Wolfgang Maass - Publications

Affiliations: 
Technische Universität Graz, Graz, Steiermark, Austria 
Area:
computation & theory

62 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
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  0.309
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  0.42
2020 Papadimitriou CH, Vempala SS, Mitropolsky D, Collins M, Maass W. Brain computation by assemblies of neurons. Proceedings of the National Academy of Sciences of the United States of America. PMID 32518114 DOI: 10.1073/Pnas.2001893117  0.359
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  0.355
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  0.364
2019 Bohnstingl T, Scherr F, Pehle C, Meier K, Maass W. Neuromorphic Hardware Learns to Learn. Frontiers in Neuroscience. 13: 483. PMID 31178681 DOI: 10.3389/Fnins.2019.00483  0.369
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  0.339
2018 Liu C, Bellec G, Vogginger B, Kappel D, Partzsch J, Neumärker F, Höppner S, Maass W, Furber SB, Legenstein R, Mayr CG. Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype. Frontiers in Neuroscience. 12: 840. PMID 30505263 DOI: 10.3389/fnins.2018.00840  0.31
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  0.407
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  0.402
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  0.738
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  0.437
2014 Kappel D, Nessler B, Maass W. STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning. Plos Computational Biology. 10: e1003511. PMID 24675787 DOI: 10.1371/Journal.Pcbi.1003511  0.725
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  0.426
2013 Klampfl S, Maass W. Emergence of dynamic memory traces in cortical microcircuit models through STDP. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 33: 11515-29. PMID 23843522 DOI: 10.1523/Jneurosci.5044-12.2013  0.701
2013 Nessler B, Pfeiffer M, Buesing L, Maass W. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. Plos Computational Biology. 9: e1003037. PMID 23633941 DOI: 10.1371/Journal.Pcbi.1003037  0.768
2012 Hauser H, Ijspeert AJ, Füchslin RM, Pfeifer R, Maass W. The role of feedback in morphological computation with compliant bodies. Biological Cybernetics. 106: 595-613. PMID 22956025 DOI: 10.1007/S00422-012-0516-4  0.303
2012 Pfeiffer M, Hartbauer M, Lang AB, Maass W, Römer H. Probing real sensory worlds of receivers with unsupervised clustering. Plos One. 7: e37354. PMID 22701566 DOI: 10.1371/Journal.Pone.0037354  0.514
2012 Klampfl S, David SV, Yin P, Shamma SA, Maass W. A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. Journal of Neurophysiology. 108: 1366-80. PMID 22696538 DOI: 10.1152/Jn.00935.2011  0.74
2012 Hauser H, Ijspeert AJ, Füchslin RM, Pfeifer R, Maass W. Towards a theoretical foundation for morphological computation with compliant bodies. Biological Cybernetics. PMID 22290137 DOI: 10.1007/S00422-012-0471-0  0.322
2011 Pecevski D, Buesing L, Maass W. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. Plos Computational Biology. 7: e1002294. PMID 22219717 DOI: 10.1371/journal.pcbi.1002294  0.427
2011 Buesing L, Bill J, Nessler B, Maass W. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. Plos Computational Biology. 7: e1002211. PMID 22096452 DOI: 10.1371/Journal.Pcbi.1002211  0.735
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  0.411
2011 Rasch MJ, Schuch K, Logothetis NK, Maass W. Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1. Journal of Neurophysiology. 105: 757-78. PMID 21106898 DOI: 10.1152/Jn.00845.2009  0.749
2010 Bill J, Schuch K, Brüderle D, Schemmel J, Maass W, Meier K. Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity. Frontiers in Computational Neuroscience. 4: 129. PMID 21031027 DOI: 10.3389/Fncom.2010.00129  0.353
2010 Klampfl S, Maass W. A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction. Neural Computation. 22: 2979-3035. PMID 20858129 DOI: 10.1162/Neco_A_00050  0.701
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  0.41
2010 Buesing L, Maass W. A spiking neuron as information bottleneck. Neural Computation. 22: 1961-92. PMID 20337537 DOI: 10.1162/neco.2010.08-09-1084  0.447
2010 Pfeiffer M, Nessler B, Douglas RJ, Maass W. Reward-modulated Hebbian learning of decision making. Neural Computation. 22: 1399-444. PMID 20141476 DOI: 10.1162/Neco.2010.03-09-980  0.774
2009 Nikolić D, Häusler S, Singer W, Maass W. Distributed fading memory for stimulus properties in the primary visual cortex. Plos Biology. 7: e1000260. PMID 20027205 DOI: 10.1371/journal.pbio.1000260  0.39
2009 Steimer A, Maass W, Douglas R. Belief propagation in networks of spiking neurons. Neural Computation. 21: 2502-23. PMID 19548806 DOI: 10.1162/neco.2009.08-08-837  0.582
