Learning and detecting emergent behavior in networks of cardiac myocytes
Abstract. We address the problem of specifying and detecting emer- gent 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 hy- brid 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 imple- mented 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 eectiveness,of our ap- proach by applying Emerald,to the scalar electrical fields produced by our CellExcite simulator.