Wolfgang Maass: Publications

Talk at the 5th International Convention on the Mathematics Of Neuroscience and AI, Rome 2024
Talk at the Allen Institute, Seattle, October 2023
Talk at the conference From Neuroscience to Artificially Intelligent Systems (NAISys), April 2022
Talk at the Nature conference "At the Interface of Brain and Machine", April 2021
Keynote at NICE 2021: Biological inspiration for improving computing and learning in spiking neural networks
Berkeley Lecture 2018 on Networks of Spiking Neurons Learn to Learn and Remember
Berkeley Lectures 2018 on Computations in Networks of Neurons in the Brain Part I - Part II - Slides
Berkeley Lectures 2015 on Searching for Principles of Brain Computation - Slides
Waterloo Brain Day Lectures 2013 - video lecture

This list is also available as BiBTeX file.

[269]: Yujie Wu and Wolfgang Maass. A simple model for behavioral time scale synaptic plasticity provides content addressable memory with binary synapses and one-shot learning one-shot learning and robust recall with btsp, a biological synaptic plasticity rule. Nature Communications, 2025. (Link to bioRxiv PDF)
[268]: Guoqi Li, Lei Deng, Huajin Tang, Gang Pan, Yonghong Tian, Kaushik Roy, and Wolfgang Maass. Brain-inspired computing: A systematic survey and future trends. Proceedings of the IEEE, 112(6):544-584, 2024. (Link to IEEE Xplore version)
[267]: Wolfgang Maass. How can neuromorphic hardware attain brain-like functional capabilities?. National Science Review, 11(5):nwad301, 12 2023. (Link to PDF, Supplementary material PDF)
[266]: Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, and Wolfgang Maass. Fast learning without synaptic plasticity in spiking neural networks. Scientific Reports, 14(1):8557, 2024. Published on April 12, 2024. (Link to Scientific Reports version)
[265]: Christoph Stoeckl, Yukun Yang, and Wolfgang Maass. Local prediction-learning in high-dimensional spaces enables neural networks to plan. Nature Communications, 15, 03 2024. (Link to Nature Communications version)
[264]: J. Galván Fraile, Franz Scherr, José J. Ramasco, Anton Arkhipov, Wolfgang Maass, and Claudio R. Mirasso. Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations. PLOS Computational Biology, 20(3):1-27, 03 2024. (Link to PLOS version)
[263]: Guozhang Chen, Franz Scherr, and Wolfgang Maass. Data-based large-scale models provide a window into the organization of cortical computations. bioRxiv, 2023. (Link to bioRxiv PDF)
[262]: Guozhang Chen, Franz Scherr, and Wolfgang Maass. A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing. Science Advances, 8(44):eabq7592, 2022. (Link to Science Advances version)
[261]: Luke Y. Prince, Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, and Katharina Anna Wilmes. Current state and future directions for learning in biological recurrent neural networks: A perspective piece. Neurons, Behavior, Data analysis, and Theory, 1, 2022. (Link to NBDT version, Link to arXiv version PDF)
[260]: C. Kraisnikovic, W. Maass, and R. Legenstein. Spike-based symbolic computations on bit strings and numbers. Neuro-Symbolic Artificial Intelligence: The State of the Art, 342:214, 2022. P Hitzler, M K Sarker (Eds). (PDF). (Link to PDF)
[259]: A. Rao, P. Plank, A. Wild, and W. Maass. A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware. Nature Machine Intelligence, 4:467-479, 2022. (PDF). (Link to PDF)
[258]: F. Zenke, S. M. Bohté, C. Clopath, I. M. Com c sa, J. Göltz, W. Maass, T. Masquelier, R. Naud, E. O. Neftci, M. A. Petrovici, F. Scherr, and D. F. M. Goodman. Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron, 109(4):571-575, 2021. (PDF). (Link to PDF)
[257]: Franz Scherr and W. Maass. Learning-to-learn for neuromorphic hardware. Neuromorphic Computing and Engineering, 2:022501, 2022. in D.V. Christensen et. all 2022, A roadmap on neuromorphic computing and engineering. (PDF). (Link to PDF)
[256]: F. Scherr, C. Stoeckl, and W. Maass. One-shot learning with spiking neural networks. bioRxiv, 2020. (PDF). (Supplementary material PDF)
[255]: D. Salaj, A. Subramoney, C. Kraisnikovic, R. Legenstein G. Bellec, and W. Maass. Spike-frequency adaptation supports network computations on temporally dispersed information. eLife, 10:e65459, 2021. (PDF). Supplementary material PDF (Link to eLife version, Link to bioRxiv version PDF)
[254]: M. G. Müller, C. H. Papadimitriou, W. Maass, and R. Legenstein. A model for structured information representation in neural networks of the brain. eNeuro, 7(3), 2020. (Journal link to PDF)
[253]: C. Papadimitriou, S. Vempala, D. Mitropolsky, M. Collins, and W. Maass. Brain computation by assemblies of neurons. PNAS, 117(25):14464-14472, 2020. (Link to journal version PDF),(Draft on biorxiv PDF)
[252]: C. Stoeckl and W. Maass. Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. Nature Machine Intelligence, 3:230-238, 2021. Draft on arXiv. (PDF). Link to journal version PDF),(Link to arXiv version PDF)
[251]: C. Stoeckl and W. Maass. Recognizing images with at most one spike per neuron. arXiv:2001.01682v3, 2019. (PDF). (Link to arXiv version PDF)
[250]: A. Subramoney, F. Scherr, and W. Maass. Reservoirs learn to learn. In Reservoir Computing: Theory, Physical Implementations, and Applications, K. Nakajima and I. Fischer, editors. Springer, 2020. (PDF). Draft on arXiv:1909.07486v1
[249]: J. Kaiser, M. Hoff, A. Konle, J. C. V. Tieck, D. Kappel, D. Reichard, A. Subramoney, R. Legenstein, A. Roennau, W. Maass, and R. Dillmann. Embodied synaptic plasticity with online reinforcement learning. Frontiers in Neurorobotics, 13(81), 2019. (PDF). (Journal link to PDF)
[248]: G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass. A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications, 11:3625, 2020. (PDF). Supplementary material PDF, Supplementary movies PDF, (Commentary by Manneschi, L. & Vasilaki, E. (2020). An alternative to backpropagation through time. In Nature Machine Intelligence, 2(3), 155-156. PDF)
[247]: T. Bohnstingl, F. Scherr, C. Pehle, K. Meier, and W. Maass. Neuromorphic hardware learns to learn. Frontiers in Neuroscience, 13:483, 2019. (PDF).
