PyNEST: a convenient interface to the NEST simulator JM Eppler, M Helias, E Muller, M Diesmann, MO Gewaltig Frontiers in neuroinformatics 2, 363, 2009 | 289 | 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 | 216 | 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, 317068, 2018 | 142 | 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 | 142 | 2014 |
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 | 124 | 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, 43-60, 2010 | 118 | 2010 |
Optimal sequence memory in driven random networks J Schuecker, S Goedeke, M Helias Physical Review X 8 (4), 041029, 2018 | 104 | 2018 |
Statistical field theory for neural networks M Helias, D Dahmen Springer, 2020 | 92 | 2020 |
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 | 90 | 2019 |
A unified view on weakly correlated recurrent networks D Grytskyy, T Tetzlaff, M Diesmann, M Helias Frontiers in computational neuroscience 7, 131, 2013 | 87 | 2013 |
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 | 87 | 2012 |
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 | 81 | 2018 |
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 | 69 | 2010 |
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 | 64 | 2015 |
Echoes in correlated neural systems M Helias, T Tetzlaff, M Diesmann New journal of physics 15 (2), 023002, 2013 | 64 | 2013 |
Structural plasticity controlled by calcium based correlation detection M Helias, S Rotter, MO Gewaltig, M Diesmann Frontiers in Computational Neuroscience 2, 307, 2008 | 55 | 2008 |
Identifying anatomical origins of coexisting oscillations in the cortical microcircuit H Bos, M Diesmann, M Helias PLoS computational biology 12 (10), e1005132, 2016 | 51 | 2016 |
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 | 51 | 2012 |
Correlated fluctuations in strongly coupled binary networks beyond equilibrium D Dahmen, H Bos, M Helias Physical Review X 6 (3), 031024, 2016 | 46 | 2016 |
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 | 46 | 2006 |