Subthreshold stimulus encoding on a stochastic scale-free neuronal network

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dc.contributor.authorYilmaz, E.
dc.contributor.authorÖzer, M.
dc.contributor.authorSen, B.
dc.date.accessioned2024-09-29T16:20:44Z
dc.date.available2024-09-29T16:20:44Z
dc.date.issued2010
dc.departmentKarabük Üniversitesien_US
dc.description18th IEEE Signal Processing and Communications Applications Conference, SIU 2010 -- 22 April 2010 through 24 April 2010 -- Diyarbakir -- 83388en_US
dc.description.abstractRandom networks with complex topology arise in many different fields of science. Recently, it has been shown that existing network models fail to incorporate two common features of real networks in nature: First, real networks are open and continuously grow by addition of new elements, and second, a new element connects preferentially to an element that already has a large number of connections. Therefore, a new network model, called a scale-free (SF) network, has been proposed based on these two features. In this study, we study the subthreshold periodic stimulus encoding on a stochastic SF neuronal network based on the collective firing regularity. The network consists of identical Hodgkin-Huxley (HH) neurons. We show that the collective firing (spiking) regularity becomes maximal at a given stimulus frequency, corresponding to the frequency of the subthreshold oscillations of HH neurons. We also show that this best regularity can be obtained if the coupling strength and average degree of connectivity have their optimal values. ©2010 IEEE.en_US
dc.identifier.doi10.1109/SIU.2010.5652536
dc.identifier.endpage648en_US
dc.identifier.isbn978-142449671-6
dc.identifier.scopus2-s2.0-78651461761en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage645en_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2010.5652536
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9284
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.relation.ispartofSIU 2010 - IEEE 18th Signal Processing and Communications Applications Conferenceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEncoding (symbols)en_US
dc.subjectSignal encodingen_US
dc.subjectSignal processingen_US
dc.subjectStochastic systemsen_US
dc.subjectAverage degreeen_US
dc.subjectCommon featuresen_US
dc.subjectComplex topologyen_US
dc.subjectCoupling strengthsen_US
dc.subjectHodgkin-Huxley neuronen_US
dc.subjectNetwork modelsen_US
dc.subjectNeuronal networksen_US
dc.subjectOptimal valuesen_US
dc.subjectPeriodic stimulien_US
dc.subjectRandom networken_US
dc.subjectReal networksen_US
dc.subjectScale free networksen_US
dc.subjectScale-freeen_US
dc.subjectStimulus frequencyen_US
dc.subjectSubthresholden_US
dc.subjectNeural networksen_US
dc.titleSubthreshold stimulus encoding on a stochastic scale-free neuronal networken_US
dc.title.alternativeStokastik ölçeksiz nöral a?da eşik-alti uyartimin kodlanmasien_US
dc.typeConference Objecten_US

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