[246]: G. Bellec, F. Scherr, E. Hajek, D. Salaj, R. Legenstein, and W. Maass. Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. arxiv.org/abs/1901.09049, January 2019. (PDF).
[245]: C. Liu, G. Bellec, B. Vogginger, D. Kappel, J. Partzsch, F. Neumärker, S. Höppner, W. Maass, S. B. Furber, R. Legenstein, and C. G. Mayr. Memory-efficient deep learning on a spinnaker 2 prototype. Frontiers in Neuroscience, 2018. (PDF).
[244]: N. Ananri, C. Daskalakis, W. Maass, C. H. Papadimitriou, A. Saberi, and S. Vempala. Smoothed analysis of discrete tensor decomposition and assemblies of neurons. 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada, 2018. (PDF).
[243]: G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass. Long short-term memory and learning-to-learn in networks of spiking neurons. 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada, 2018. (PDF).
[242]: R. Legenstein, W. Maass, C. H. Papadimitriou, and S. S. Vempala. Long term memory and the densest K-subgraph problem. In Proc. of Innovations in Theoretical Computer Science (ITCS), 2018. (PDF).
[241]: G. Bellec, D. Kappel, W. Maass, and R. Legenstein. Deep rewiring: training very sparse deep networks. International Conference on Learning Representations (ICLR), 2018. (PDF).
[240]: C. Pokorny, M. J. Ison, A. Rao, R. Legenstein, C. Papadimitriou, and W. Maass. STDP forms associations between memory traces in networks of spiking neurons. Cerebral Cortex, 30(3):952-968, 2020. (PDF). (Supplementary material PDF), (Journal link to PDF)
[239]: R. Legenstein, Z. Jonke, S. Habenschuss, and W. Maass. A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition. arXiv:1707.05182, 2017. (PDF).
[238]: Z. Jonke, R. Legenstein, S. Habenschuss, and W. Maass. Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs. Journal of Neuroscience, 37(35):8511-8523, 2017. (PDF).
[237]: D. Kappel, R. Legenstein, S. Habenschuss, M. Hsieh, and W. Maass. A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning. eNeuro, 2 April, 2018. (PDF).
[236]: M. A. Petrovici, S. Schmitt, J. Klähn, D. Stöckel, A. Schroeder, G. Bellec, J. Bill, O. Breitwieser, I. Bytschok, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, , S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, P. Müller, J. Partzsch, T. Pfeil, S. Schiefer, S. Scholze, A. Subramoney, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, R. Schüffny, C. Mayr, J. Schemmel, and K. Meier. Pattern representation and recognition with accelerated analog neuromorphic systems. arXiv:1703.06043, 2017. (PDF).
[235]: S. Schmitt, J. Klähn, G. Bellec, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, E. Müller, P. Müller, J. Partzsch, M. A. Petroviciy, S. Schiefer, S. Scholze, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, C. Mayr, R. Schüffny, J. Schemmel, and K. Meier. Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS Wafer-Scale System. In IEEE International Joint Conference on Neural Networks (IJCNN) 2017, pages 2227-2234, 2017. (PDF).
[234]: W. Maass, C. H. Papadimitriou, S. Vempala, and R. Legenstein. Brain computation: A computer science perspective. Draft of an invited contribution to Springer Lecture Notes in Computer Science, vol. 10000, 2017. (PDF).
[233]: R. Legenstein, C. H. Papadimitriou, S. Vempala, and W. Maass. Assembly pointers for variable binding in networks of spiking neurons. arXiv preprint arXiv:1611.03698, 2016. (PDF).
[232]: W. Maass. Energy-efficient neural network chips approach human recognition capabilities. PNAS, 113(40):doi/10.1073/pnas.1614109113, 2016. (PDF).
[231]: Z. Yu, D. Kappel, R. Legenstein, S. Song, F. Chen, and W. Maass. CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling. arXiv:1606.00157, 2016. (PDF).
[230]: D. Pecevski and W. Maass. Learning probabilistic inference through STDP. eNeuro, 2016. (PDF).
[229]: Z. Jonke, S. Habenschuss, and W. Maass. Solving constraint satisfaction problems with networks of spiking neurons. Front. Neurosci., 30 March, 2016. (Journal link to PDF)
[228]: W. Maass. Searching for principles of brain computation. Current Opinion in Behavioral Sciences (Special Issue on Computational Modelling), 11:81-92, 2016. (PDF).
[227]: W. Maass. To spike or not to spike: That is the question. Proceedings of the IEEE, 103(12):2219-2224, 2015. (PDF).
[226]: D. Kappel, S. Habenschuss, R. Legenstein, and W. Maass. Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring. In Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, pages 370-378. Curran Associates, Inc., 2015. (PDF).
[225]: D. Kappel, S. Habenschuss, R. Legenstein, and W. Maass. Network plasticity as Bayesian inference. PLOS Computational Biology, 11(11):e1004485, 2015. (Journal link to PDF)
[224]: J. Bill, L. Buesing, S. Habenschuss, B. Nessler, W. Maass, and R. Legenstein. Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition. PLOS ONE, 10(8):e0134356, 2015. (Journal link to PDF)
[223]: Z. Jonke, S. Habenschuss, and W. Maass. A theoretical basis for efficient computations with noisy spiking neurons. arXiv.org, arXiv:1412.5862, 2014. (PDF).
