Learning cycle-linear hybrid automata for excitable cells

R. Grosu, S. Mitra, P. Ye, E. Entcheva, I. V. Ramakrishnan, S. A. Smolka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We show how to automatically learn the class of Hybrid Automata called Cycle-Linear Hybrid Automata (CLHA) in order to model the behavior of excitable cells. Such cells, whose main purpose is to amplify and propagate an electrical signal known as the action potential (AP), serve as the "biologic transistors" of living organisms. The learning algorithm we propose comprises the following three phases: (1) Geometric analysis of the APs in the training set is used to identify, for each AP, the modes and switching logic of the corresponding Linear Hybrid Automata. (2) For each mode, the modified Prony's method is used to learn the coefficients of the associated linear flows. (3) The modified Prony's method is used again to learn the functions that adjust, on a per-cycle basis, the mode dynamics and switching logic of the Linear Hybrid Automata obtained in the first two phases. Our results show that the learned CLHA is able to successfully capture AP morphology and other important excitable-cell properties, such as refractoriness and restitution, up to a prescribed approximation error. Our approach is fully implemented in MATLAB and, to the best of our knowledge, provides the most accurate approximation model for ECs to date.

Original languageEnglish (US)
Title of host publicationHybrid Systems
Subtitle of host publicationComputation and Control - 10th International Conference, HSCC 2007, Proceedings
PublisherSpringer
Pages245-258
Number of pages14
ISBN (Print)9783540714927
DOIs
StatePublished - Jan 1 2007
Externally publishedYes
Event10th International Conference on Hybrid Systems: Computation and Control, HSCC 2007 - Pisa, Italy
Duration: Apr 3 2007Apr 5 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4416 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Hybrid Systems: Computation and Control, HSCC 2007
CountryItaly
CityPisa
Period4/3/074/5/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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