Robert Legenstein

Institute for Adaptive and Neural Computation University of Edinburgh, Edinburgh, Scotland, United Kingdom 
"Robert Legenstein"
Mean distance: 106866


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Johannes Bill grad student


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Rui Ponte Costa collaborator 2011-2013 Edinburgh
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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
Verbist C, Müller MG, Mansvelder HD, et al. (2020) The location of the axon initial segment affects the bandwidth of spike initiation dynamics. Plos Computational Biology. 16: e1008087
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
Kaiser J, Hoff M, Konle A, et al. (2019) Embodied Synaptic Plasticity With Online Reinforcement Learning. Frontiers in Neurorobotics. 13: 81
Pokorny C, Ison MJ, Rao A, et al. (2019) STDP Forms Associations between Memory Traces in Networks of Spiking Neurons. Cerebral Cortex (New York, N.Y. : 1991)
Yan Y, Kappel D, Neumaerker F, et al. (2019) Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. Ieee Transactions On Biomedical Circuits and Systems
Liu C, Bellec G, Vogginger B, et al. (2018) Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype. Frontiers in Neuroscience. 12: 840
Kappel D, Legenstein R, Habenschuss S, et al. (2018) A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. Eneuro. 5
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