[222]: R. Legenstein and W. Maass. Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment. PLOS Computational Biology, 10(10):e1003859, 2014. (Journal link to PDF)
[221]: W. Maass. Noise as a resource for computation and learning in networks of spiking neurons. Special Issue of the Proc. of the IEEE on "Engineering Intelligent Electronic Systems based on Computational Neuroscience", 102(5):860-880, 2014. (PDF).
[220]: D. Kappel, B. Nessler, and W. Maass. STDP installs in winner-take-all circuits an online approximation to hidden Markov model learning. PLOS Computational Biology, 10(3):e1003511, 2014. (Journal link to PDF)
[219]: S. Habenschuss, Z. Jonke, and W. Maass. Stochastic computations in cortical microcircuit models. PLOS Computational Biology, 9(11):e1003311, 2013. (PDF). (Additional technical information PDF)
[218]: S. Klampfl and W. Maass. Emergence of dynamic memory traces in cortical microcircuit models through STDP. The Journal of Neuroscience, 33(28):11515-11529, 2013. (PDF).
[217]: B. Nessler, M. Pfeiffer, L. Buesing, and W. Maass. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLOS Computational Biology, 9(4):e1003037, 2013. (Journal link to PDF)
[216]: S. Habenschuss, H. Puhr, and W. Maass. Emergence of optimal decoding of population codes through STDP. Neural Computation, 25(6):1371-1407, 2013. (PDF).
[215]: E. A. Rueckert, G. Neumann, M. Toussaint, and W. Maass. Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers in Computational Neuroscience, 6:1-20, 2013. doi:10.3389/fncom.2012.00097. (PDF). (Journal link to PDF)
[214]: G. M. Hoerzer, R. Legenstein, and Wolfgang Maass. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. Cerebral Cortex, 24:677-690, 2014. (PDF). (Supplementary material PDF)
[213]: D. Probst, W. Maass, H. Markram, and M. O. Gewaltig. Liquid computing in a simplified model of cortical layer IV: Learning to balance a ball. In Proceedings of the 22nd International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2012, Alessandro E.P. Villa, Wlodzislaw Duch, Peter Erdi, Francesco Masulli, and Günther Palm, editors, volume 7552 of Lecture Notes in Computer Science, pages 209-216. Springer, 2012. (PDF). (Journal link to PDF)
[212]: H. Hauser, A. J. Ijspeert, R. M. Füchslin, R. Pfeifer, and W. Maass. The role of feedback in morphological computation with compliant bodies. Biological Cybernetics, published 06 Sept 2012. doi: 10.1007/s00422-012-0516-4. (PDF). (Journal link to PDF)
[211]: S. Klampfl, S. V. David, P. Yin, S. A. Shamma, and W. Maass. A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. Journal of Neurophysiology, 108:1366-1380, 2012. (PDF). (Journal link to abstract PDF)
[210]: M. Pfeiffer, M. Hartbauer, A. B. Lang, W. Maass, and H. Römer. Probing real sensory worlds of receivers with unsupervised clustering. PLoS ONE, 7(6):e37354. doi:10.1371, 2012. (PDF). (Journal link to PDF)
[209]: H. Hauser, A. J. Ijspeert, R. M. Füchslin, R. Pfeifer, and W. Maass. Towards a theoretical foundation for morphological computation with compliant bodies. Biological Cybernetics, 105(5-6):355-370, 2011. (PDF). (Journal link to PDF)
[208]: D. Pecevski, L. Büsing, and W. Maass. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Computational Biology, 7(12):e1002294, 2011. (Journal link to PDF)
[207]: L. Büsing, J. Bill, B. Nessler, and W. Maass. Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7(11):e1002211, 2011. (Journal link to PDF)
[206]: R. Legenstein and W. Maass. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. The Journal of Neuroscience, 31(30):10787-10802, 2011. (PDF). (Commentary by R. P. Costa and P. J. Sjöström in Frontiers in Synaptic Neuroscience PDF)
[205]: H. Hauser, G. Neumann, A. J. Ijspeert, and W. Maass. Biologically inspired kinematic synergies enable linear balance control of a humanoid robot. Biological Cybernetics, 104(4-5):235-249, 2011. (PDF). (Journal link to PDF)
[204]: M. J. Rasch, K. Schuch, N. K. Logothetis, and W. Maass. Statistical comparision 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, 105:757-778, 2011. (PDF). (Commentary by W.S. Anderson and B. Kreiman in Current Biology 2011 PDF)
[203]: J. Bill, K. Schuch, D. Brüderle, J. Schemmel, W. Maass, and K. Meier. Compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity. Frontiers in Computational Neuroscience, 4:1-14, 2010. doi:10.3389/fncom.2010.00129. (PDF). (Journal link to the PDF)
[202]: S. Klampfl and W. Maass. A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction. Neural Computation, 22(12):2979-3035, 2010. Epub 2010 Sep 21. (PDF).
[201]: R. Legenstein, S. M. Chase, A. B. Schwartz, and W. Maass. A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task. The Journal of Neuroscience, 30(25):8400-8410, 2010. (PDF).
[200]: D. Nikolic, S. Haeusler, W. Singer, and W. Maass. Distributed fading memory for stimulus properties in the primary visual cortex. PLoS Biology, 7(12):1-19, 2009. (Journal link to PDF)
[199]: R. Legenstein and W. Maass. An integrated learning rule for branch strength potentiation and STDP. 39th Annual Conference of the Society for Neuroscience, Program 895.20, Poster HH36, 2009.
[198]: S. Klampfl, S.V. David, P. Yin, S.A. Shamma, and W. Maass. Integration of stimulus history in information conveyed by neurons in primary auditory cortex in response to tone sequences. 39th Annual Conference of the Society for Neuroscience, Program 163.8, Poster T6, 2009.
[197]: S. Liebe, G. Hoerzer, N.K. Logothetis, W. Maass, and G. Rainer. Long range coupling between V4 and PF in theta band during visual short-term memory. 39th Annual Conference of the Society for Neuroscience, Program 652.20, Poster Y31, 2009.
[196]: S. Haeusler, K. Schuch, and W. Maass. Motif distribution and computational performance of two data-based cortical microcircuit templates. 38th Annual Conference of the Society for Neuroscience, Program 220.9, 2008.
