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ORCIDWolfgang Maass 0001
Wolfgang Maaß 0001
Person information
- affiliation: Graz University of Technology, Institute for Theoretical Computer Science, Austria
- affiliation (former): University of Illinois at Chicago, Department of Mathematics, Statistics and Computer Science, IL, USA
- affiliation (former): University of California, Berkeley, Department of Computer Science, CA, USA
Other persons with the same name
- Wolfgang Maass 0002
![[0000-0003-4057-0924]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0003-4057-0924")
(aka: Wolfgang Maaß 0002) — DFKI, Germany (and 2 more)
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2020 – today
- 2024
[j93]Guoqi Li, Lei Deng, Huajin Tang, Gang Pan, Yonghong Tian, Kaushik Roy, Wolfgang Maass:
Brain-Inspired Computing: A Systematic Survey and Future Trends. Proc. IEEE 112(6): 544-584 (2024)- 2023
- [i47]Wolfgang Maass:
How can neuromorphic hardware attain brain-like functional capabilities? CoRR abs/2310.16444 (2023) - 2022
- [j92]Arjun Rao, Philipp Plank, Andreas Wild, Wolfgang Maass:
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware. Nat. Mach. Intell. 4(5): 467-479 (2022) - [j91]Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Steve B. Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shih-Chii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds:
2022 roadmap on neuromorphic computing and engineering. Neuromorph. Comput. Eng. 2(2): 22501 (2022) - 2021
- [j90]Christoph Stöckl, Wolfgang Maass:
Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. Nat. Mach. Intell. 3(3): 230-238 (2021) - [p4]Ceca Kraisnikovic, Wolfgang Maass, Robert Legenstein:
Spike-Based Symbolic Computations on Bit Strings and Numbers. Neuro-Symbolic Artificial Intelligence 2021: 214-234 - [p3]Anand Subramoney, Franz Scherr, Wolfgang Maass:
Reservoirs Learn to Learn. Reservoir Computing 2021: 59-76 - [i46]Luke Y. Prince, Ellen Boven, Roy Henha Eyono, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes:
CCN GAC Workshop: Issues with learning in biological recurrent neural networks. CoRR abs/2105.05382 (2021) - [i45]Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Emre Neftci, Srikanth Ramaswamy, Jonathan Tapson, Franz Scherr, Wolfgang Maass, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shih-Chii Liu, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds:
2021 Roadmap on Neuromorphic Computing and Engineering. CoRR abs/2105.05956 (2021) - [i44]Philipp Plank, Arjun Rao, Andreas Wild, Wolfgang Maass:
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware. CoRR abs/2107.03992 (2021) - 2020
- [i43]Christoph Stöckl, Wolfgang Maass:
Recognizing Images with at most one Spike per Neuron. CoRR abs/2001.01682 (2020) - [i42]Christoph Stöckl, Wolfgang Maass:
Classifying Images with Few Spikes per Neuron. CoRR abs/2002.00860 (2020) - [i41]Jacques Kaiser, Michael Hoff, Andreas Konle, Juan Camilo Vasquez Tieck, David Kappel, Daniel Reichard, Anand Subramoney, Robert Legenstein, Arne Roennau, Wolfgang Maass, Rüdiger Dillmann:
Embodied Synaptic Plasticity with Online Reinforcement learning. CoRR abs/2003.01431 (2020) - [i40]Thomas Bohnstingl, Stanislaw Wozniak, Wolfgang Maass, Angeliki Pantazi, Evangelos Eleftheriou:
Online spatio-temporal learning in deep neural networks. CoRR abs/2007.12723 (2020)
2010 – 2019
- 2019
- [j89]Jacques Kaiser, Michael Hoff, Andreas Konle, Juan Camilo Vasquez Tieck, David Kappel, Daniel Reichard, Anand Subramoney, Robert Legenstein, Arne Roennau, Wolfgang Maass, Rüdiger Dillmann:
Embodied Synaptic Plasticity With Online Reinforcement Learning. Frontiers Neurorobotics 13: 81 (2019) - [j88]Yexin Yan, David Kappel, Felix Neumärker, Johannes Partzsch, Bernhard Vogginger, Sebastian Höppner, Steve B. Furber, Wolfgang Maass, Robert Legenstein, Christian Mayr:
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. IEEE Trans. Biomed. Circuits Syst. 13(3): 579-591 (2019) - [p2]Wolfgang Maass, Christos H. Papadimitriou, Santosh S. Vempala, Robert Legenstein:
Brain Computation: A Computer Science Perspective. Computing and Software Science 2019: 184-199 - [i39]Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass:
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. CoRR abs/1901.09049 (2019) - [i38]Thomas Bohnstingl, Franz Scherr, Christian Pehle, Karlheinz Meier, Wolfgang Maass:
