| PyNEST: a convenient interface to the NEST simulator JM Eppler, M Helias, E Muller, M Diesmann, MO Gewaltig Frontiers in neuroinformatics 2, 363, 2009 | 317 | 2009 |
| Decorrelation of neural-network activity by inhibitory feedback T Tetzlaff, M Helias, GT Einevoll, M Diesmann Public Library of Science 8 (8), e1002596, 2012 | 244 | 2012 |
| Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers J Jordan, T Ippen, M Helias, I Kitayama, M Sato, J Igarashi, M Diesmann, ... Frontiers in neuroinformatics 12, 2, 2018 | 169 | 2018 |
| Spiking network simulation code for petascale computers S Kunkel, M Schmidt, JM Eppler, HE Plesser, G Masumoto, J Igarashi, ... Frontiers in neuroinformatics 8, 78, 2014 | 163 | 2014 |
| Statistical field theory for neural networks M Helias, D Dahmen Springer, 2020 | 148 | 2020 |
| Optimal sequence memory in driven random networks J Schuecker, S Goedeke, M Helias Physical Review X 8 (4), 041029, 2018 | 146 | 2018 |
| The correlation structure of local neuronal networks intrinsically results from recurrent dynamics M Helias, T Tetzlaff, M Diesmann PLoS computational biology 10 (1), e1003428, 2014 | 137 | 2014 |
| Run-time interoperability between neuronal network simulators based on the MUSIC framework M Djurfeldt, J Hjorth, JM Eppler, N Dudani, M Helias, TC Potjans, ... Neuroinformatics 8 (1), 43-60, 2010 | 134 | 2010 |
| Second type of criticality in the brain uncovers rich multiple-neuron dynamics D Dahmen, S Grün, M Diesmann, M Helias Proceedings of the National Academy of Sciences 116 (26), 13051-13060, 2019 | 133 | 2019 |
| Computational neuroscience: Mathematical and statistical perspectives RE Kass, SI Amari, K Arai, EN Brown, CO Diekman, M Diesmann, ... Annual review of statistics and its application 5 (1), 183-214, 2018 | 95 | 2018 |
| Supercomputers ready for use as discovery machines for neuroscience M Helias, S Kunkel, G Masumoto, J Igarashi, JM Eppler, S Ishii, T Fukai, ... Frontiers in neuroinformatics 6, 26, 2012 | 92 | 2012 |
| A unified view on weakly correlated recurrent networks D Grytskyy, T Tetzlaff, M Diesmann, M Helias Frontiers in computational neuroscience 7, 131, 2013 | 91 | 2013 |
| Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations SJ Van Albada, M Helias, M Diesmann PLoS computational biology 11 (9), e1004490, 2015 | 85 | 2015 |
| A general and efficient method for incorporating precise spike times in globally time-driven simulations A Hanuschkin, S Kunkel, M Helias, A Morrison, M Diesmann Frontiers in neuroinformatics 4, 113, 2010 | 78 | 2010 |
| Echoes in correlated neural systems M Helias, T Tetzlaff, M Diesmann New journal of physics 15 (2), 023002, 2013 | 64 | 2013 |
| Identifying anatomical origins of coexisting oscillations in the cortical microcircuit H Bos, M Diesmann, M Helias PLoS computational biology 12 (10), e1005132, 2016 | 57 | 2016 |
| Structural plasticity controlled by calcium based correlation detection M Helias, S Rotter, MO Gewaltig, M Diesmann Frontiers in Computational Neuroscience 2, 307, 2008 | 56 | 2008 |
| The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG M Völker, LDJ Fiederer, S Berberich, J Hammer, J Behncke, P Kršek, ... Neuroimage 173, 564-579, 2018 | 55 | 2018 |
| Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex M Deger, M Helias, S Rotter, M Diesmann Public Library of Science 8 (9), e1002689, 2012 | 55 | 2012 |
| Electro-quasistatic field simulations based on a discrete electromagnetism formulation T Steinmetz, M Helias, G Wimmer, LO Fichte, M Clemens IEEE transactions on magnetics 42 (4), 755-758, 2006 | 55 | 2006 |
Markus DiesmannDirector, IAS-6, INM-10, Jülich Research CentreVerified email at fz-juelich.de