[195]: L. Buesing and W. Maass. A spiking neuron as information bottleneck. Neural Computation, 22:1961-1992, 2010. (PDF).
[194]: M. Pfeiffer, B. Nessler, R. Douglas, and W. Maass. Reward-modulated Hebbian Learning of Decision Making. Neural Computation, 22:1399-1444, 2010. (PDF).
[193]: R. Legenstein, S. A. Chase, A. B. Schwartz, and W. Maass. Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning. In Proc. of NIPS 2009: Advances in Neural Information Processing Systems, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, volume 22, pages 1105-1113. MIT Press, 2010. (PDF).
[192]: S. Klampfl and W. Maass. Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks. In Proc. of NIPS 2009: Advances in Neural Information Processing Systems, volume 22, pages 988-996. MIT Press, 2010. (PDF).
[191]: B. Nessler, M. Pfeiffer, and W. Maass. STDP enables spiking neurons to detect hidden causes of their inputs. In Proc. of NIPS 2009: Advances in Neural Information Processing Systems, volume 22, pages 1357-1365. MIT Press, 2010. (PDF).
[190]: R. Legenstein, S. A. Chase, A. B. Schwartz, and W. Maass. A model for learning effects in motor cortex that may facilitate the brain control of neuroprosthetic devices. 38th Annual Conference of the Society for Neuroscience, Program 517.6, 2008.
[189]: W. Maass. Liquid state machines: Motivation, theory, and applications. In Computability in Context: Computation and Logic in the Real World, B. Cooper and A. Sorbi, editors, pages 275-296. Imperial College Press, 2010. (PDF).
[188]: G. Neumann, W. Maass, and J. Peters. Learning complex motions by sequencing simpler motion templates. In Proc. of the 26th Int. Conf. on Machine Learning (ICML 2009), Montreal, 2009. (PDF).
[187]: A. Steimer, W. Maass, and R. Douglas. Belief-propagation in networks of spiking neurons. Neural Computation, 21:2502-2523, 2009. (PDF).
[186]: D. Buonomano and W. Maass. State-dependent computations: Spatiotemporal processing in cortical networks. Nature Reviews in Neuroscience, 10(2):113-125, 2009. (PDF).
[185]: S. Haeusler, K. Schuch, and W. Maass. Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates. J. of Physiology (Paris), 103(1-2):73-87, 2009. (PDF).
[184]: B. Nessler, M. Pfeiffer, and W. Maass. Hebbian learning of Bayes optimal decisions. In Proc. of NIPS 2008: Advances in Neural Information Processing Systems, 21, 2009. MIT Press. (PDF).
[183]: R. Legenstein, D. Pecevski, and W. Maass. A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Computational Biology, 4(10):e1000180, 2008. (Journal link to PDF)
[182]: L. Buesing and W. Maass. Simplified rules and theoretical analysis for information bottleneck optimization and PCA with spiking neurons. In Proc. of NIPS 2007, Advances in Neural Information Processing Systems, volume 20. MIT Press, 2008. (PDF).
[181]: R. Legenstein, D. Pecevski, and W. Maass. Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity. In Proc. of NIPS 2007, Advances in Neural Information Processing Systems, volume 20, pages 881-888. MIT Press, 2008. (PDF).
[180]: G. Neumann, M. Pfeiffer, and W. Maass. Efficient continuous-time reinforcement learning with adaptive state graphs. In Proceedings of the 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2007, Warsaw (Poland). Springer (Berlin), 2007. in press. (PDF).
[179]: S. Klampfl, R. Legenstein, and W. Maass. Spiking neurons can learn to solve information bottleneck problems and extract independent components. Neural Computation, 21(4):911-959, 2009. (PDF).
[178]: W. Maass. Liquid computing. In Proceedings of the Conference CiE'07: COMPUTABILITY IN EUROPE 2007, Siena (Italy), Lecture Notes in Computer Science, pages 507-516. Springer (Berlin), 2007. (PDF).
[177]: S. Haeusler, W. Singer, W. Maass, and D. Nikolic. Superposition of information in large ensembles of neurons in primary visual cortex. 37th Annual Conference of the Society for Neuroscience, Program 176.2, Poster II23, 2007.
[176]: D. Sussillo, T. Toyoizumi, and W. Maass. Self-tuning of neural circuits through short-term synaptic plasticity. Journal of Neurophysiology, 97:4079-4095, 2007. (PDF). (Supplementary material PDF)
[175]: H. Hauser, G. Neumann, A. J. Ijspeert, and W. Maass. Biologically inspired kinematic synergies provide a new paradigm for balance control of humanoid robots. In Proceedings of the IEEE-RAS 7th International Conference on Humanoid Robots (Humanoids 2007), 2007. Best Paper Award. (PDF).
[174]: H. Jaeger, W. Maass, and J. Principe. Special issue on echo state networks and liquid state machines. Neural Networks, 20(3):287-289, 2007. (PDF).
[173]: M. J. Rasch, A. Gretton, Y. Murayama, W. Maass, and N. K. Logothetis. Inferring spike trains from local field potentials. Journal of Neurophysiology, 99:1461-1476, 2008. (PDF).
[172]: S. Klampfl, R. Legenstein, and W. Maass. Information bottleneck optimization and independent component extraction with spiking neurons. In Proc. of NIPS 2006, Advances in Neural Information Processing Systems, volume 19, pages 713-720. MIT Press, 2007. (PDF).
[171]: D. Nikolic, S. Haeusler, W. Singer, and W. Maass. Temporal dynamics of information content carried by neurons in the primary visual cortex. In Proc. of NIPS 2006, Advances in Neural Information Processing Systems, volume 19, pages 1041-1048. MIT Press, 2007. (PDF).
[170]: R. Legenstein and W. Maass. On the classification capability of sign-constrained perceptrons. Neural Computation, 20(1):288-309, 2008. (PDF).
[169]: W. Maass. Book review of "Imitation of life: how biology is inspiring computing" by Nancy Forbes. Pattern Analysis and Applications, 8(4):390-391, 2006. Springer (London). (PDF).
