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Automated Inference of Excitable Cell Models as Hybrid Automata
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this paper, we explore from an experimental point of view the possibilities and limitations of the new HYCGE learning algorithm for hybrid automata. As an example of a practical application, we study the algorithm’s performance on learning the behaviour of the action potential in excitable cells, specifically the Hodgkin-Huxley model of a squid giant axon, the Luo-Rudy model of a guinea pig ventricular cell, and the Entcheva model of a neonatal rat ventricular cell. The validity and accuracy of the algorithm is also visualized through graphical means.

Abstract [sv]

I denna uppsats undersöker vi från en experimentell synvinkel möjligheter och begränsningar i den nya inlärningsalgoritmen HYCGE för hybridautomater. Som ett exempel på en praktisk tillämpning, studerar vi algoritmens förmåga att lära sig aktionspotentialens beteende i retbara celler, specifikt Hodgkin-Huxleymodellen av en bläckfisks jätteaxon, Luo-Rudymodellen av en ventrikulärcell i marsvin, och Entchevas modell av en ventrikulär cell i nyfödd råtta .Giltigheten och noggrannheten hos algoritmen visualiseras även genom grafiskamedel.

Place, publisher, year, edition, pages
2013. , 29 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-154065OAI: diva2:755070
Educational program
Master of Science in Engineering -Engineering Physics
Available from: 2014-10-13 Created: 2014-10-13 Last updated: 2014-10-13Bibliographically approved

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Rasmus Ansin & Didrik Lundberg kandidatexam(697 kB)26 downloads
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