Moritz Helias
Moritz Helias
Inst. for Neuroscience and medicine (INM-6), Jülich; Faculty of Physics, RWTH Aachen
Verified email at - Homepage
Cited by
Cited by
PyNEST: a convenient interface to the NEST simulator
JM Eppler, M Helias, E Muller, M Diesmann, MO Gewaltig
Frontiers in neuroinformatics 2, 363, 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
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
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
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
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
Optimal sequence memory in driven random networks
J Schuecker, S Goedeke, M Helias
Physical Review X 8 (4), 041029, 2018
Statistical field theory for neural networks
M Helias, D Dahmen
Springer, 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
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
A unified view on weakly correlated recurrent networks
D Grytskyy, T Tetzlaff, M Diesmann, M Helias
Frontiers in computational neuroscience 7, 131, 2013
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, 183-214, 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
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
Echoes in correlated neural systems
M Helias, T Tetzlaff, M Diesmann
New journal of physics 15 (2), 023002, 2013
Structural plasticity controlled by calcium based correlation detection
M Helias, S Rotter, MO Gewaltig, M Diesmann
Frontiers in Computational Neuroscience 2, 307, 2008
Identifying anatomical origins of coexisting oscillations in the cortical microcircuit
H Bos, M Diesmann, M Helias
PLoS computational biology 12 (10), e1005132, 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
Correlated fluctuations in strongly coupled binary networks beyond equilibrium
D Dahmen, H Bos, M Helias
Physical Review X 6 (3), 031024, 2016
Instantaneous non-linear processing by pulse-coupled threshold units
M Helias, M Deger, S Rotter, M Diesmann
PLoS computational biology 6 (9), e1000929, 2010
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