[168]: W. Maass, P. Joshi, and E. D. Sontag. Computational aspects of feedback in neural circuits. PLoS Computational Biology, 3(1):e165, 2007. (Journal link to PDF)
[167]: K. Uchizawa, R. Douglas, and W. Maass. Energy complexity and entropy of threshold circuits. In Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, ICALP (1) 2006, Venice, Italy, July 10-14, 2006, Part I, M. Bugliesi, B. Preneel, V. Sassone, and I. Wegener, editors, volume 4051 of Lecture Notes in Computer Science, pages 631-642. Springer, 2006. (PDF).
[166]: R. Legenstein and W. Maass. Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks, 20(3):323-334, 2007. (PDF).
[165]: R. Legenstein and W. Maass. What makes a dynamical system computationally powerful?. In New Directions in Statistical Signal Processing: From Systems to Brains, S. Haykin, J. C. Principe, T.J. Sejnowski, and J.G. McWhirter, editors, pages 127-154. MIT Press, 2007. (PDF).
[164]: W. Maass, P. Joshi, and E. D. Sontag. Principles of real-time computing with feedback applied to cortical microcircuit models. In Advances in Neural Information Processing Systems, Y. Weiss, B. Schoelkopf, and J. Platt, editors, volume 18, pages 835-842. MIT Press, 2006. (PDF).
[163]: K. Uchizawa, R. Douglas, and W. Maass. On the computational power of threshold circuits with sparse activity. Neural Computation, 18(12):2994-3008, 2006. (PDF).
[162]: S. Haeusler and W. Maass. A statistical analysis of information processing properties of lamina-specific cortical microcircuit models. Cerebral Cortex, 17(1):149-162, 2007. (PDF).
[161]: R. Legenstein and W. Maass. A criterion for the convergence of learning with spike timing dependent plasticity. In Advances in Neural Information Processing Systems, Y. Weiss, B. Schoelkopf, and J. Platt, editors, volume 18, pages 763-770. MIT Press, 2006. (PDF).
[160]: W. Maass, R. Legenstein, and N. Bertschinger. Methods for estimating the computational power and generalization capability of neural microcircuits. In Advances in Neural Information Processing Systems, L. K. Saul, Y. Weiss, and L. Bottou, editors, volume 17, pages 865-872. MIT Press, 2005. (PDF).
[159]: Y. Fregnac, M. Blatow, J.-P. Changeux, J. de Felipe, A. Lansner, W. Maass, D. A. McCormick, C. M. Michel, H. Monyer, E. Szathmary, and R. Yuste. UPs and DOWNs in cortical computation. In The Interface between Neurons and Global Brain Function, S. Grillner and A. M. Graybiel, editors, Dahlem Workshop Report 93, pages 393-433. MIT Press, 2006. (PDF).
[158]: P. Joshi and W. Maass. Movement generation with circuits of spiking neurons. Neural Computation, 17(8):1715-1738, 2005. (PDF).
[157]: W. Maass and H. Markram. Theory of the computational function of microcircuit dynamics. In The Interface between Neurons and Global Brain Function, S. Grillner and A. M. Graybiel, editors, Dahlem Workshop Report 93, pages 371-390. MIT Press, 2006. (PDF).
[156]: A. Kaske and W. 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. (PDF).
[155]: O. Melamed, W. Gerstner, W. Maass, M. Tsodyks, and H. Markram. Coding and learning of behavioral sequences. Trends in Neurosciences, 27(1):11-14, 2004. (PDF).
[154]: R. Legenstein, C. Naeger, and W. Maass. What can a neuron learn with spike-timing-dependent plasticity?. Neural Computation, 17(11):2337-2382, 2005. (PDF).
[153]: T. Natschlaeger and W. Maass. Dynamics of information and emergent computation in generic neural microcircuit models. Neural Networks, 18(10):1301-1308, 2005. (PDF).
[151]: P. Joshi and W. Maass. Movement generation and control with generic neural microcircuits. In Biologically Inspired Approaches to Advanced Information Technology. First International Workshop, BioADIT 2004, Lausanne, Switzerland, January 2004, Revised Selected Papers, A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, volume 3141 of Lecture Notes in Computer Science, pages 258-273. Springer Verlag, 2004. (PDF).
[150]: T. Natschlaeger and W. Maass. Information dynamics and emergent computation in recurrent circuits of spiking neurons. In Proc. of NIPS 2003, Advances in Neural Information Processing Systems, S. Thrun, L. Saul, and B. Schoelkopf, editors, volume 16, pages 1255-1262, Cambridge, 2004. MIT Press. (PDF).
[149]: W. Maass, T. Natschlaeger, and H. Markram. Computational models for generic cortical microcircuits. In Computational Neuroscience: A Comprehensive Approach, J. Feng, editor, chapter 18, pages 575-605. Chapman & Hall/CRC, Boca Raton, 2004. (PDF).
[148]: W. Maass, T. Natschlaeger, and H. Markram. Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology -- Paris, 98(4-6):315-330, 2004. (PDF).
[147]: W. Maass, T. Natschlaeger, and H. Markram. A model for real-time computation in generic neural microcircuits. In Proc. of NIPS 2002, Advances in Neural Information Processing Systems, S. Becker, S. Thrun, and K. Obermayer, editors, volume 15, pages 229-236. MIT Press, 2003. (PDF).
[146]: W. Maass, R. Legenstein, and H. Markram. A new approach towards vision suggested by biologically realistic neural microcircuit models. In Biologically Motivated Computer Vision. Proc. of the Second International Workshop, BMCV 2002, Tuebingen, Germany, November 22-24, 2002, H. H. Buelthoff, S. W. Lee, T. A. Poggio, and C. Wallraven, editors, volume 2525 of Lecture Notes in Computer Science, pages 282-293. Springer (Berlin), 2002. (PDF).