2009 Buonomano DV, Maass W. State-dependent computations: spatiotemporal processing in cortical networks. Nature Reviews. Neuroscience. 10: 113-25. PMID 19145235 DOI: 10.1038/nrn2558  0.387
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  0.755
2009 Klampfl S, Maass W. Replacing supervised classification learning by slow feature analysis in spiking neural networks Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 988-996.  0.686
2009 Nessler B, Pfeiffer M, Maass W. STDP enables spiking neurons to detect hidden causes of their inputs Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1357-1365.  0.731
2009 Nessler B, Pfeiffer M, Maass W. Hebbian learning of Bayes optimal decisions Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1169-1176.  0.707
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  0.444
2008 Auer P, Burgsteiner H, Maass W. A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks : the Official Journal of the International Neural Network Society. 21: 786-95. PMID 18249524 DOI: 10.1016/j.neunet.2007.12.036  0.383
2008 Rasch MJ, Gretton A, Murayama Y, Maass W, Logothetis NK. Inferring spike trains from local field potentials. Journal of Neurophysiology. 99: 1461-76. PMID 18160425 DOI: 10.1152/Jn.00919.2007  0.735
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  0.393
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  0.375
2007 Sussillo D, Toyoizumi T, Maass W. Self-tuning of neural circuits through short-term synaptic plasticity. Journal of Neurophysiology. 97: 4079-95. PMID 17409166 DOI: 10.1152/Jn.01357.2006  0.39
2007 Maass W, Joshi P, Sontag ED. Computational aspects of feedback in neural circuits. Plos Computational Biology. 3: e165. PMID 17238280 DOI: 10.1371/Journal.Pcbi.0020165  0.626
2007 Neumann G, Pfeiffer M, Maass W. Efficient continuous-time reinforcement learning with adaptive state graphs Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4701: 250-261.  0.382
2007 Klampfl S, Legenstein R, Maass W. Information bottleneck optimization and independent component extraction with spiking neurons Advances in Neural Information Processing Systems. 713-720.  0.686
2006 Uchizawa K, Douglas R, Maass W. On the computational power of threshold circuits with sparse activity. Neural Computation. 18: 2994-3008. PMID 17052156 DOI: 10.1162/neco.2006.18.12.2994  0.558
2006 Uchizawa K, Douglas R, Maass W. Energy complexity and entropy of threshold circuits Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4051: 631-642. DOI: 10.1007/11786986_55  0.44
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  0.432
2005 Joshi P, Maass W. Movement generation with circuits of spiking neurons. Neural Computation. 17: 1715-38. PMID 15969915 DOI: 10.1162/0899766054026684  0.585
2004 Maass W, Natschläger T, Markram H. Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology, Paris. 98: 315-30. PMID 16310350 DOI: 10.1016/j.jphysparis.2005.09.020  0.41
2004 Melamed O, Gerstner W, Maass W, Tsodyks M, Markram H. Coding and learning of behavioral sequences. Trends in Neurosciences. 27: 11-4; discussion 14-. PMID 14698603 DOI: 10.1016/J.Tins.2003.10.014  0.307
2004 Maass W, Markram H. On the computational power of circuits of spiking neurons Journal of Computer and System Sciences. 69: 593-616. DOI: 10.1016/j.jcss.2004.04.001  0.384
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  0.404
2003 Häusler S, Markram H, Maass W. Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts Complexity. 8: 39-50. DOI: 10.1002/cplx.10089  0.426
2002 Maass W, Natschläger T, Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation. 14: 2531-60. PMID 12433288 DOI: 10.1162/089976602760407955  0.414
2002 Maass W, Markram H. Synapses as dynamic memory buffers. Neural Networks : the Official Journal of the International Neural Network Society. 15: 155-61. PMID 12022505 DOI: 10.1016/S0893-6080(01)00144-7  0.374
2001 Natschläger T, Maass W, Zador A. Efficient temporal processing with biologically realistic dynamic synapses Network: Computation in Neural Systems. 12: 75-87. DOI: 10.1088/0954-898X/12/1/305  0.361
2001 Grossberg S, Maass W, Markram H. Introduction: Spiking neurons in neuroscience and technology Neural Networks. 14: 587. DOI: 10.1016/S0893-6080(01)00102-2  0.353
2000 Maass W, Sontag ED. Neural systems as nonlinear filters. Neural Computation. 12: 1743-72. PMID 10953237 DOI: 10.1162/089976600300015123  0.336
1998 Maass W, Warmuth MK. Efficient Learning with Virtual Threshold Gates Information and Computation. 141: 66-83. DOI: 10.1006/Inco.1997.2686  0.31
1994 Maass W, Turán G. Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts Machine Learning. 14: 251-269. DOI: 10.1023/A:1022653511837  0.321
1993 Hajnal A, Maass W, Pudlák P, Szegedy M, Turán G. Threshold circuits of bounded depth Journal of Computer and System Sciences. 46: 129-154. DOI: 10.1016/0022-0000(93)90001-D  0.303
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