Robert Legenstein - Publications

Affiliations: 
Institute for Adaptive and Neural Computation University of Edinburgh, Edinburgh, Scotland, United Kingdom 

35 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2022 Petschenig H, Bisio M, Maschietto M, Leparulo A, Legenstein R, Vassanelli S. Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks. Frontiers in Neuroscience. 16: 838054. PMID 35495034 DOI: 10.3389/fnins.2022.838054  0.426
2022 Korcsak-Gorzo A, Müller MG, Baumbach A, Leng L, Breitwieser OJ, van Albada SJ, Senn W, Meier K, Legenstein R, Petrovici MA. Cortical oscillations support sampling-based computations in spiking neural networks. Plos Computational Biology. 18: e1009753. PMID 35324886 DOI: 10.1371/journal.pcbi.1009753  0.307
2021 Acharya J, Basu A, Legenstein R, Limbacher T, Poirazi P, Wu X. Dendritic Computing: Branching Deeper into Machine Learning. Neuroscience. PMID 34656706 DOI: 10.1016/j.neuroscience.2021.10.001  0.522
2021 Salaj D, Subramoney A, Kraisnikovic C, Bellec G, Legenstein R, Maass W. Spike frequency adaptation supports network computations on temporally dispersed information. Elife. 10. PMID 34310281 DOI: 10.7554/eLife.65459  0.406
2020 Limbacher T, Legenstein R. Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring. Frontiers in Computational Neuroscience. 14: 57. PMID 32848681 DOI: 10.3389/fncom.2020.00057  0.503
2020 Bellec G, Scherr F, Subramoney A, Hajek E, Salaj D, Legenstein R, Maass W. A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications. 11: 3625. PMID 32681001 DOI: 10.1038/s41467-020-17236-y  0.613
2020 Müller MG, Papadimitriou CH, Maass W, Legenstein R. A model for structured information representation in neural networks of the brain. Eneuro. PMID 32381648 DOI: 10.1523/ENEURO.0533-19.2020  0.364
2019 Kaiser J, Hoff M, Konle A, Vasquez Tieck JC, Kappel D, Reichard D, Subramoney A, Legenstein R, Roennau A, Maass W, Dillmann R. Embodied Synaptic Plasticity With Online Reinforcement Learning. Frontiers in Neurorobotics. 13: 81. PMID 31632262 DOI: 10.3389/fnbot.2019.00081  0.494
2019 Pokorny C, Ison MJ, Rao A, Legenstein R, Papadimitriou C, Maass W. STDP Forms Associations between Memory Traces in Networks of Spiking Neurons. Cerebral Cortex (New York, N.Y. : 1991). PMID 31403679 DOI: 10.1093/Cercor/Bhz140  0.329
2019 Yan Y, Kappel D, Neumaerker F, Partzsch J, Vogginger B, Hoeppner S, Furber S, Maass W, Legenstein R, Mayr C. Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. Ieee Transactions On Biomedical Circuits and Systems. PMID 30932847 DOI: 10.1109/Tbcas.2019.2906401  0.357
2018 Kappel D, Legenstein R, Habenschuss S, Hsieh M, Maass W. A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. Eneuro. 5. PMID 29696150 DOI: 10.1523/ENEURO.0301-17.2018  0.567
2017 Jonke Z, Legenstein R, Habenschuss S, Maass W. Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. PMID 28760861 DOI: 10.1523/JNEUROSCI.2078-16.2017  0.398
2016 Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nature Communications. 7: 12611. PMID 27681181 DOI: 10.1038/Ncomms12611  0.588
2015 Kappel D, Habenschuss S, Legenstein R, Maass W. Network Plasticity as Bayesian Inference. Plos Computational Biology. 11: e1004485. PMID 26545099 DOI: 10.1371/journal.pcbi.1004485  0.645
2015 Bill J, Buesing L, Habenschuss S, Nessler B, Maass W, Legenstein R. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition. Plos One. 10: e0134356. PMID 26284370 DOI: 10.1371/Journal.Pone.0134356  0.6
2015 Kappel D, Habenschuss S, Legenstein R, Maass W. Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring Advances in Neural Information Processing Systems. 2015: 370-378.  0.609
