Robert Legenstein

Institute for Adaptive and Neural Computation University of Edinburgh, Edinburgh, Scotland, United Kingdom 
"Robert Legenstein"
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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
Jonke Z, Legenstein R, Habenschuss S, et al. (2017) Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience
Serb A, Bill J, Khiat A, et al. (2016) Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nature Communications. 7: 12611
Kappel D, Habenschuss S, Legenstein R, et al. (2015) Network Plasticity as Bayesian Inference. Plos Computational Biology. 11: e1004485
Bill J, Buesing L, Habenschuss S, et al. (2015) Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition. Plos One. 10: e0134356
Legenstein R. (2015) Computer science: Nanoscale connections for brain-like circuits. Nature. 521: 37-8
Kappel D, Habenschuss S, Legenstein R, et al. (2015) Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring Advances in Neural Information Processing Systems. 2015: 370-378
Bill J, Legenstein R. (2014) A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Frontiers in Neuroscience. 8: 412
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