Full metadata record
DC Field | Value | Language |
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dc.citation.endPage | 169 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 157 | - |
dc.citation.title | EXPERIMENTAL NEUROBIOLOGY | - |
dc.citation.volume | 32 | - |
dc.contributor.author | Kim, Jaehun | - |
dc.contributor.author | Choi, Young In | - |
dc.contributor.author | Sohn, Jeong-woo | - |
dc.contributor.author | Kim, Sung-Phil | - |
dc.contributor.author | Jung, Sung Jun | - |
dc.date.accessioned | 2023-12-21T12:36:29Z | - |
dc.date.available | 2023-12-21T12:36:29Z | - |
dc.date.created | 2023-09-01 | - |
dc.date.issued | 2023-06 | - |
dc.description.abstract | To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns. We also search for optimal parameter values for the proposed BNM to describe firing patterns of biological SA-I afferent neurons in response to sustained pressure longer than 1-second. We obtain the firing data of SA-I afferent neurons for six different mechanical pressure ranging from 0.1 mN to 300 mN from the ex-vivo experiment on SA-I afferent neurons in rodents. Upon finding the optimal parameters, we generate spike trains using the proposed BNM and compare the resulting spike trains to those of biological SA-I afferent neurons using the spike distance metrics. We verify that the proposed BNM can generate spike trains showing long-term adaptation, which is not achievable by other conventional models. Our new model may offer an essential function to artificial tactile sensing technology to perceive sustained mechanical touch. | - |
dc.identifier.bibliographicCitation | EXPERIMENTAL NEUROBIOLOGY, v.32, no.3, pp.157 - 169 | - |
dc.identifier.doi | 10.5607/en23005 | - |
dc.identifier.issn | 1226-2560 | - |
dc.identifier.scopusid | 2-s2.0-85165231188 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65331 | - |
dc.identifier.wosid | 001041947700004 | - |
dc.language | 영어 | - |
dc.publisher | KOREAN SOC BRAIN & NEURAL SCIENCE, KOREAN SOC NEURODEGENERATIVE DISEASE | - |
dc.title | Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Medicine, Research & Experimental; Neurosciences | - |
dc.relation.journalResearchArea | Research & Experimental Medicine; Neurosciences & Neurology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Touch | - |
dc.subject.keywordAuthor | Physiological adaptation | - |
dc.subject.keywordAuthor | Afferent neuron | - |
dc.subject.keywordAuthor | Neurological models | - |
dc.subject.keywordAuthor | Computer simulation | - |
dc.subject.keywordPlus | ELECTRONIC SKIN | - |
dc.subject.keywordPlus | TACTILE | - |
dc.subject.keywordPlus | SENSORS | - |
dc.subject.keywordPlus | MECHANORECEPTORS | - |
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