| Learning interacting theories from data C Merger, A René, K Fischer, P Bouss, S Nestler, D Dahmen, ... Physical Review X 13 (4), 041033, 2023 | 6 | 2023 |
| Unfolding recurrence by Green’s functions for optimized reservoir computing S Nestler, C Keup, D Dahmen, M Gilson, H Rauhut, M Helias Advances in Neural Information Processing Systems 33, 17380-17390, 2020 | 6 | 2020 |
| Path classification by stochastic linear recurrent neural networks Y Boutaib, W Bartolomaeus, S Nestler, H Rauhut Advances in continuous and discrete models 2022 (1), 13, 2022 | 3 | 2022 |
| Statistical temporal pattern extraction by neuronal architecture S Nestler, M Helias, M Gilson Physical Review Research 5 (3), 033177, 2023 | 1 | 2023 |
| A statistical perspective on learning of time series in neural networks S Nestler Dissertation, RWTH Aachen University, 2024, 2024 | | 2024 |
| Statistical physics, Bayesian inference and neural information processing E Grant, S Nestler, B Şimşek, S Solla arXiv preprint arXiv:2309.17006, 2023 | | 2023 |
| Neuronal architecture extracts statistical temporal patterns S Nestler, M Helias, M Gilson arXiv preprint arXiv:2301.10203, 2023 | | 2023 |
| Nonlinear dimensionality reduction with normalizing flows for analysis of electrophysiological recordings P Bouss, S Nestler, CL Merger, K Fischer, M Helias, A Rene 32nd Annual Computational Neuroscience Meeting, 2023 | | 2023 |
| Neural networks learning structure in temporal signals S Nestler, D Dahmen, M Gilson, Y Boutaib, W Bartolomaeus, C Keup, ... Seminar Talk, Barak Lab, 2023 | | 2023 |
| Neuronal extraction of statistical patterns embedded in time series S Nestler, M Helias, M Gilson Cosyne 2023, 2023 | | 2023 |
| Path classification by stochastic linear recurrent neural networks B Youness, B Wiebke, S Nestler, R Holger Advances in Difference Equations 2022 (1), 2022 | | 2022 |
| Fluctuations, correlations, chaos: dynamics and computation in recurrent networks M Helias, D Dahmen, S Nestler, A van Meegen, C Keup MILA Seminar, 2021 | | 2021 |
| Joint Optimization Of Input And Output Projections In Reservoir Computing S Nestler RWTH Aachen University, 2019 | | 2019 |
| Optimized Reservoir Computing with Stochastic Recurrent Networks S Nestler, D Dahmen, M Helias, C Keup CNS 2019 Barcelona, 2019 | | 2019 |
Moritz HeliasInst. for Neuroscience and medicine (INM-6), Jülich; Faculty of Physics, RWTH Aachen在 fz-juelich.de 的电子邮件经过验证
