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An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

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dc.contributor.authorSinha, Animesh-
dc.contributor.authorLee, Jihun-
dc.contributor.authorKim, Junho-
dc.contributor.authorSo, Hongyun-
dc.date.accessioned2026-03-11T07:00:11Z-
dc.date.available2026-03-11T07:00:11Z-
dc.date.issued2024-10-
dc.identifier.issn2051-6347-
dc.identifier.issn2051-6355-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211196-
dc.description.abstractIn the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.-
dc.format.extent28-
dc.language영어-
dc.language.isoENG-
dc.publisherRoyal Society of Chemistry-
dc.titleAn evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1039/d4mh00522h-
dc.identifier.scopusid2-s2.0-85201091756-
dc.identifier.wosid001285972700001-
dc.identifier.bibliographicCitationMaterials Horizons, v.11, no.21, pp 5181 - 5208-
dc.citation.titleMaterials Horizons-
dc.citation.volume11-
dc.citation.number21-
dc.citation.startPage5181-
dc.citation.endPage5208-
dc.type.docTypeReview; Early Access-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusPEROVSKITE SOLAR-CELLS-
dc.subject.keywordPlusARTIFICIAL NEURAL-NETWORKS-
dc.subject.keywordPlusELECTRICAL SYNAPSES-
dc.subject.keywordPlusSEIZURE PREDICTION-
dc.subject.keywordPlusCHEMICAL SYNAPSES-
dc.subject.keywordPlusBUTTERFLY WINGS-
dc.subject.keywordPlusQUANTUM-DOTS-
dc.subject.keywordPlusLOW-VOLTAGE-
dc.subject.keywordPlusBIG DATA-
dc.subject.keywordPlusBRAIN-
dc.identifier.urlhttps://pubs.rsc.org/en/content/articlelanding/2024/mh/d4mh00522h-
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