[145]: W. Maass. On the computational power of neural microcircuit models: Pointers to the literature. In Proc. of the International Conference on Artificial Neural Networks -- ICANN 2002, José R. Dorronsoro, editor, volume 2415 of Lecture Notes in Computer Science, pages 254-256. Springer, 2002. (PDF).
[144]: T. Natschlaeger, H. Markram, and W. Maass. Computer models and analysis tools for neural microcircuits. In Neuroscience Databases. A Practical Guide, R. Koetter, editor, chapter 9, pages 121-136. Kluwer Academic Publishers (Boston), 2003. (PDF).
[143]: T. Natschlaeger, W. Maass, and H. Markram. The "liquid computer": A novel strategy for real-time computing on time series. Special Issue on Foundations of Information Processing of TELEMATIK, 8(1):39-43, 2002. (PDF).
[141]: W. Maass. Computing with spikes. Special Issue on Foundations of Information Processing of TELEMATIK, 8(1):32-36, 2002. (PDF).
[140]: R. Legenstein, H. Markram, and W. Maass. Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons. Reviews in the Neurosciences (Special Issue on Neuroinformatics of Neural and Artificial Computation), 14(1-2):5-19, 2003. (PDF).
[139]: Peter L. Bartlett and W. Maass. Vapnik-Chervonenkis dimension of neural nets. In The Handbook of Brain Theory and Neural Networks, M. A. Arbib, editor, pages 1188-1192. MIT Press (Cambridge), 2nd edition, 2003. (PDF).
[138]: W. Maass and H. Markram. Temporal integration in recurrent microcircuits. In The Handbook of Brain Theory and Neural Networks, M. A. Arbib, editor, pages 1159-1163. MIT Press (Cambridge), 2nd edition, 2003. (PDF).
[137]: S. Haeusler, H. Markram, and W. Maass. Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts. Complexity (Special Issue on Complex Adaptive Systems), 8(4):39-50, 2003. (PDF).
[136]: T. Natschlaeger and W. Maass. Spiking neurons and the induction of finite state machines. Theoretical Computer Science: Special Issue on Natural Computing, 287:251-265, 2002. (PDF).
[135]: W. Maass and H. Markram. On the computational power of circuits of spiking neurons. Journal of Computer and System Sciences, 69(4):593-616, 2004. (PDF).
[134]: R. A. Legenstein and W. Maass. Optimizing the layout of a balanced tree. Technical Report, 2001. (PDF).
[133]: R. A. Legenstein and W. Maass. Neural circuits for pattern recognition with small total wire length. Theoretical Computer Science, 287:239-249, 2002. (PDF).
[132]: R. A. Legenstein and W. Maass. Wire length as a circuit complexity measure. Journal of Computer and System Sciences, 70:53-72, 2005. (PDF).
[131]: G. Steinbauer, R. Koholka, and W. Maass. A very short story about autonomous robots. Special Issue on Foundations of Information Processing of TELEMATIK, 8(1):26-29, 2002. (PDF).
[130]: W. Maass, T. Natschlaeger, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11):2531-2560, 2002. (PDF).
[129a]: W. Maass. wetware (English version). In TAKEOVER: Who is Doing the Art of Tomorrow (Ars Electronica 2001), pages 148-152. Springer, 2001. (PDF).
[129b]: W. Maass. wetware (deutsche Version). In TAKEOVER: Who is Doing the Art of Tomorrow (Ars Electronica 2001), pages 153-157. Springer, 2001. (PDF).
[128]: W. Maass, G. Steinbauer, and R. Koholka. Autonomous fast learning in a mobile robot. In Sensor Based Intelligent Robots. International Workshop, Dagstuhl Castle, Germany, October 15-25, 2000, Selected Revised Papers, G. D. Hager, H. I. Christensen, H. Bunke, and R. Klein, editors, volume 2238 of lncs, pages 345-356, 2002. (PDF).
[127]: P. Auer, H. Burgsteiner, and W. Maass. Reducing communication for distributed learning in neural networks. In http://www.springer.de/comp/lncs/index.html - Proc. of the International Conference on Artificial Neural Networks -- ICANN 2002, José R. Dorronsoro, editor, volume 2415 of Lecture Notes in Computer Science, pages 123-128. Springer, 2002. (PostScript). (PDF).
[126]: P. Auer, H. Burgsteiner, and W. Maass. A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks, 21(5):786-795, 2008. (PDF).
[125]: T. Natschlaeger and W. Maass. Computing the optimally fitted spike train for a synapse. Neural Computation, 13(11):2477-2494, 2001. (PostScript). (PDF).
[124]: T. Natschlaeger, W. Maass, and A. Zador. Efficient temporal processing with biologically realistic dynamic synapses. Network: Computation in Neural Systems, 12:75-87, 2001. (PostScript). (PDF).
[123a]: W. Maass. Neural computation: a research topic for theoretical computer science? Some thoughts and pointers. In Current Trends in Theoretical Computer Science, Entering the 21th Century, Rozenberg G., Salomaa A., and Paun G., editors, pages 680-690. World Scientific Publishing, 2001. (PostScript). (PDF).
[123b]: W. Maass. Neural computation: a research topic for theoretical computer science? Some thoughts and pointers. In Bulletin of the European Association for Theoretical Computer Science (EATCS), volume 72, pages 149-158, 2000.
[122]: R. A. Legenstein and W. Maass. Foundations for a circuit complexity theory of sensory processing. In Proc. of NIPS 2000, Advances in Neural Information Processing Systems, T. K. Leen, T. G. Dietterich, and V. Tresp, editors, volume 13, pages 259-265, Cambridge, 2001. MIT Press. (PDF).
[121]: T. Natschlaeger and W. Maass. Finding the key to a synapse. In Advances in Neural Information Processing Systems (NIPS '2000), Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, volume 13, pages 138-144, Cambridge, 2001. MIT Press. (PostScript). (PDF). The poster presented at NIPS is available as PDF file.
[120]: W. Maass, A. Pinz, R. Braunstingl, G. Wiesspeiner, T. Natschlaeger, O. Friedl, and H. Burgsteiner. Konstruktion von lernfaehigen mobilen Robotern im Studentenwettbewerb ``Robotik 2000'' an der Technischen Universitaet Graz. in: Telematik, pages 20-24, 2000. (PostScript). (PDF).
