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Automated Inference of Excitable Cell Models as Hybrid Automata.
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
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.

Place, publisher, year, edition, pages
2013. , 29 p.
National Category
Engineering and Technology Computer and Information Science
URN: urn:nbn:se:kth:diva-143330OAI: diva2:706235
Available from: 2015-05-28 Created: 2014-03-19 Last updated: 2015-05-28Bibliographically approved

Open Access in DiVA

Rasmus Ansin & Didrik Lundberg kandidatexjobb. Bachelor´s Thesis, teknisk fysik.(697 kB)15 downloads
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