|Profile Research Publications CV||
Kavli Institute for Systems Neuroscience, NTNU
I am a postdoc in computational neuroscientist and machine learning at Yasser Roudi's lab at the Kavli Institute for Systems Neuroscience at NTNU since 2010, working on grid cell attractor networks in the mammalian brain and developing kinetic inverse Ising models. I am interested in brain functions and using statistical physics to understand behaviors in complex systems.
My research interests are in the crossroads of machine learning, statistical physics and theoretical neuroscience. My major motivation is to either support or falsify the presence of attractor networks in the medial entorhinal cortex (mEC)-hippocampus (HC) systems. My long term goal is to reconstruct networks from experimental neural data from these brain regions and developing the findings into working network models of brain functions of memory and spatial representation. To facilitate this research, I have interests in dynamical systems, statistical tools for inferring connectivity and understanding of learning behaviors in complex systems.
J.J. Couey, A. Witoelar, S.-J. Zhang, K. Zheng, J. Ye, B. Dunn, R. Czajkowski, M.-B. Moser, E.I. Moser, Y. Roudi and M.P. Witter. Recurrent inhibitory circuitry as a mechanism for grid formation. Nature Neuroscience 16, p. 318-328, 2013. doi:10.1038/nn.3310
A. Witoelar, G.J. de Vries, A. Ghosh, B. Hammer and M. Biehl, Window-based example selection of learning vector quantization, Neural Computation, Vol. 22, No. 11, p. 2924-2961, 2010. doi: 10.1162/NECO a 00030
A. Witoelar and M. Biehl, Phase transitions in Vector Quantization and Neural Gas, Neurocomputing 72, p. 1390-1397, 2009. doi: 10.1016/j.neucom.2008.10.023
A. Witoelar, M. Biehl, A. Ghosh and B. Hammer, Learning Dynamics of Neural Gas and Vector Quantization. Neurocomputing 71, p. 1210-1219, 2008. doi:10.1016/j.neucom.2007.11.022