Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9193
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dc.contributor.authorZhang, Zhizhong-
dc.contributor.authorLin, Kelian-
dc.contributor.authorZhang, Yue-
dc.contributor.authorBournel, Arnaud-
dc.contributor.authorXia, Ke-
dc.contributor.authorKläui, Mathias-
dc.contributor.authorZhao, Weisheng-
dc.date.accessioned2023-06-19T08:14:13Z-
dc.date.available2023-06-19T08:14:13Z-
dc.date.issued2023-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/9210-
dc.description.abstractNeuromorphic computing is expected to achieve human-brain performance by reproducing the structure of biological neural systems. However, previous neuromorphic designs based on synapse devices are all unsatisfying for their hardwired network structure and limited connection density, far from their biological counterpart, which has high connection density and the ability of meta-learning. Here, we propose a neural network based on magnon scattering modulated by an omnidirectional mobile hopfion in antiferromagnets. The states of neurons are encoded in the frequency distribution of magnons, and the connections between them are related to the frequency dependence of magnon scattering. Last, by controlling the hopfion’s state, we can modulate hyperparameters in our network and realize the first meta-learning device that is verified to be well functioning. It not only breaks the connection density bottleneck but also provides a guideline for future designs of neuromorphic devices.en_GB
dc.language.isoengde
dc.rightsCC BY-NC*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subject.ddc530 Physikde_DE
dc.subject.ddc530 Physicsen_GB
dc.titleMagnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learningen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-9193-
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 08 Physik, Mathematik u. Informatikde
jgu.organisation.number7940-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleScience advancesde
jgu.journal.volume9de
jgu.journal.issue6de
jgu.pages.alternativeeade7439de
jgu.publisher.year2023-
jgu.publisher.nameAssoc.de
jgu.publisher.placeWashington, DC u.a.de
jgu.publisher.issn2375-2548de
jgu.organisation.placeMainz-
jgu.subject.ddccode530de
dc.date.updated2023-04-17T13:14:02Z-
jgu.publisher.licenceCC BY-NC-
jgu.publisher.doi10.1126/sciadv.ade7439de
elements.object.id152670-
elements.object.typejournal-article-
jgu.organisation.rorhttps://ror.org/023b0x485-
Appears in collections:JGU-Publikationen

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