DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sung, Sang Hyun | ko |
dc.contributor.author | Kim, Tae Jin | ko |
dc.contributor.author | Shin, Hyera | ko |
dc.contributor.author | Im, Tae Hong | ko |
dc.contributor.author | Lee, Keon Jae | ko |
dc.date.accessioned | 2022-07-11T08:01:01Z | - |
dc.date.available | 2022-07-11T08:01:01Z | - |
dc.date.created | 2022-07-11 | - |
dc.date.created | 2022-07-11 | - |
dc.date.created | 2022-07-11 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.citation | NATURE COMMUNICATIONS, v.13, no.1 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | http://hdl.handle.net/10203/297314 | - |
dc.description.abstract | Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of synaptic plasticity has shown promising results after the advent of memristors. However, neuronal intrinsic plasticity, which involves in learning process through interactions with synaptic plasticity, has been rarely demonstrated. Synaptic and intrinsic plasticity occur concomitantly in learning process, suggesting the need of the simultaneous implementation. Here, we report a neurosynaptic device that mimics synaptic and intrinsic plasticity concomitantly in a single cell. Threshold switch and phase change memory are merged in threshold switch-phase change memory device. Neuronal intrinsic plasticity is demonstrated based on bottom threshold switch layer, which resembles the modulation of firing frequency in biological neuron. Synaptic plasticity is also introduced through the nonvolatile switching of top phase change layer. Intrinsic and synaptic plasticity are simultaneously emulated in a single cell to establish the positive feedback between them. A positive feedback learning loop which mimics the retraining process in biological system is implemented in threshold switch-phase change memory array for accelerated training. Synaptic plasticity and neuronal intrinsic plasticity are both involved in the learning process of hardware artificial neural network. Here, Lee et al. integrate a threshold switch and a phase change memory in a single device, which emulates biological synaptic and intrinsic plasticity simultaneously. | - |
dc.language | English | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse | - |
dc.type | Article | - |
dc.identifier.wosid | 000798347800025 | - |
dc.identifier.scopusid | 2-s2.0-85130349387 | - |
dc.type.rims | ART | - |
dc.citation.volume | 13 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | NATURE COMMUNICATIONS | - |
dc.identifier.doi | 10.1038/s41467-022-30432-2 | - |
dc.contributor.localauthor | Lee, Keon Jae | - |
dc.contributor.nonIdAuthor | Sung, Sang Hyun | - |
dc.contributor.nonIdAuthor | Kim, Tae Jin | - |
dc.contributor.nonIdAuthor | Shin, Hyera | - |
dc.contributor.nonIdAuthor | Im, Tae Hong | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | PHASE-CHANGE MEMORY | - |
dc.subject.keywordPlus | MEMBRANE CAPACITANCE | - |
dc.subject.keywordPlus | MECHANISM | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | DYNAMICS | - |
dc.subject.keywordPlus | RULE | - |
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