I majored in Electronics Engineering, and completed my MSc as a member of the Neuroscience Modelling and Research Group (Simmag) at Istanbul Technical University. I'm currently working for Simmag as a PhD fellow for reward-relating learning framework. We are also dealing with the robotic applications of this framework in order to show the efficiency of our computational models.
Last year, I was working as an intern at Neurorobotics and Simplification Group within Blue Brain Project (BBP) at EPFL and I will continue my research at BBP as a PhD student from this summer.
In my computational neuroscience studies, I have been working on modeling and simulating brain areas using experimental insights, from spiking neural networks to system level models. The main goal of my research is to show the efficiency of brain-inspired mathematical models. These models of cognitive processes help to understand the information processes of the brain. Information proceses of brain studies would go further by giving feedback to computational neuroscience studies and inspire the development of new tools to functionalize cognitive processes, while bringing mathematical and computational approaches together to find a way to explain the complexities of the nervous system. In order to simulate a wide range of brain regions, one has also to deal with the computational costs, one of the major challenges in the contemporary neuroscience. Nevertheless, the structure of the nervous system allows us to consider highly parallel computing. Even though the simulations have a key role in understanding the brain formations, mathematical theory still remains important. Hence, comprehending the brain formations is an open problem for both the computational and theoretical point of view. As pointed out by information processing studies in the primate brain, the formation of brain function is dynamic. My approach to understanding brain function is strongly guided by nonlinear dynamical systems, such as bifurcation theory, chaos and synchronization.