Modeling Biological Neural Circuits with Novel Deep Learning Approaches
Richard is a first-year DPhil student supervised by Mark Woolrich at the Oxford Centre for Human Brain Activity. He is interested in how computational neuroscience and machine learning can inspire each other to gain a better understanding of the brain.
Richard completed a Computer Science M.S. and a Mechatronics B.S. at the Budapest University of Technology. He was involved in dialogue modeling research for 3 years under the supervision of Gabor Recski, and he also did computer vision research at Robert Bosch.
Richard is interested in several topics. 1. building neural network models of single neurons and connecting these in biologically plausible ways. 2. Applying state-of-the-art deep learning models to the task of inferring brain network dynamics, specifically functional connectivity. 3. How different brain imaging modalities (fMRI, MEG, EEG) can be used to optimize neural models of larger brain areas directly and their application to Brain-Computer Interfaces.