Abstract: Earthquakes, hurricanes, landslides, financial crashes, and epileptic seizures are catastrophic events thought to exhibit similar self-organized behavior found in complex systems. The proposed analogy is based on statistical similarities, such as a power law distribution of event sizes, and on common structures in terms of coupled threshold oscillators of relaxation. But, beyond the analogies, what can we learn from them and how can this knowledge be used to advance each relevant discipline? Here we demonstrate a striking correspondence between seizures (SZ) and Southern California earthquakes (EQ), extending over seven statistics which exhibit remarkably robust scale-free properties: The Gutenberg-Richter distribution of event sizes, the Omori law, the inverse Omori law of foreshock rates, and the conditional distribution of inter-event times, among others. Then, based on a theoretical framework developed for EQ and using an animal model of epilepsy, we verify the prediction that increased neuronal coupling leads to synchronized SZ behavior with characteristic SZ size and time scale. Translating back from SZ to EQ, the proposed analogy, informed by the rats’ results, suggest that characteristic EQ behavior, proposed based on controversial observations, should be a genuine class of dynamics in relevant seismo-tectonic settings characterized by strong coupling and low crust heterogeneity. Taken in their totality, the SZ-EQ equivalencies explain the wide range of SZ energies, the existence of metastable epileptic states (from SOC to quasiperiodicity), memory, and their inherent self-triggering capacity. They also point to a general strategy for forecasting SZ capitalizing on the mechanism that future events are in part triggered by past events. While this mechanism is the basis of time-dependent EQ forecasts, the implications for SZ remain to be exploited. This approach may bring us closer to one of neurosciences’ grails: prediction and prevention of SZ.

Brief Biography of the speaker:
Ivan Osorio is a Professor of Neurology at the UNiversity of Kansas Medical Center and Visiting Scientist at the Dept. of Mathematics, Havey Mudd College, Claremont, Ca. He is a graduate of the Universidad del Valle School of Medicine. He applies mathematical tools and a systems approach to the study of epilepsy, a dynamical disease, with cyclic but aperiodic manifestations. The group which he leads demonstrated the feasibility and safety of seizure control using high frequency electrical currents, triggered by the automated real-time detection of non-stationary signals changes. His collaboration with Professor D. Sornette from ETHZ has led to the uncovering of dynamcial similarities between epileptic seziures in human and animals and earthquakes.