Wolfgang Maass
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学术经历
慕尼黑路德维希马克西米利安大学(Ludwig-Maximilians-Universitaet)数学博士 (1974) 和 Habilitation (1978)。
1979 - 1984 年在麻省理工学院、芝加哥大学和加州大学伯克利分校作德国研究协会的海森堡研究员。
1982 - 1986 年在芝加哥伊利诺伊大学担任副教授。
1986 - 1993 年计算机科学教授。
自 1991 年起任奥地利格拉茨科技大学(Graz University of Technology)计算机科学教授，并于 1992-2017 年担任格拉茨科技大学理论计算机科学研究所（Institut fuer Grundlagen der Informationsverarbeitung）负责人。
1997/98 年期间，索尔克研究所（美国拉霍亚）计算神经生物学实验室的斯隆研究员。
从 9/2002 - 2/2003 和
2012 年 4 月 - 2012 年 7 月，瑞士洛桑 EPFL 脑脑研究所客座教授。
自 2005 年起担任法兰克福高等研究院 (FIAS) 的兼职研究员
2008 - 2012 年国际神经网络学会理事会成员。
自 2013 年起成为欧洲学术界成员
2018：加州大学伯克利分校西蒙斯研究所特别学期“大脑与计算”的联合组织者
Machine Learning编辑，1995 - 1997
Machine Learning编辑委员会成员，1998 - 2000
Mathematical Logic档案编辑，1987 - 2000
Computer and System Sciences杂志副主编，1992 - 2014
Neurocomputing编辑委员会成员，1994 - 2007
Cognitive Neurodynamics编辑委员会成员，2006 年至今
Biological Cybernetics编辑，2006 年至今
研究兴趣
生物神经系统中的信息处理
神经网络
机器学习
计算复杂度
计算理论
发表文献
近十年文献
| 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 | 0.385 |
| 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 | 0.339 |
| 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 |
高被引代表作
| TITLE | CITED | YEAR |
|---|---|---|
| Real-time computing without stable states: A new framework for neural computation based on perturbations
W Maass, T Natschläger, H Markram Neural computation 14 (11), 2531-2560 |
3568 | 2002 |
| Networks of spiking neurons: the third generation of neural network models
W Maass Neural networks 10 (9), 1659-1671 |
2442 | 1997 |
| Pulsed neural networks
W Maass, CM Bishop MIT press |
1246 | 2001 |
| Approximation schemes for covering and packing problems in image processing and VLSI
DS Hochbaum, W Maass Journal of the ACM (JACM) 32 (1), 130-136 |
886 | 1985 |
| State-dependent computations: spatiotemporal processing in cortical networks
DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125 |
876 | 2009 |
| Threshold circuits of bounded depth
A Hajnal, W Maass, P Pudlák, M Szegedy, G Turán Journal of Computer and System Sciences 46 (2), 129-154 |
475 | 1993 |
| Edge of chaos and prediction of computational performance for neural circuit models
R Legenstein, W Maass Neural networks 20 (3), 323-334 |
429 | 2007 |
| Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons
L Buesing, J Bill, B Nessler, W Maass PLoS computational biology 7 (11), e1002211 |
421 | 2011 |
| On the computational power of winner-take-all
W Maass Neural computation 12 (11), 2519-2535 |
372 | 2000 |
参考 https://scholar.google.com/citations?user=2WpvdH0AAAAJ&hl=en