Neuromorphic Hardware learns to learn. CoRR abs/1903.06493 (2019) - [i37]Yexin Yan, David Kappel, Felix Neumärker, Johannes Partzsch, Bernhard Vogginger, Sebastian Höppner, Steve B. Furber, Wolfgang Maass, Robert Legenstein, Christian Mayr:
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. CoRR abs/1903.08500 (2019) - [i36]Anand Subramoney, Franz Scherr, Wolfgang Maass:
Reservoirs learn to learn. CoRR abs/1909.07486 (2019) - 2018
- [c76]Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein:
Deep Rewiring: Training very sparse deep networks. ICLR (Poster) 2018 - [c75]Robert Legenstein, Wolfgang Maass, Christos H. Papadimitriou, Santosh S. Vempala:
Long Term Memory and the Densest K-Subgraph Problem. ITCS 2018: 57:1-57:15 - [c74]Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass:
Long short-term memory and Learning-to-learn in networks of spiking neurons. NeurIPS 2018: 795-805 - [c73]Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos H. Papadimitriou, Amin Saberi, Santosh S. Vempala:
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. NeurIPS 2018: 10880-10890 - [i35]Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass:
Long short-term memory and Learning-to-learn in networks of spiking neurons. CoRR abs/1803.09574 (2018) - [i34]Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos H. Papadimitriou, Amin Saberi, Santosh S. Vempala:
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. CoRR abs/1810.11896 (2018) - 2017
- [c72]Sebastian Schmitt, Johann Klähn, Guillaume Bellec, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Eric Müller, Paul Müller, Johannes Partzsch, Mihai A. Petrovici, Stefan Schiefer, Stefan Scholze, Vasilis N. Thanasoulis, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, Christian Mayr, René Schüffny, Johannes Schemmel, Karlheinz Meier:
Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system. IJCNN 2017: 2227-2234 - [c71]Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn, Robert D. St. Louis, Anna Schroeder, Guillaume Bellec, Johannes Bill, Oliver Breitwieser, Ilja Bytschok, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Eric Müller, Paul Müller, Johannes Partzsch, Thomas Pfeil, Stefan Schiefer, Stefan Scholze, Anand Subramoney, Vasilis N. Thanasoulis, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, René Schüffny, Christian Mayr, Johannes Schemmel, Karlheinz Meier:
Pattern representation and recognition with accelerated analog neuromorphic systems. ISCAS 2017: 1-4 - [i33]Sebastian Schmitt, Johann Klaehn, Guillaume Bellec, Andreas Grübl, Maurice Guettler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Vitali Karasenko, Mitja Kleider, Christoph Koke, Christian Mauch, Eric Müller, Paul Müller, Johannes Partzsch, Mihai A. Petrovici, Stefan Schiefer, Stefan Scholze, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, Christian Mayr, Johannes Schemmel, Karlheinz Meier:
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System. CoRR abs/1703.01909 (2017) - [i32]Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn, David Stöckel, Anna Schroeder, Guillaume Bellec, Johannes Bill, Oliver Breitwieser, Ilja Bytschok, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Paul Müller, Johannes Partzsch, Thomas Pfeil, Stefan Schiefer, Stefan Scholze, Anand Subramoney, Vasilis N. Thanasoulis, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, René Schüffny, Christian Mayr, Johannes Schemmel, Karlheinz Meier:
Pattern representation and recognition with accelerated analog neuromorphic systems. CoRR abs/1703.06043 (2017) - [i31]David Kappel, Robert Legenstein, Stefan Habenschuss, Michael Hsieh, Wolfgang Maass:
Reward-based stochastic self-configuration of neural circuits. CoRR abs/1704.04238 (2017) - [i30]Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein:
Deep Rewiring: Training very sparse deep networks. CoRR abs/1711.05136 (2017) - 2016
- [j87]Wolfgang Maass:
Energy-efficient neural network chips approach human recognition capabilities. Proc. Natl. Acad. Sci. USA 113(41): 11387-11389 (2016) - [c70]Robert Legenstein, Christos H. Papadimitriou, Santosh S. Vempala, Wolfgang Maass:
Variable Binding through Assemblies in Spiking Neural Networks. CoCo@NIPS 2016 - [i29]Zhaofei Yu, David Kappel, Robert Legenstein, Sen Song, Feng Chen, Wolfgang Maass:
Hamiltonian synaptic sampling in a model for reward-gated network plasticity. CoRR abs/1606.00157 (2016) - 2015
- [j86]Wolfgang Maass:
To Spike or Not to Spike: That Is the Question. Proc. IEEE 103(12): 2219-2224 (2015) - [j85]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Network Plasticity as Bayesian Inference. PLoS Comput. Biol. 11(11) (2015) - [c69]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring. NIPS 2015: 370-378 - [i28]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Network Plasticity as Bayesian Inference. CoRR abs/1504.05143 (2015) - 2014
- [j84]Wolfgang Maass:
Noise as a Resource for Computation and Learning in Networks of Spiking Neurons. Proc. IEEE 102(5): 860-880 (2014) - [j83]David Kappel, Bernhard Nessler, Wolfgang Maass:
STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. PLoS Comput. Biol. 10(3) (2014) - [j82]Robert Legenstein, Wolfgang Maass:
Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment. PLoS Comput. Biol. 10(10) (2014) - [i27]Zeno Jonke, Stefan Habenschuss, Wolfgang Maass:
A theoretical basis for efficient computations with noisy spiking neurons. CoRR abs/1412.5862 (2014) - 2013
- [j81]Stefan Habenschuss, Helmut Puhr, Wolfgang Maass:
Emergence of Optimal Decoding of Population Codes Through STDP. Neural Comput. 25(6): 1371-1407 (2013) - [j80]Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass:
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. 9(4) (2013) - [j79]Stefan Habenschuss, Zeno Jonke, Wolfgang Maass:
Stochastic Computations in Cortical Microcircuit Models. PLoS Comput. Biol. 9(11) (2013) - 2012
- [j78]Helmut Hauser, Auke Jan Ijspeert, Rudolf M. Füchslin, Rolf Pfeifer, Wolfgang Maass:
The role of feedback in morphological computation with compliant bodies. Biol. Cybern. 106(10): 595-613 (2012) - [j77]Elmar A. Rückert, Gerhard Neumann, Marc Toussaint, Wolfgang Maass:
Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers Comput. Neurosci. 6: 97 (2012) - [c68]Dimitri Probst, Wolfgang Maass, Henry Markram, Marc-Oliver Gewaltig:
Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball. ICANN (1) 2012: 209-216 - 2011
- [j76]Helmut Hauser, Gerhard Neumann, Auke Jan Ijspeert, Wolfgang Maass:
Biologically inspired kinematic synergies enable linear balance control of a humanoid robot. Biol. Cybern. 104(4-5): 235-249 (2011) - [j75]Helmut Hauser, Auke Jan Ijspeert, Rudolf M. Füchslin, Rolf Pfeifer, Wolfgang Maass:
Towards a theoretical foundation for morphological computation with compliant bodies. Biol. Cybern. 105(5-6): 355-370 (2011) - [j74]Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass:
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. PLoS Comput. Biol. 7(11) (2011) - [j73]Dejan Pecevski, Lars Buesing, Wolfgang Maass:
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons. PLoS Comput. Biol. 7(12) (2011) - 2010
- [j72]Johannes Bill, Klaus Schuch, Daniel Brüderle, Johannes Schemmel, Wolfgang Maass, Karlheinz Meier:
Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity. Frontiers Comput. Neurosci. 4: 129 (2010) - [j71]Michael Pfeiffer, Bernhard Nessler, Rodney J. Douglas, Wolfgang Maass:
Reward-Modulated Hebbian Learning of Decision Making. Neural Comput. 22(6): 1399-1444 (2010) - [j70]Lars Buesing, Wolfgang Maass:
A Spiking Neuron as Information Bottleneck. Neural Comput. 22(8): 1961-1992 (2010) - [j69]Stefan Klampfl, Wolfgang Maass:
A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction. Neural Comput. 22(12): 2979-3035 (2010) - [e3]Barbara Hammer, Pascal Hitzler, Wolfgang Maass, Marc Toussaint:
Learning paradigms in dynamic environments, 25.07. - 30.07.2010. Dagstuhl Seminar Proceedings 10302, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2010 [contents] - [i26]Barbara Hammer, Pascal Hitzler, Wolfgang Maass, Marc Toussaint:
10302 Abstracts Collection - Learning paradigms in dynamic environments. Learning paradigms in dynamic environments 2010 - [i25]Barbara Hammer, Pascal Hitzler, Wolfgang Maass, Marc Toussaint:
10302 Summary - Learning paradigms in dynamic environments. Learning paradigms in dynamic environments 2010
2000 – 2009
- 2009
- [j68]Stefan Klampfl, Robert Legenstein, Wolfgang Maass:
Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components. Neural Comput. 21(4): 911-959 (2009) - [j67]Andreas Steimer, Wolfgang Maass, Rodney J. Douglas:
Belief Propagation in Networks of Spiking Neurons. Neural Comput. 21(9): 2502-2523 (2009) - [c67]Gerhard Neumann, Wolfgang Maass, Jan Peters:
Learning complex motions by sequencing simpler motion templates. ICML 2009: 753-760 - [c66]Stefan Klampfl, Wolfgang Maass:
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks. NIPS 2009: 988-996 - [c65]Robert Legenstein, Steven M. Chase, Andrew B. Schwartz, Wolfgang Maass:
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning. NIPS 2009: 1105-1113 - [c64]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
STDP enables spiking neurons to detect hidden causes of their inputs. NIPS 2009: 1357-1365 - 2008
- [j66]Robert Legenstein, Wolfgang Maass:
On the Classification Capability of Sign-Constrained Perceptrons. Neural Comput. 20(1): 288-309 (2008) - [j65]Peter Auer, Harald Burgsteiner, Wolfgang Maass:
A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks 21(5): 786-795 (2008) - [j64]Robert Legenstein, Dejan Pecevski, Wolfgang Maass:
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback. PLoS Comput. Biol. 4(10) (2008) - [c63]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
Hebbian Learning of Bayes Optimal Decisions. NIPS 2008: 1169-1176 - [e2]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
Recurrent Neural Networks - Models, Capacities, and Applications, 20.01. - 25.01.2008. Dagstuhl Seminar Proceedings 08041, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - [i24]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications. Recurrent Neural Networks 2008 - [i23]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications. Recurrent Neural Networks 2008 - 2007
- [j63]Herbert Jaeger, Wolfgang Maass, José Carlos Príncipe:
Special issue on echo state networks and liquid state machines. Neural Networks 20(3): 287-289 (2007) - [j62]Robert Legenstein, Wolfgang Maass:
Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20(3): 323-334 (2007) - [j61]Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
Computational Aspects of Feedback in Neural Circuits. PLoS Comput. Biol. 3(1) (2007) - [c62]Wolfgang Maass:
Liquid Computing. CiE 2007: 507-516 - [c61]Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass:
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. ECML 2007: 250-261 - [c60]Helmut Hauser, Gerhard Neumann, Auke Jan Ijspeert, Wolfgang Maass:
Biologically inspired kinematic synergies provide a new paradigm for balance control of humanoid robots. Humanoids 2007: 73-80 - [c59]Lars Buesing, Wolfgang Maass:
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. NIPS 2007: 193-200 - [c58]Robert Legenstein, Dejan Pecevski, Wolfgang Maass:
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity. NIPS 2007: 881-888 - 2006
- [j60]Kei Uchizawa, Rodney J. Douglas, Wolfgang Maass:
On the Computational Power of Threshold Circuits with Sparse Activity. Neural Comput. 18(12): 2994-3008 (2006) - [j59]Alexander Kaske, Wolfgang Maass:
A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models. Neural Networks 19(5): 600-609 (2006) - [j58]Wolfgang Maass:
"Imitation of life: how biology is inspiring computing" by Nancy Forbes. Pattern Anal. Appl. 8(4): 390-391 (2006) - [c57]Kei Uchizawa, Rodney J. Douglas, Wolfgang Maass:
Energy Complexity and Entropy of Threshold Circuits. ICALP (1) 2006: 631-642 - [c56]Stefan Klampfl, Robert Legenstein, Wolfgang Maass:
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons. NIPS 2006: 713-720 - [c55]Danko Nikolic, Stefan Häusler, Wolf Singer, Wolfgang Maass:
Temporal dynamics of information content carried by neurons in the primary visual cortex. NIPS 2006: 1041-1048 - [i22]Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
Computational aspects of feedback in neural circuits. Electron. Colloquium Comput. Complex. TR06 (2006) - [i21]Wolfgang Maass, Kei Uchizawa, Rodney J. Douglas:
Energy Complexity and Entropy of Threshold Circuits. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [j57]Robert Legenstein, Wolfgang Maass:
Wire length as a circuit complexity measure. J. Comput. Syst. Sci. 70(1): 53-72 (2005) - [j56]Prashant Joshi, Wolfgang Maass:
Movement Generation with Circuits of Spiking Neurons. Neural Comput. 17(8): 1715-1738 (2005) - [j55]Robert Legenstein, Christian Naeger, Wolfgang Maass:
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? Neural Comput. 17(11): 2337-2382 (2005) - [j54]Thomas Natschläger, Wolfgang Maass:
