@INPROCEEDINGS{GBCESW08,
  title = {{Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes}},
  author = {Grosu, Radu and Bartocci, Ezio and Corradini, Flavio and Entcheva, Emilia and Smolka, Scott A. and Wasilewska, Anita },
  booktitle = {In the proceedings of the 11th International Conference on Hybrid Systems: Computation and Control (HSCC'08), St. Louis, USA, April, 2008},
  pages = {229-243},
  abstract = {We address the problem of specifying and detecting emergent behavior in networks
      of cardiac myocytes, spiral electric waves in particular, a precursor to atrial
      and ventricular fibrillation. To solve this 
      problem we: (1) Apply discrete mode-abstraction to the cycle-linear hybrid
      automata (CLHA) we have recently developed for modeling the 
      behavior of myocyte networks; (2) Introduce the new concept of
      spatial-superposition of CLHA modes; (3) Develop a new spatial logic, based on
      spatial-superposition, for specifying emergent behavior; (4) Devise a new
      method for learning the formulae of this logic from the spatial patterns under
      investigation; and (5) Apply bounded model checking to detect (within
      milliseconds) the onset of spiral waves. We have implemented our methodology as
      the Emerald tool-suite, a component of our EHA framework for specification,
      simulation, analysis and control of excitable hybrid automata. We illustrate
      the effectiveness of our approach by applying Emerald to the scalar electrical
      fields produced by our CellExcite simulator. 
      },
  publisher = {Springer},
  series = {Lectures Notes in Computer Science},
  volume = {4981},
  year = {2008},
}