[119]: W. Maass and H. Markram. Synapses as dynamic memory buffers. Neural Networks, 15:155-161, 2002. (PostScript). (PDF).
[118]: W. Maass. Spike trains -- im Rhythmus neuronaler Zellen. In Katalog der steirischen Landesausstellung gr2000az, R. Kriesche H. Konrad, editor, pages 36-42. Springer Verlag, 2000.
[117]: W. Maass. Lernende Maschinen. In Katalog der steirischen Landesausstellung gr2000az, R. Kriesche H. Konrad, editor, pages 50-56. Springer Verlag, 2000.
[116]: W. Maass. Neural computation with winner-take-all as the only nonlinear operation. In Advances in Information Processing Systems, Sara A. Solla, Todd K. Leen, and Klaus-Robert Mueller, editors, volume 12, pages 293-299. MIT Press (Cambridge), 2000. (PostScript). (PDF).
[115]: T. Natschlaeger and W. Maass. Fast analog computation in networks of spiking neurons using unreliable synapses. In ESANN'99 Proceedings of the European Symposium on Artificial Neural Networks, pages 417-422, Bruges, Belgium, 1999. (PostScript). (PDF).
[114]: W. Maass. Computation with spiking neurons. In The Handbook of Brain Theory and Neural Networks, M. A. Arbib, editor, pages 1080-1083. MIT Press (Cambridge), 2nd edition, 2003. (PostScript). (PDF).
[113]: W. Maass. On the computational power of winner-take-all. Neural Computation, 12(11):2519-2535, 2000. (PostScript). (PDF).
[112]: W. Maass and T. Natschlaeger. Emulation of Hopfield networks with spiking neurons in temporal coding. In Computational Neuroscience: Trends in Research, J. M. Bower, editor, pages 221-226. Plenum Press, 1998. (PostScript). (PDF).
[111]: T. Natschlaeger, W. Maass, E. D. Sontag, and A. Zador. Processing of time series by neural circuits with biologically realistic synaptic dynamics. In Advances in Neural Information Processing Systems 2000 (NIPS '2000), Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, volume 13, pages 145-151, Cambridge, 2001. MIT Press. (PostScript). (PDF). The poster presented at NIPS is available as PDF file.
[110]: W. Maass. Paradigms for computing with spiking neurons. In Models of Neural Networks. Early Vision and Attention, J. L. van Hemmen, J. D. Cowan, and E. Domany, editors, volume 4, chapter 9, pages 373-402. Springer (New York), 2002. (PostScript). (PDF).
[109]: W. Maass and E. D. Sontag. A precise characterization of the class of languages recognized by neural nets under Gaussian and other common noise distributions. In Advances in Neural Information Processing Systems, M. S. Kearns, S. S. Solla, and D. A. Cohn, editors, volume 11, pages 281-287. MIT Press (Cambridge), 1999. (PostScript). (PDF).
[108]: W. Maass. Das menschliche Gehirn -- nur ein Rechner?. In Zur Kunst des Formalen Denkens, R. E. Burkard, W. Maass, and P. Weibel, editors, pages 209-233. Passagen Verlag (Wien), 2000. (PostScript). (PDF).
[107]: W. Maass and E. D. Sontag. Neural systems as nonlinear filters. Neural Computation, 12(8):1743-1772, 2000. (PostScript). (PDF).
[106]: P. Auer and W. Maass. Introduction to the special issue on computational learning theory. Algorithmica, 22(1/2):1-2, 1998. (PDF).
[105]: W. Maass. Spiking neurons. In Proceedings of the ICSC/IFAC Symposium on Neural Computation 1998 (NC'98), pages 16-20. ICSC Academic Press (Alberta), 1998. Invited talk.
[104]: W. Maass. Models for fast analog computation with spiking neurons. In Proc. of the International Conference on Neural Information Processing 1998 (ICONIP'98) in Kytakyusyu, Japan, pages 187-188. IOS Press (Amsterdam), 1998. Invited talk at the special session on ``Dynamic Brain''.
[103]: W. Maass. On the role of time and space in neural computation. In Proc. of the Federated Conference of CLS'98 and MFCS'98, Mathematical Foundations of Computer Science 1998, volume 1450 of Lecture Notes in Computer Science, pages 72-83. Springer (Berlin), 1998. Invited talk. (PostScript). (PDF).
[102]: W. Maass and T. Natschlaeger. A model for fast analog computation based on unreliable synapses. Neural Computation, 12(7):1679-1704, 2000. (PostScript). (PDF).
[101]: W. Maass and A. Zador. Computing and learning with dynamic synapses. In Pulsed Neural Networks, W. Maass and C. Bishop, editors, pages 321-336. MIT-Press (Cambridge), 1998. (PostScript). (PDF).
[100]: W. Maass. Computing with spiking neurons. In Pulsed Neural Networks, W. Maass and C. M. Bishop, editors, pages 55-85. MIT Press (Cambridge), 1999. (PostScript). (PDF).
[99]: W. Maass and T. Natschlaeger. Associative memory with networks of spiking neurons in temporal coding. In Neuromorphic Systems: Engineering Silicon from Neurobiology, L. S. Smith and A. Hamilton, editors, pages 21-32. World Scientific, 1998. (PostScript). (PDF).
[98]: W. Maass and B. Ruf. On computation with pulses. Information and Computation, 148:202-218, 1999. (PostScript). (PDF).
[97a]: W. Maass. On the relevance of time in neural computation and learning. Theoretical Computer Science, 261:157-178, 2001. (PDF).
[97b]: W. Maass. On the relevance of time in neural computation and learning. In Proc. of the 8th International Conference on Algorithmic Learning Theory in Sendai (Japan), M. Li and A. Maruoka, editors, volume 1316 of Lecture Notes in Computer Science, pages 364-384. Springer (Berlin), 1997. (PostScript). (PDF).