Dynamics of information and emergent computation in generic neural microcircuit models. Neural Networks 18(10): 1301-1308 (2005) - [c54]Robert Legenstein, Wolfgang Maass:
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. NIPS 2005: 763-770 - [c53]Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
Principles of real-time computing with feedback applied to cortical microcircuit models. NIPS 2005: 835-842 - 2004
- [j53]Wolfgang Maass, Henry Markram:
On the computational power of circuits of spiking neurons. J. Comput. Syst. Sci. 69(4): 593-616 (2004) - [c52]Prashant Joshi, Wolfgang Maass:
Movement Generation and Control with Generic Neural Microcircuits. BioADIT 2004: 258-273 - [c51]Wolfgang Maass, Robert Legenstein, Nils Bertschinger:
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits. NIPS 2004: 865-872 - 2003
- [j52]Stefan Häusler, Henry Markram, Wolfgang Maass:
Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts. Complex. 8(4): 39-50 (2003) - [c50]Thomas Natschläger, Wolfgang Maass:
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons. NIPS 2003: 1255-1262 - 2002
- [j51]Wolfgang Maass, Thomas Natschläger, Henry Markram:
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Comput. 14(11): 2531-2560 (2002) - [j50]Wolfgang Maass, Henry Markram:
Synapses as dynamic memory buffers. Neural Networks 15(1): 155-161 (2002) - [j49]Robert Legenstein, Wolfgang Maass:
Neural circuits for pattern recognition with small total wire length. Theor. Comput. Sci. 287(1): 239-249 (2002) - [j48]Thomas Natschläger, Wolfgang Maass:
Spiking neurons and the induction of finite state machines. Theor. Comput. Sci. 287(1): 251-265 (2002) - [c49]Wolfgang Maass, Robert Legenstein, Henry Markram:
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models. Biologically Motivated Computer Vision 2002: 282-293 - [c48]Peter Auer, Harald Burgsteiner, Wolfgang Maass:
Reducing Communication for Distributed Learning in Neural Networks. ICANN 2002: 123-128 - [c47]Wolfgang Maass:
On the Computational Power of Neural Microcircuit Models: Pointers to the Literature. ICANN 2002: 254-258 - [c46]Wolfgang Maass, Thomas Natschläger, Henry Markram:
A Model for Real-Time Computation in Generic Neural Microcircuits. NIPS 2002: 213-220 - [i20]Wolfgang Maass, Henry Markram:
On the Computational Power of Recurrent Circuits of Spiking Neurons. Electron. Colloquium Comput. Complex. TR02 (2002) - 2001
- [j47]Thomas Natschläger, Wolfgang Maass:
Computing the Optimally Fitted Spike Train for a Synapse. Neural Comput. 13(11): 2477-2494 (2001) - [j46]Stephen Grossberg, Wolfgang Maass, Henry Markram:
Introduction: Spiking Neurons in Neuroscience and Technology. Neural Networks 14(6-7): 587- (2001) - [j45]Wolfgang Maass:
On the relevance of time in neural computation and learning. Theor. Comput. Sci. 261(1): 157-178 (2001) - [p1]Wolfgang Maass:
Neural Computation: A Research Topic for Theoretical Computer Science? Some Thoughts and Pointers. Current Trends in Theoretical Computer Science 2001: 680-690 - [i19]Robert Legenstein, Wolfgang Maass:
Optimizing the Layout of a Balanced Tree. Electron. Colloquium Comput. Complex. TR01 (2001) - [i18]Robert Legenstein, Wolfgang Maass:
Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing. Electron. Colloquium Comput. Complex. TR01 (2001) - [i17]Robert Legenstein, Wolfgang Maass:
Neural Circuits for Pattern Recognition with Small Total Wire Length. Electron. Colloquium Comput. Complex. TR01 (2001) - 2000
- [j44]Wolfgang Maass:
Neural Computation: A Research Topic for Theoretical Computer Science? Some Thoughts and Pointers. Bull. EATCS 72: 149-158 (2000) - [j43]Wolfgang Maass, Thomas Natschläger:
A Model for Fast Analog Computation Based on Unreliable Synapses. Neural Comput. 12(7): 1679-1704 (2000) - [j42]Wolfgang Maass, Eduardo D. Sontag:
Neural Systems as Nonlinear Filters. Neural Comput. 12(8): 1743-1772 (2000) - [j41]Wolfgang Maass:
On the Computational Power of Winner-Take-All. Neural Comput. 12(11): 2519-2535 (2000) - [c45]Wolfgang Maass, Gerald Steinbauer, Roland Koholka:
Autonomous Fast Learning in a Mobile Robot. Sensor Based Intelligent Robots 2000: 345-356 - [c44]Thomas Natschläger, Wolfgang Maass:
Finding the Key to a Synapse. NIPS 2000: 138-144 - [c43]Thomas Natschläger, Wolfgang Maass, Eduardo D. Sontag, Anthony M. Zador:
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics. NIPS 2000: 145-151 - [c42]Robert Legenstein, Wolfgang Maass:
