Felix Schürmann

Computing Director

Felix Schürmann is adjunct professor at the Ecole Polytechnique Fédérale de Lausanne, co-director of the Blue Brain Project and involved in several research challenges of the European Human Brain Project.

He studied physics at the University of Heidelberg, Germany, supported by the German National Academic Foundation. Later, as a Fulbright Scholar, he obtained his Master’s degree (M.S.) in Physics from the State University of New York, Buffalo, USA, under the supervision of Richard Gonsalves. During these studies, he became curious about the role of different computing substrates and dedicated his master thesis to the simulation of quantum computing.

He studied for his Ph.D. at the University of Heidelberg, Germany, under the supervision of Karlheinz Meier. For his thesis he co-designed an efficient implementation of a neural network in hardware.

The Felix Schürmann Lab – Neuroscience and Computing – https://www.epfl.ch/labs/gr-fsch/


Cremonesi, F., Hager, G., Wellein, G., and Schürmann, F. Analytic performance  modelingand analysis of detailed neuron simulations. The International Journal of High Performance Computing Applications, 34(4):428–449, 2020.
DOI: 10.1177/1094342020912528

Cremonesi, F., and Schürmann, F. Understanding computational costs of cellular-level braintissue simulations through analytical performance models. Neuroinformatics, 18:407–428,2020
DOI: 10.1007/s12021-019-09451-w

Magalhães, B. R. C., Sterling, T., Hines, M., and Schürmann. F. Fully-asynchronous fully-implicit variable-order variable-timestep simulation of neural networks. In V. Krzhizhanovskaya etal. editors, Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in ComputerScience, volume 12141, pages 94–108, Cham, 2019. Springer.

Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar, P., Schürmann, F., Segev, I. An efficient analytical reduction of detailed nonlinear neuron models. Nat Commun 11, 288 (2020).
DOI: 10.1038/s41467-019-13932-6

Kumbhar, P., Hines, M., Fouriaux, J., Ovcharenko, A., King, J., Delalondre, F., and Schürmann, F. Coreneuron: An optimized compute engine for the neuron simulator. Frontiers in Neuroinformatics, 13:63, 2019. 
DOI: 10.3389/fninf.2019.00063

Einevoll, G.T., Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., de Kamps, M., Migliore, M., Ness, T.V., Plesser, H.E., Schürmann, F. The scientific case for brain simulations. Neuron. 2019.
DOI: 10.1016/j.neuron.2019.03.027

Hill SL., Wang Y., Riachi I., Schürmann F., Markram H. (2012). Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits, PNAS, Published online before print September 18, 2012,
DOI: 10.1073/pnas.1202128109

Hines, M. L., Markram, H., & Schürmann, F., (2008). Fully implicit parallel simulation of single neurons. J Comput Neurosci, 25(3), 439-448.
DOI: 10.1007/s10827-008-0087-5

Druckmann, S., Banitt, Y., Gidon, A., Schürmann, F., Markram, H., & Segev, I. (2007). A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci, 1(1), 7-18.
DOI: 10.3389/neuro.

Schürmann, F., Meier, K., & Schemmel, J. (2005). Edge of Chaos Computation in Mixed-Mode VLS – “A hard liquid”. In L. K. Saul, Y. Weiss & L. Bottou (Eds.), Advances in neural information processing systems 17 (NIPS204). Cambridge, MA: MIT Press.
Publication: 221619543