Robert Legenstein
Affiliations: | Institute for Adaptive and Neural Computation | University of Edinburgh, Edinburgh, Scotland, United Kingdom |
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| Subramoney A, Bellec G, Scherr F, et al. (2024) Fast learning without synaptic plasticity in spiking neural networks. Scientific Reports. 14: 8557 |
| Baronig M, Legenstein R. (2024) Context association in pyramidal neurons through local synaptic plasticity in apical dendrites. Frontiers in Neuroscience. 17: 1276706 |
| Limbacher T, Ozdenizci O, Legenstein R. (2023) Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity. Ieee Transactions On Neural Networks and Learning Systems |
| Petschenig H, Bisio M, Maschietto M, et al. (2022) Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks. Frontiers in Neuroscience. 16: 838054 |
| Korcsak-Gorzo A, Müller MG, Baumbach A, et al. (2022) Cortical oscillations support sampling-based computations in spiking neural networks. Plos Computational Biology. 18: e1009753 |
| Acharya J, Basu A, Legenstein R, et al. (2021) Dendritic Computing: Branching Deeper into Machine Learning. Neuroscience |
| Salaj D, Subramoney A, Kraisnikovic C, et al. (2021) Spike frequency adaptation supports network computations on temporally dispersed information. Elife. 10 |
| Limbacher T, Legenstein R. (2020) Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring. Frontiers in Computational Neuroscience. 14: 57 |
| Bellec G, Scherr F, Subramoney A, et al. (2020) A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications. 11: 3625 |
| Müller MG, Papadimitriou CH, Maass W, et al. (2020) A model for structured information representation in neural networks of the brain. Eneuro |