Foundations for a Circuit Complexity Theory of Sensory Processing. NIPS 2000: 259-265 - [i16]Wolfgang Maass:
A Simple Model for Neural Computation with Firing Rates and Firing Correlations. Electron. Colloquium Comput. Complex. TR00 (2000) - [i15]Wolfgang Maass, Eduardo D. Sontag:
Neural Systems as Nonlinear Filters. Electron. Colloquium Comput. Complex. TR00 (2000) - [i14]Wolfgang Maass:
On the Computational Power of Winner-Take-All. Electron. Colloquium Comput. Complex. TR00 (2000) - [i13]Wolfgang Maass:
On Computation with Pulses. Electron. Colloquium Comput. Complex. TR00 (2000) - [i12]Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger:
On the Complexity of Function Learning. Electron. Colloquium Comput. Complex. TR00 (2000) - [i11]Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth:
Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes. Electron. Colloquium Comput. Complex. TR00 (2000)
1990 – 1999
- 1999
- [j40]Wolfgang Maass, Berthold Ruf:
On Computations with Pulses. Inf. Comput. 148(2): 202-218 (1999) - [j39]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Inf. Comput. 153(1): 26-46 (1999) - [j38]Wolfgang Maass, Eduardo D. Sontag:
Analog Neural Nets with Gaussian or Other Common Noise Distribution Cannot Recognize Arbitrary Regular Languages. Neural Comput. 11(3): 771-782 (1999) - [j37]Wolfgang Maass, Anthony M. Zador:
Dynamic Stochastic Synapses as Computational Units. Neural Comput. 11(4): 903-917 (1999) - [c41]Thomas Natschläger, Wolfgang Maass:
Fast analog computation in networks of spiking neurons using unreliable synapses. ESANN 1999: 417-422 - [c40]Wolfgang Maass:
Neural Computation with Winner-Take-All as the Only Nonlinear Operation. NIPS 1999: 293-299 - 1998
- [j36]Wolfgang Maass, Manfred K. Warmuth:
Efficient Learning With Virtual Threshold Gates. Inf. Comput. 141(1): 66-83 (1998) - [j35]Wolfgang Maass, Pekka Orponen:
On the Effect of Analog Noise in Discrete-Time Analog Computations. Neural Comput. 10(5): 1071-1095 (1998) - [c39]Wolfgang Maass:
Models for Fast Analog Computation with Spiking Neurons. ICONIP 1998: 187-188 - [c38]Wolfgang Maass:
On the Role of Time and Space in Neural Computation. MFCS 1998: 72-83 - [c37]Wolfgang Maass:
Spiking Neurons. NC 1998: 16-20 - [c36]Wolfgang Maass, Eduardo D. Sontag:
A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions. NIPS 1998: 281-287 - 1997
- [j34]Wolfgang Maass:
Fast Sigmoidal Networks via Spiking Neurons. Neural Comput. 9(2): 279-304 (1997) - [j33]Wolfgang Maass:
Networks of spiking neurons: The third generation of neural network models. Neural Networks 10(9): 1659-1671 (1997) - [j32]Wolfgang Maass:
Bounds for the Computational Power and Learning Complexity of Analog Neural Nets. SIAM J. Comput. 26(3): 708-732 (1997) - [c35]Wolfgang Maass:
On the Relevance of Time in Neural Computation and Learning. ALT 1997: 364-384 - [c34]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for a Spiking Neuron (Extended Abstract). COLT 1997: 54-61 - [c33]Wolfgang Maass, Anthony M. Zador:
Dynamic Stochastic Synapses as Computational Units. NIPS 1997: 194-200 - [i10]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Electron. Colloquium Comput. Complex. TR97 (1997) - [i9]Wolfgang Maass, Pekka Orponen:
On the Effect of Analog Noise in Discrete-Time Analog Computations. Electron. Colloquium Comput. Complex. TR97 (1997) - [i8]Wolfgang Maass, Eduardo D. Sontag:
Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages. Electron. Colloquium Comput. Complex. TR97 (1997) - 1996
- [j31]David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass:
Computing the Maximum Bichromatic Discrepancy with Applications to Computer Graphics and Machine Learning. J. Comput. Syst. Sci. 52(3): 453-470 (1996) - [j30]Wolfgang Maass:
Lower Bounds for the Computational Power of Networks of Spiking Neurons. Neural Comput. 8(1): 1-40 (1996) - [c32]Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth:
Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). COLT 1996: 333-343 - [c31]Wolfgang Maass:
Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons. NIPS 1996: 211-217 - [c30]Wolfgang Maass, Pekka Orponen:
On the Effect of Analog Noise in Discrete-Time Analog Computations. NIPS 1996: 218-224 - [i7]Wolfgang Maass, Berthold Ruf:
