Moritz Helias
Affiliations: | Institute for Neuroscience and Medicine 6 | Forschungszentrum Jülich |
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| Layer M, Senk J, Essink S, et al. (2022) NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models. Frontiers in Neuroinformatics. 16: 835657 |
| Stapmanns J, Kühn T, Dahmen D, et al. (2022) Erratum: Self-consistent formulations for stochastic nonlinear neuronal dynamics [Phys. Rev. E 101, 042124 (2020)]. Physical Review. E. 105: 059901 |
| Dahmen D, Layer M, Deutz L, et al. (2022) Global organization of neuronal activity only requires unstructured local connectivity. Elife. 11 |
| van Meegen A, Kühn T, Helias M. (2021) Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions. Physical Review Letters. 127: 158302 |
| Stapmanns J, Hahne J, Helias M, et al. (2021) Event-Based Update of Synapses in Voltage-Based Learning Rules. Frontiers in Neuroinformatics. 15: 609147 |
| Gilson M, Dahmen D, Moreno-Bote R, et al. (2020) The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks. Plos Computational Biology. 16: e1008127 |
| Jordan J, Helias M, Diesmann M, et al. (2020) Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Frontiers in Neuroinformatics. 14: 12 |
| Stapmanns J, Kühn T, Dahmen D, et al. (2020) Self-consistent formulations for stochastic nonlinear neuronal dynamics. Physical Review. E. 101: 042124 |
| Dahmen D, Gilson M, Helias M. (2020) Capacity of the covariance perceptron Journal of Physics A. 53: 354002 |
| Helias M, Dahmen D. (2020) Statistical Field Theory for Neural Networks Arxiv: Disordered Systems and Neural Networks |