2014 Bill J, Legenstein R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Frontiers in Neuroscience. 8: 412. PMID 25565943 DOI: 10.3389/fnins.2014.00412  0.592
2014 Legenstein R, Maass W. Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment. Plos Computational Biology. 10: e1003859. PMID 25340749 DOI: 10.1371/journal.pcbi.1003859  0.419
2014 Hoerzer GM, Legenstein R, Maass W. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. Cerebral Cortex (New York, N.Y. : 1991). 24: 677-90. PMID 23146969 DOI: 10.1093/cercor/bhs348  0.487
2013 Indiveri G, Linares-Barranco B, Legenstein R, Deligeorgis G, Prodromakis T. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology. 24: 384010. PMID 23999381 DOI: 10.1088/0957-4484/24/38/384010  0.387
2011 Legenstein R, Maass W. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 31: 10787-802. PMID 21795531 DOI: 10.1523/JNEUROSCI.5684-10.2011  0.429
2010 Legenstein R, Wilbert N, Wiskott L. Reinforcement learning on slow features of high-dimensional input streams. Plos Computational Biology. 6. PMID 20808883 DOI: 10.1371/journal.pcbi.1000894  0.468
2010 Legenstein R, Chase SM, Schwartz AB, Maass W. A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 30: 8400-10. PMID 20573887 DOI: 10.1523/JNEUROSCI.4284-09.2010  0.455
2010 Büsing L, Schrauwen B, Legenstein R. Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons. Neural Computation. 22: 1272-311. PMID 20028227 DOI: 10.1162/neco.2009.01-09-947  0.436
2009 Legenstein R, Chase SM, Schwartz AB, Maass W. Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning. Advances in Neural Information Processing Systems. 2009: 1105-1113. PMID 25284966  0.411
2009 Klampfl S, Legenstein R, Maass W. Spiking neurons can learn to solve information bottleneck problems and extract independent components. Neural Computation. 21: 911-59. PMID 19018708 DOI: 10.1162/Neco.2008.01-07-432  0.404
2009 Wilbert N, Legenstein R, Franzius M, Wiskott L. Reinforcement learning on complex visual stimuli Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P90  0.389
2009 Legenstein R, Pecevski D, Maass W. Theoretical analysis of learning with reward-modulated spike-timing- dependent plasticity Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 0.441
2009 Schrauwen B, Büsing L, Legenstein R. On computational power and the order-chaos phase transition in Reservoir Computing Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1425-1432.  0.349
2008 Legenstein R, Pecevski D, Maass W. A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. Plos Computational Biology. 4: e1000180. PMID 18846203 DOI: 10.1371/journal.pcbi.1000180  0.581
2008 Legenstein R, Maass W. On the classification capability of sign-constrained perceptrons. Neural Computation. 20: 288-309. PMID 18045010 DOI: 10.1162/neco.2008.20.1.288  0.487
2007 Legenstein R, Maass W. Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks : the Official Journal of the International Neural Network Society. 20: 323-34. PMID 17517489 DOI: 10.1016/j.neunet.2007.04.017  0.317
2005 Legenstein R, Naeger C, Maass W. What can a neuron learn with spike-timing-dependent plasticity? Neural Computation. 17: 2337-82. PMID 16156932 DOI: 10.1162/0899766054796888  0.451
2005 Legenstein R, Maass W. A criterion for the convergence of learning with spike timing dependent plasticity Advances in Neural Information Processing Systems. 762-770.  0.454
2003 Legenstein R, Markram H, Maass W. Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons. Reviews in the Neurosciences. 14: 5-19. PMID 12929914 DOI: 10.1515/Revneuro.2003.14.1-2.5  0.321
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