The Computational Power of Spiking Neurons Depends on the Shape of the Postsynaptic Potentials. Electron. Colloquium Comput. Complex. TR96 (1996) - [i6]Wolfgang Maass:
Networks of Spiking Neurons: The Third Generation of Neural Network Models. Electron. Colloquium Comput. Complex. TR96 (1996) - 1995
- [j29]William J. Bultman, Wolfgang Maass:
Fast Identification of Geometric Objects with Membership Queries. Inf. Comput. 118(1): 48-64 (1995) - [j28]Wolfgang Maass:
Editor's Foreword. J. Comput. Syst. Sci. 51(3): 339 (1995) - [j27]Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger:
On the Complexity of Function Learning. Mach. Learn. 18(2-3): 187-230 (1995) - [j26]Wolfgang Maass:
Agnostic PAC Learning of Functions on Analog Neural Nets. Neural Comput. 7(5): 1054-1078 (1995) - [c29]Peter Auer, Robert C. Holte, Wolfgang Maass:
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees. ICML 1995: 21-29 - [c28]Wolfgang Maass, Manfred K. Warmuth:
Efficient Learning with Virtual Threshold Gates. ICML 1995: 378-386 - [c27]Wolfgang Maass:
On the Computational Power of Noisy Spiking Neurons. NIPS 1995: 211-217 - [e1]Wolfgang Maass:
Proceedings of the Eigth Annual Conference on Computational Learning Theory, COLT 1995, Santa Cruz, California, USA, July 5-8, 1995. ACM 1995, ISBN 0-89791-723-5 [contents] - 1994
- [j25]Wolfgang Maass, György Turán:
Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts. Mach. Learn. 14(1): 251-269 (1994) - [j24]Zhixiang Chen, Wolfgang Maass:
On-Line Learning of Rectangles and Unions of Rectangles. Mach. Learn. 17(2-3): 201-223 (1994) - [j23]Wolfgang Maass:
Neural Nets with Superlinear VC-Dimension. Neural Comput. 6(5): 877-884 (1994) - [c26]Wolfgang Maass:
Efficient Agnostic PAC-Learning with Simple Hypothesis. COLT 1994: 67-75 - [c25]Wolfgang Maass:
On the Computational Complexity of Networks of Spiking Neurons. NIPS 1994: 183-190 - [i5]Wolfgang Maass:
Bounds for the Computational Power and Learning Complexity of Analog Neural Nets. Electron. Colloquium Comput. Complex. TR94 (1994) - [i4]Wolfgang Maass:
Neural Nets with Superlinear VC-Dimension. Electron. Colloquium Comput. Complex. TR94 (1994) - [i3]Wolfgang Maass:
Lower Bounds for the Computational Power of Networks of Spiking Neurons. Electron. Colloquium Comput. Complex. TR94 (1994) - [i2]Wolfgang Maass:
Agnostic PAC-Learning of Functions on Analog Neural Nets. Electron. Colloquium Comput. Complex. TR94 (1994) - [i1]David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass:
Computing the Maximum Bichromatic Discrepancy, with applications to Computer Graphics and Machine Learning. Electron. Colloquium Comput. Complex. TR94 (1994) - 1993
- [j22]Wolfgang Maass, Georg Schnitger, Endre Szemerédi, György Turán:
Two Tapes Versus One for Off-Line Turing Machines. Comput. Complex. 3: 392-401 (1993) - [j21]András Hajnal, Wolfgang Maass, Pavel Pudlák, Mario Szegedy, György Turán:
Threshold Circuits of Bounded Depth. J. Comput. Syst. Sci. 46(2): 129-154 (1993) - [j20]Martin Dietzfelbinger, Wolfgang Maass:
The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape. Theor. Comput. Sci. 108(2): 271-290 (1993) - [c24]Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger:
On the Complexity of Function Learning. COLT 1993: 392-401 - [c23]Wolfgang Maass:
On the complexity of learning on neural nets. EuroCOLT 1993: 1-17 - [c22]Wolfgang Maass:
Agnostic PAC-Learning of Functions on Analog Neural Nets. NIPS 1993: 311-318 - [c21]Wolfgang Maass:
Bounds for the computational power and learning complexity of analog neural nets. STOC 1993: 335-344 - 1992
- [j19]Wolfgang Maass, Theodore A. Slaman:
The Complexity Types of Computable Sets. J. Comput. Syst. Sci. 44(2): 168-192 (1992) - [j18]Wolfgang Maass, György Turán:
Lower Bound Methods and Separation Results for On-Line Learning Models. Mach. Learn. 9: 107-145 (1992) - [c20]Zhixiang Chen, Wolfgang Maass:
A Solution of the Credit Assignment Problem in the Case of Learning Rectangles (Abstract). AII 1992: 26-34 - [c19]Zhixiang Chen, Wolfgang Maass:
On-line Learning of Rectangles. COLT 1992: 16-28 - 1991
- [j17]Martin Dietzfelbinger, Wolfgang Maass, Georg Schnitger:
The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines. Theor. Comput. Sci. 82(1): 113-129 (1991) - [c18]Wolfgang Maass:
On-Line Learning with an Oblivious Environment and the Power of Randomization. COLT 1991: 167-175 - [c17]William J. Bultman, Wolfgang Maass:
