@inproceedings{0366c0d54ea84fb3ba148ba5289b74ba,

title = "Modeling complexity of physiological time series in-silico",

abstract = "A free-running physiological system produces time series with complexity which has been correlated to the robustness and health of the system. The essential tool to study the link between the structure of the system and the complexity of the series it produces is a mathematical model that is capable of reproducing the statistical signatures of a physiological time series. We construct a model based on the neural structure of the hippocampus that reproduces detrended fluctuations and multiscale entropy complexity signatures of physiological time series. We study the dependence of these signatures on the length of the series and on the initial data.",

keywords = "Complexity, Neural networks, Physiological time series",

author = "Jesse Berwald and Tom{\'a}{\v s} Gedeon and Konstantin Mischaikow",

year = "2010",

language = "English (US)",

isbn = "9789896740184",

series = "BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings",

pages = "61--67",

booktitle = "BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing",

note = "3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 ; Conference date: 20-01-2010 Through 23-01-2010",

}