title: Measuring predictability of autonomous network transitions into bursting dynamics.
abstract: Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.
authors: Biophysics Program, University of Michigan, 930N University, Ann Arbor, Michigan, United States of America.
authors: Sima
authors: S
authors: Mofakham
authors: Biophysics Program, University of Michigan, 930N University, Ann Arbor, Michigan, United States of America; Department of Physics, University of Michigan, 450 Church St, Ann Arbor, Michigan, United States of America; The R.B. Zajonc Institute for Social Studies, Stawki 5/7, 00-183 Warsaw, Poland.
authors: Michal
authors: M
authors: Zochowski
mesh_terms: D000200:Action Potentials / Q000502:physiology; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology