| 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 |
0.384 |
|
| 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 |
0.482 |
|
| 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 |
0.397 |
|
| 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 |
0.376 |
|
| 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 |
0.412 |
|
| 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 |
0.406 |
|
| 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 |
0.362 |
|
| 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 |
0.338 |
|
| 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 |
0.358 |
|
| 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 |
0.467 |
|
| 2016 |
Maass W. Energy-efficient neural network chips approach human recognition capabilities. Proceedings of the National Academy of Sciences of the United States of America. PMID 27702894 DOI: 10.1073/pnas.1614109113 |
0.45 |
|
| 2016 |
Pecevski D, Maass W. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity. Eneuro. 3. PMID 27419214 DOI: 10.1523/ENEURO.0048-15.2016 |
0.399 |
|
| 2016 |
Jonke Z, Habenschuss S, Maass W. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons. Frontiers in Neuroscience. 10: 118. PMID 27065785 DOI: 10.3389/fnins.2016.00118 |
0.472 |
|
| 2016 |
Maass W. Searching for principles of brain computation Current Opinion in Behavioral Sciences. 11: 81-92. DOI: 10.1016/J.Cobeha.2016.06.003 |
0.475 |
|
| 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 |
0.455 |
|
| 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 |
0.765 |
|
| 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 |
0.504 |
|
| 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 |
0.743 |
|
| 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 |
0.488 |
|
| 2014 |
Maass W. Noise as a resource for computation and learning in networks of spiking neurons Proceedings of the Ieee. 102: 860-880. DOI: 10.1109/JPROC.2014.2310593 |
0.367 |
|
| 2013 |
Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical microcircuit models. Plos Computational Biology. 9: e1003311. PMID 24244126 DOI: 10.1371/journal.pcbi.1003311 |
0.45 |
|
| 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 |
0.762 |
|
| 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 |
0.796 |
|
| 2013 |
Habenschuss S, Puhr H, Maass W. Emergence of optimal decoding of population codes through STDP. Neural Computation. 25: 1371-407. PMID 23517096 DOI: 10.1162/NECO_a_00446 |
0.493 |
|
| 2013 |
Habenschuss S, Jonke Z, Maass W. Solving Sudoku, a constraint satisfaction problem, through structured interactions between stochastically firing excitatory and inhibitory neurons. Plos Computational Biology. DOI: 10.1371/Journal.Pcbi.1003311.G005 |
0.362 |
|
| 2012 |
Rückert EA, Neumann G, Toussaint M, Maass W. Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers in Computational Neuroscience. 6: 97. PMID 23293598 DOI: 10.3389/fncom.2012.00097 |
0.377 |
|
| 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 |
0.318 |
|
| 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 |
0.546 |
|
| 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 |
0.757 |
|
| 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 |
0.344 |
|
| 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 |
0.498 |
|
| 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 |
0.763 |
|
| 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 |
0.467 |
|
| 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 |
0.76 |
|
| 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 |
0.387 |
|
| 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 |
0.785 |
|
| 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 |
0.459 |
|
| 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 |
0.511 |
|
| 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 |
0.788 |
|
| 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 |
0.356 |
|
| 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 |
0.437 |
|
| 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 |
0.615 |
|
| 2009 |
Haeusler S, Schuch K, Maass W. Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates. Journal of Physiology, Paris. 103: 73-87. PMID 19500669 DOI: 10.1016/j.jphysparis.2009.05.006 |
0.435 |
|
| 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 |
0.436 |
|
| 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 |
0.78 |
|
| 2009 |
Buesing L, Maass W. Simplified rules and theoretical analysis for Information Bottleneck Optimization and PCA with spiking neurons Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.327 |
|
| 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. |
0.751 |
|
| 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. |
0.714 |
|
| 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. |
0.74 |
|
| 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 |
0.506 |
|
| 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 |
0.43 |
|
| 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 |
0.74 |
|
| 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 |
0.441 |
|
| 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 |
0.416 |
|
| 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 |
0.428 |
|
| 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 |
0.659 |
|
| 2007 |
Haeusler S, Maass W. A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cerebral Cortex (New York, N.Y. : 1991). 17: 149-62. PMID 16481565 DOI: 10.1093/cercor/bhj132 |
0.445 |
|
| 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. |
0.738 |
|
| 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. |
0.431 |
|
| 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 |
0.573 |
|
| 2006 |
Kaske A, Maass W. A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models. Neural Networks : the Official Journal of the International Neural Network Society. 19: 600-9. PMID 16150571 DOI: 10.1016/j.neunet.2005.06.047 |
0.471 |
|
| 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 |
0.427 |
|
| 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 |
0.493 |
|
| 2005 |
Natschläger T, Maass W. Dynamics of information and emergent computation in generic neural microcircuit models. Neural Networks : the Official Journal of the International Neural Network Society. 18: 1301-8. PMID 16150570 DOI: 10.1016/j.neunet.2005.05.004 |
0.488 |
|
| 2005 |
Joshi P, Maass W. Movement generation with circuits of spiking neurons. Neural Computation. 17: 1715-38. PMID 15969915 DOI: 10.1162/0899766054026684 |
0.597 |
|
| 2005 |
Legenstein RA, Maass W. Wire length as a circuit complexity measure Journal of Computer and System Sciences. 70: 53-72. DOI: 10.1016/j.jcss.2004.06.001 |
0.305 |
|
| 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 |
0.471 |
|
| 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 |
0.315 |
|
| 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 |
0.435 |
|
| 2004 |
Joshi P, Maass W. Movement generation and control with generic neural microcircuits Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3141: 258-273. DOI: 10.1007/978-3-540-27835-1_20 |
0.305 |
|
| 2004 |
Natschläger T, Maass W. Information dynamics and emergent computation in recurrent circuits of spiking neurons Advances in Neural Information Processing Systems. |
0.344 |
|
| 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 |
0.448 |
|
| 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 |
0.488 |
|
| 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 |
0.476 |
|
| 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 |
0.409 |
|
| 2002 |
Natschläger T, Maass W. Spiking neurons and the induction of finite state machines Theoretical Computer Science. 287: 251-265. DOI: 10.1016/S0304-3975(02)00099-3 |
0.438 |
|
| 2002 |
Legenstein RA, Maass W. Neural circuits for pattern recognition with small total wire length Theoretical Computer Science. 287: 239-249. DOI: 10.1016/S0304-3975(02)00097-X |
0.308 |
|
| 2001 |
Natschläger T, Maass W. Computing the optimally fitted spike train for a synapse. Neural Computation. 13: 2477-94. PMID 11674847 DOI: 10.1162/089976601753195987 |
0.405 |
|
| 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 |
0.392 |
|
| 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 |
0.387 |
|
| 2000 |
Maass W. On the computational power of winner-take-all. Neural Computation. 12: 2519-35. PMID 11110125 DOI: 10.1162/089976600300014827 |
0.412 |
|
| 2000 |
Maass W, Sontag ED. Neural systems as nonlinear filters. Neural Computation. 12: 1743-72. PMID 10953237 DOI: 10.1162/089976600300015123 |
0.362 |
|
| 2000 |
Maass W, Natschläger T. A model for fast analog computation based on unreliable synapses. Neural Computation. 12: 1679-704. PMID 10935922 DOI: 10.1162/089976600300015303 |
0.5 |
|
| 1999 |
Maass W, Zador AM. Dynamic stochastic synapses as computational units. Neural Computation. 11: 903-17. PMID 10226188 |
0.326 |
|
| 1999 |
Maass W, Schmitt M. On the Complexity of Learning for Spiking Neurons with Temporal Coding Information and Computation. 153: 26-46. DOI: 10.1006/Inco.1999.2806 |
0.512 |
|
| 1999 |
Maass W, Ruf B. On Computation with Pulses Information and Computation. 148: 202-218. DOI: 10.1006/Inco.1998.2743 |
0.335 |
|
| 1998 |
Maass W. A simple model for neural computation with firing rates and firing correlations. Network (Bristol, England). 9: 381-97. PMID 9861997 DOI: 10.1088/0954-898X_9_3_007 |
0.419 |
|
| 1998 |
Maass W, Warmuth MK. Efficient Learning with Virtual Threshold Gates Information and Computation. 141: 66-83. DOI: 10.1006/Inco.1997.2686 |
0.326 |
|
| 1997 |
Maass W. Fast sigmoidal networks via spiking neurons. Neural Computation. 9: 279-304. PMID 9117904 DOI: 10.1162/Neco.1997.9.2.279 |
0.504 |
|
| 1997 |
Maass W. Bounds for the computational power and learning complexity of analog neural nets Siam Journal On Computing. 26: 708-732. DOI: 10.1137/S0097539793256041 |
0.394 |
|
| 1997 |
Maass W, Natschlager T. Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding Network: Computation in Neural Systems. 8: 355-371. DOI: 10.1088/0954-898X_8_4_002 |
0.482 |
|
| 1997 |
Maass W. Networks of spiking neurons: The third generation of neural network models Neural Networks. 10: 1659-1671. DOI: 10.1016/S0893-6080(97)00011-7 |
0.319 |
|
| 1997 |
Maass W. Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons Advances in Neural Information Processing Systems. 211-217. |
0.319 |
|
| 1996 |
Maass W. Lower Bounds for the Computational Power of Networks of Spiking Neurons Neural Computation. 8: 1-40. DOI: 10.1162/Neco.1996.8.1.1 |
0.498 |
|
| 1995 |
Maass W. Agnostic PAC Learning of Functions on Analog Neural Nets Neural Computation. 7: 1054-1078. DOI: 10.1162/neco.1995.7.5.1054 |
0.311 |
|
| 1995 |
Bultman WJ, Maass W. Fast Identification of Geometric Objects with Membership Queries Information and Computation. 118: 48-64. DOI: 10.1006/inco.1995.1051 |
0.343 |
|
| 1994 |
Maass W. Neural nets with superlinear VC-dimension Neural Computation. 6: 877-884. DOI: 10.1162/Neco.1994.6.5.877 |
0.311 |
|
| 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 |
0.342 |
|
| 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 |
0.31 |
|
| 1989 |
Maass W, Slaman TA. Some Problems and Results in the Theory of Actually Computable Functions (preliminary abstract) Studies in Logic and the Foundations of Mathematics. 127: 79-89. DOI: 10.1016/S0049-237X(08)70263-5 |
0.331 |
|
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