Fast Identification of Geometric Objects with Membership Queries. COLT 1991: 337-353 - [c16]Wolfgang Maass, Georg Schnitger, Eduardo D. Sontag:
On the Computational Power of Sigmoid versus Boolean Threshold Circuits. FOCS 1991: 767-776 - 1990
- [c15]Wolfgang Maass, György Turán:
On the Complexity of Learning from Counterexamples and Membership Queries (abstract). COLT 1990: 391 - [c14]Wolfgang Maass, György Turán:
On the Complexity of Learning from Counterexamples and Membership Queries. FOCS 1990: 203-210 - [c13]Ajay Gupta, Wolfgang Maass:
Efficient Design of Boltzmann Machines. NIPS 1990: 825-831
1980 – 1989
- 1989
- [c12]Wolfgang Maass, Theodore A. Slaman:
The Complexity Types of Computable Sets. SCT 1989: 231-239 - [c11]Wolfgang Maass, Theodore A. Slaman:
Extensional Properties of Sets of Time Bounded Complexity (Extended Abstract). FCT 1989: 318-326 - [c10]Wolfgang Maass, György Turán:
On the Complexity of Learning From Counterexamples (Extended Abstract). FOCS 1989: 262-267 - 1988
- [j16]Klaus Sutner, Wolfgang Maass:
Motion Planning Among Time Dependent Obstacles. Acta Informatica 26(1/2): 93-122 (1988) - [j15]Martin Dietzfelbinger, Wolfgang Maass:
Lower Bound Arguments with "Inaccessible" Numbers. J. Comput. Syst. Sci. 36(3): 313-335 (1988) - [j14]Noga Alon, Wolfgang Maass:
Meanders and Their Applications in Lower Bounds Arguments. J. Comput. Syst. Sci. 37(2): 118-129 (1988) - [j13]Wolfgang Maass:
On the Use of Inaccessible Numbers and Order Indiscernibles in Lower Bound Arguments for Random Access Machines. J. Symb. Log. 53(4): 1098-1109 (1988) - [c9]Martin Dietzfelbinger, Wolfgang Maass:
The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape. ICALP 1988: 188-200 - [c8]András Hajnal, Wolfgang Maass, György Turán:
On the Communication Complexity of Graph Properties. STOC 1988: 186-191 - 1987
- [j12]Dorit S. Hochbaum, Wolfgang Maass:
Fast Approximation Algorithms for a Nonconvex Covering Problem. J. Algorithms 8(3): 305-323 (1987) - [j11]Wolfgang Maass, Amir Schorr:
Speed-Up of Turing Machines with One Work Tape and a Two-Way Input Tape. SIAM J. Comput. 16(1): 195-202 (1987) - [c7]András Hajnal, Wolfgang Maass, Pavel Pudlák, Mario Szegedy, György Turán:
Threshold circuits of bounded depth. FOCS 1987: 99-110 - [c6]Wolfgang Maass, Georg Schnitger, Endre Szemerédi:
Two Tapes Are Better than One for Off-Line Turing Machines. STOC 1987: 94-100 - 1986
- [j10]Wolfgang Maass:
On the Complexity of Nonconvex Covering. SIAM J. Comput. 15(2): 453-467 (1986) - [c5]Martin Dietzfelbinger, Wolfgang Maass:
two Lower Bound Arguments with "Inaccessible" Numbers. SCT 1986: 163-183 - [c4]Wolfgang Maass, Georg Schnitger:
An Optimal Lower Bound for Turing Machines with One Work Tape and a Two- way Input Tape. SCT 1986: 249-264 - [c3]Noga Alon, Wolfgang Maass:
Meanders, Ramsey Theory and Lower Bounds for Branching Programs. FOCS 1986: 410-417 - 1985
- [j9]Dorit S. Hochbaum, Wolfgang Maass:
Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI. J. ACM 32(1): 130-136 (1985) - [j8]Wolfgang Maass:
Variations on Promptly Simple Sets. J. Symb. Log. 50(1): 138-148 (1985) - 1984
- [j7]Wolfgang Maass:
On the Orbits of Hyperhypersimple Sets. J. Symb. Log. 49(1): 51-62 (1984) - [c2]Dorit S. Hochbaum, Wolfgang Maass:
Approximation Schemes for Covering and Packing Problems in Robotics and VLSI. STACS 1984: 55-62 - [c1]Wolfgang Maass:
Quadratic Lower Bounds for Deterministic and Nondeterministic One-Tape Turing Machines (Extended Abstract). STOC 1984: 401-408 - 1983
- [j6]Wolfgang Maass, Michael Stob:
The intervals of the lattice of recursively enumerable sets determined by major subsets. Ann. Pure Appl. Log. 24(2): 189-212 (1983) - [j5]Steven Homer, Wolfgang Maass:
Oracle-Dependent Properties of the Lattice of NP Sets. Theor. Comput. Sci. 24: 279-289 (1983) - 1982
- [j4]Wolfgang Maass:
Recursively Enumerable Generic Sets. J. Symb. Log. 47(4): 809-823 (1982)
1970 – 1979
- 1978
- [j3]Wolfgang Maass:
The Uniform Regular Set Theorem in a-Recursion Theory. J. Symb. Log. 43(2): 270-279 (1978) - 1977
- [j2]Wolfgang Maaß:
Eine Funktionalinterpretation der prädikativen Analysis. Arch. Math. Log. 18(1): 27-46 (1977) - [j1]Wolfgang Maass:
On minimal pairs and minimal degrees in higher recursion theory. Arch. Math. Log. 18(1): 169-186 (1977)
Coauthor Index
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