. Volume 7, Article number: 411.
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/2144/28190
dc.description.abstractTowards practical realization of brain-inspired computing in a scalable physical system, we investigate a network of coupled micromechanical oscillators. We numerically simulate this array of all-to-all coupled nonlinear oscillators in the presence of stochasticity and demonstrate its ability to synchronize and store information in the relative phase differences at synchronization. Sensitivity of behavior to coupling strength, frequency distribution, nonlinearity strength, and noise amplitude is investigated. Our results demonstrate that neurocomputing in a physically realistic network of micromechanical oscillators with silicon-based fabrication process can be robust against noise sources and fabrication process variations. This opens up tantalizing prospects for hardware realization of a low-power brain-inspired computing architecture that captures complexity on a scalable manufacturing platform.en_US
dc.format.extent411en_US
dc.languageeng
dc.relation.ispartofSci Rep
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectNeural networksen_US
dc.subjectSynchronizationen_US
dc.titleAutoassociative memory and pattern recognition in micromechanical oscillator networken_US
dc.typeArticleen_US
dc.identifier.doi10.1038/s41598-017-00442-y
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Physicsen_US
pubs.publication-statusPublished onlineen_US


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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/