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dc.contributor.authorGençağa, Deniz
dc.contributor.authorŞengül Ayan, Sevgi
dc.contributor.authorFarnoudkia, Hajar
dc.contributor.authorOkuyucu, Serdar
dc.date.accessioned2021-05-25T10:14:16Z
dc.date.available2021-05-25T10:14:16Z
dc.date.issued2020
dc.identifier.citationGençağa, D., Şengül Ayan, S., Farnoudkia, H. & Okuyucu, S. (2020). Statistical approaches for the analysis of dependency among neurons under noise. MDPI, 22(4).en_US
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/20.500.12566/788
dc.description.abstractNeuronal noise is a major factor affecting the communication between coupled neurons. In this work, we propose a statistical toolset to infer the coupling between two neurons under noise. We estimate these statistical dependencies from data which are generated by a coupled Hodgkin–Huxley (HH) model with additive noise. To infer the coupling using observation data, we employ copulas and information-theoretic quantities, such as the mutual information (MI) and the transfer entropy (TE). Copulas and MI between two variables are symmetric quantities, whereas TE is asymmetric. We demonstrate the performances of copulas and MI as functions of different noise levels and show that they are effective in the identification of the interactions due to coupling and noise. Moreover, we analyze the inference of TE values between neurons as a function of noise and conclude that TE is an effective tool for finding out the direction of coupling between neurons under the effects of noise.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTransfer entropyen_US
dc.subjectTransfer entropisitr_TR
dc.subjectMutual informationen_US
dc.subjectKarşılıklı bilgitr_TR
dc.subjectInformation theoryen_US
dc.subjectBilgi teorisitr_TR
dc.subjectCopulasen_US
dc.subjectBirleştirici yapıtr_TR
dc.subjectHodgkin-Huxley modelen_US
dc.subjectHodgkin-Huxley modelitr_TR
dc.titleStatistical approaches for the analysis of dependency among neurons under noiseen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.wosWOS:000537222600026
dc.identifier.scopus2-s2.0-85086665228
dc.identifier.volume22
dc.identifier.issue4
dc.contributor.orcid0000-0003-0065-172X [Gençağa, Deniz]
dc.contributor.orcid0000-0003-0083-4446 [Şengül Ayan, Sevgi]
dc.contributor.orcid0000-0001-5124-2745 [Okuyucu, Serdar]
dc.contributor.abuauthorGençağa, Deniz
dc.contributor.abuauthorŞengül Ayan, Sevgi
dc.contributor.abuauthorOkuyucu, Serdar
dc.contributor.yokid270826 [Gençağa, Deniz]
dc.contributor.yokid236492 [Şengül Ayan, Sevgi]
dc.contributor.yokid239781 [Okuyucu, Serdar]
dc.contributor.ScopusAuthorID15070644500 [Gençağa, Deniz]
dc.contributor.ScopusAuthorID57217184680 [Şengül Ayan, Sevgi]
dc.contributor.ScopusAuthorID57201466194 [Okuyucu, Serdar]
dc.identifier.PubMedID33286161
dc.identifier.doi10.3390/e22040387


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