By training I am a physicist and worked in nuclear and medium energy particle physics in the groups of Professor Hinde in Canberra, Australia and Professors Beck and Thoma at the Crystal Barrel experiment in Bonn, Germany. I obtained my diploma in physics in March 2009. I then turned my back on experimental physics and started my PhD in theoretical neuroscience in the group of Wulfram Gerstner in Lausanne, Switzerland. My thesis research was on associative memories and how they can form in an unsupervised way in recurrent neural networks. In the process I got interested in homeostasis and inhibitory plasticity. Ultimately I am striving to understand how plasticity, network dynamics and homeostasis interact with each other and self organize to form non-random networks that serve a particular purpose. To do that I take a dynamical systems viewpoint to derive stability requirements that ultimately lead to stable network functions. To that end I developed a mean field framework which comprises plastic synaptic weights. To verify analytical results I am used to running large-time-scale simulations for which I am pushing the boundaries of GPU and parallel computing using close-to-hardware programming techniques. The efforts behind this are also reflected in the open source simulation software Auryn that I actively develop.
tl;dr? There is a short 3rd person version here.