Researcher in medical physics (multi-ion beam therapy for cancer) at DKFZ Heidelberg (German Cancer Research Center). M.Sc. in Biomedical Engineering from ETH Zurich and B.Sc. in Physics from University of Sarajevo.
Polje Istraživanja: Medical physics
Researcher in medical physics (multi-ion beam therapy for cancer) at DKFZ Heidelberg (German Cancer Research Center), previously researcher at IST Austria (various projects in physics), with Masters in Biomedical Engineering from ETH Zurich and Bachelors in Physics from University of Sarajevo.
Previously competitor at International Physsics Olympiad (bronze medal in 2012) and since 2013 mentor/leader for Physics Olympic teams of Bosnia and Herzegovina, lecturer and organizer of ca. 10 physics camps.
Founding member of Association for Advancement of Science and Technology (ANNT) which gathers young researchers and students with aim to contribute to betterment of science and technology in Bosnia and Herzegovina.
Neurons in the brain are wired into adaptive networks that exhibit collective dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches. Although existing models account for oscillations and avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically or intractable to infer from data rigorously. Here we propose a feedback-driven Ising-like class of neural networks that captures avalanches and oscillations simultaneously and quantitatively. In the simplest yet fully microscopic model version, we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model’s two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations to collective behaviors of extreme events and neuronal avalanches. Importantly, the inferred parameters indicate that the co-existence of scale-specific (oscillations) and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations. The study shows that scale-specific oscillations and scale-free neuronal avalanches in resting brains co-exist in the simplest model of an adaptive neural network close to a non-equilibrium critical point at the onset of self-sustained oscillations.
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