| Year |
Citation |
Score |
| 2024 |
Subramoney A, Bellec G, Scherr F, Legenstein R, Maass W. Fast learning without synaptic plasticity in spiking neural networks. Scientific Reports. 14: 8557. PMID 38609429 DOI: 10.1038/s41598-024-55769-0 |
0.625 |
|
| 2024 |
Baronig M, Legenstein R. Context association in pyramidal neurons through local synaptic plasticity in apical dendrites. Frontiers in Neuroscience. 17: 1276706. PMID 38357522 DOI: 10.3389/fnins.2023.1276706 |
0.543 |
|
| 2023 |
Limbacher T, Ozdenizci O, Legenstein R. Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity. Ieee Transactions On Neural Networks and Learning Systems. PMID 38113154 DOI: 10.1109/TNNLS.2023.3341446 |
0.537 |
|
| 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 |
0.425 |
|
| 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 |
0.52 |
|
| 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 |
0.501 |
|
| 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 |
0.611 |
|
| 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 |
0.493 |
|
| 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 |
0.358 |
|
| 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 |
0.566 |
|
| 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 |
0.397 |
|
| 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 |
0.584 |
|
| 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 |
0.643 |
|
| 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 |
0.597 |
|
| 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. |
0.607 |
|
| 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 |
0.589 |
|
| 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 |
0.418 |
|
| 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 |
0.486 |
|
| 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 |
0.386 |
|
| 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 |
0.427 |
|
| 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 |
0.466 |
|
| 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 |
0.454 |
|
| 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 |
0.409 |
|
| 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 |
0.403 |
|
| 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 |
0.388 |
|
| 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 |
|
| 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. |
0.439 |
|
| 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 |
0.579 |
|
| 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 |
0.486 |
|
| 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 |
0.45 |
|
| 2005 |
Natschläger T, Bertschinger N, Legenstein R. At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks Advances in Neural Information Processing Systems. |
0.388 |
|
| 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. |
0.452 |
|
| 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 |
0.32 |
|
| Show low-probability matches. |