Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes

DOI10.1007/978-3-540-78929-1_17zbMath1144.92313OpenAlexW2154593067MaRDI QIDQ3523110

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Publication date: 2 September 2008

Published in: Hybrid Systems: Computation and Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-78929-1_17

Mathematics Subject Classification ID

Formal languages and automata (68Q45) Medical applications (general) (92C50) Biophysics (92C05) Specification and verification (program logics, model checking, etc.) (68Q60) Computational methods for problems pertaining to biology (92-08)

Related Items (7)

StonyCam: A Formal Framework for Modeling, Analyzing and Regulating Cardiac Myocytes ⋮ Back-and-forth in space: on logics and bisimilarity in closure spaces ⋮ Quantitative verification of implantable cardiac pacemakers over hybrid heart models ⋮ Modeling and simulation of cardiac tissue using hybrid I/O automata ⋮ Quadtrees as an Abstract Domain ⋮ Adaptive testing for specification coverage and refinement in CPS models ⋮ System design of stochastic models using robustness of temporal properties

Cites Work

• Unnamed Item
• A spatial logic for concurrency. II
• A spatial logic for concurrency. I
• Coordinated control of networked vehicles: an autonomous underwater system
• Learning Cycle-Linear Hybrid Automata for Excitable Cells
• Reasoning About Space: The Modal Way
• Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

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