Gradient method of learning for stochastic kinetic model of neuron

dc.contributor.authorŚwietlicka, Aleksandra
dc.contributor.authorGugała, Karol
dc.contributor.authorKarón, Igor
dc.contributor.authorKolanowski, Krzysztof
dc.contributor.authorMajchrzycki, Mateusz
dc.contributor.authorRybarczyk, Andrzej
dc.date.accessioned2014-07-14T07:23:21Z
dc.date.available2014-07-14T07:23:21Z
dc.date.issued2013
dc.description.abstractIn this paper we are focusing on the kinetic extension [4] of classic model of Hodgkin and Huxley [2]. We are showing the descent gradient method used in the learning process of neuron, which is described with stochastic kinetic model. In comparison with [1] we use only 3 weights instead of 9: gNa; gK and gL: We show that this model behaves equally accurate as the model of Hodgkin and Huxley with slighter system description.en
dc.format2 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationISTET 2013: International Symposiumon Theoretical Electrical Engineering: 24th – 26th June 2013, Pilsen, Czech Republic, p. III-17-III-18.en
dc.identifier.isbn978-80-261-0246-5
dc.identifier.urihttp://hdl.handle.net/11025/11489
dc.language.isoenen
dc.publisherUniversity of West Bohemiaen
dc.relation.ispartofseriesISTET: International Symposium on Theoretical Electrical Engineeringen
dc.rights© University of West Bohemiaen
dc.rights.accessopenAccessen
dc.subjectstochastický kinetický model neuronucs
dc.subjectgradientní metoda učenícs
dc.subjectHodgin-Huxleyho modelcs
dc.subject.translatedstochastic kinetic model of neuronen
dc.subject.translatedgradient method of learningen
dc.subject.translatedHodgkin-Huxley modelen
dc.titleGradient method of learning for stochastic kinetic model of neuronen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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