[96]: W. Maass and M. Schmitt. On the complexity of learning for spiking neurons with temporal coding. Information and Computation, 153:26-46, 1999. (PostScript). (PDF).
[95]: W. Maass and E. Sontag. Analog neural nets with Gaussian or other common noise distributions cannot recognize arbitrary regular languages. Neural Computation, 11:771-782, 1999. (PostScript). (PDF).
[94a]: W. Maass and A. M. Zador. Dynamic stochastic synapses as computational units. Neural Computation, 11(4):903-917, 1999. (PostScript). (PDF).
[94b]: W. Maass and A. M. Zador. Dynamic stochastic synapses as computational units. In Advances in Neural Processing Systems, volume 10, pages 194-200. MIT Press (Cambridge), 1998. (PostScript). (PDF).
[93]: W. Maass and T. Natschlaeger. Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding. Network: Computation in Neural Systems, 8(4):355-371, 1997. (PostScript). (PDF).
[92]: W. Maass and M. Schmitt. On the complexity of learning for a spiking neuron. In Proc. of the 10th Conference on Computational Learning Theory 1997, pages 54-61. ACM-Press (New York), 1997. See also Electronic Proc. of the Fifth International Symposium on Artificial Intelligence and Mathematics (http://rutcor.rutgers.edu/~amai). (PDF).
[91]: W. Maass. A simple model for neural computation with firing rates and firing correlations. Network: Computation in Neural Systems, 9(3):381-397, 1998. (PDF).
[90]: W. Maass. Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons. In Advances in Neural Information Processing Systems, M. Mozer, M. I. Jordan, and T. Petsche, editors, volume 9, pages 211-217. MIT Press (Cambridge), 1997. (PostScript). (PDF).
[89]: W. Maass. Analog computations with temporal coding in networks of spiking neurons. In Spatiotemporal Models in Biological and Artificial Systems, F. L. Silva, editor, pages 97-104. IOS-Press, 1997.
[88]: W. Maass and P. Weibel. Ist die Vertreibung der Vernunft reversibel? Ueberlegungen zu einem Wissenschafts- und Medienzentrum. In Jenseits von Kunst, P. Weibel, editor, pages 745-747. Passagen Verlag, 1997. (PostScript). (PDF).
[87a]: W. Maass and P. Orponen. On the effect of analog noise in discrete-time analog computations. Neural Computation, 10:1071-1095, 1998. (PostScript). (PDF).
[87b]: W. Maass and P. Orponen. On the effect of analog noise in discrete-time analog computations. In Advances in Neural Information Processing Systems, M. Mozer, M. I. Jordan, and T. Petsche, editors, volume 9, pages 218-224. MIT Press (Cambridge), 1997. (PostScript). (PDF).
[85a]: W. Maass. Networks of spiking neurons: the third generation of neural network models. Neural Networks, 10:1659-1671, 1997. (PostScript). (PDF).
[85b]: W. Maass. Networks of spiking neurons: the third generation of neural network models. In Proc. of the 7th Australian Conference on Neural Networks 1996 in Canberra, Australia, pages 1-10, 1996. (PDF).
[84]: P. Auer, S. Kwek, W. Maass, and M. K. Warmuth. Learning of depth two neural nets with constant fan-in at the hidden nodes. In Proc. of the 9th Conference on Computational Learning Theory 1996, pages 333-343. ACM-Press (New York), 1996. (PostScript). (PDF).
[83]: W. Maass. A model for fast analog computations with noisy spiking neurons. In Computational Neuroscience: Trends in research, James Bower, editor, pages 123-127, 1997. (PostScript). (PDF).
[82]: W. Maass. Fast sigmoidal networks via spiking neurons. Neural Computation, 9:279-304, 1997. (PostScript). (PDF).
[81]: W. Maass. Neuronale Netze und maschinelles Lernen am Institut fuer Grundlagen der Informationsverarbeitung an der Technischen Universitaet Graz. Telematik, 2:53-60, 1995. (PDF).
[80]: W. Maass. On the computational power of noisy spiking neurons. In Advances in Neural Information Processing Systems, D. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, volume 8, pages 211-217. MIT Press (Cambridge), 1996. (PostScript). (PDF).
[79]: W. Maass and B. Ruf. On the relevance of the shape of postsynaptic potentials for the computational power of networks of spiking neurons. In Proc. of the International Conference on Artificial Neural Networks ICANN, pages 515-520, Paris, 1995. EC2&Cie. (PostScript). (PDF).
[78]: W. Maass and G. Turan. On learnability and predicate logic (extended abstract). In Proc. of the 4th Bar-Ilan Symposium on Foundations of Artificial Intelligence (BISFAI'95), pages 126-136, Jerusalem, 1995. (PDF).
[77]: P. Auer, R. C. Holte, and W. Maass. Theory and applications of agnostic PAC-learning with small decision trees. In Proc. of the 12th International Machine Learning Conference, Tahoe City (USA), pages 21-29. Morgan Kaufmann (San Francisco), 1995. (PostScript). (PDF).
[76]: W. Maass. Analog computations on networks of spiking neurons (extended abstract). In Proc. of the 7th Italian Workshop on Neural Nets 1995, pages 99-104. World Scientific (Singapore), 1996. (PDF).
[75]: W. Maass. Lower bounds for the computational power of networks of spiking neurons. Neural Computation, 8(1):1-40, 1996. (PostScript). (PDF).
[74]: D. P. Dobkin, D. Gunopulos, and W. Maass. Computing the maximum bichromatic discrepancy, with applications to computer graphics and machine learning. Journal of Computer and System Sciences, 52(3):453-470, June 1996. (PostScript). (PDF).
[73a]: W. Maass and M. Warmuth. Efficient learning with virtual threshold gates. Information and Computation, 141(1):66-83, 1998. (PostScript). (PDF).
[73b]: W. Maass and M. Warmuth. Efficient learning with virtual threshold gates. In Proc. of the 12th International Machine Learning Conference, Tahoe City, USA, Morgan Kaufmann (San Francisco), editor, pages 378-386, 1995.
[72]: W. Maass. On the computational complexity of networks of spiking neurons. In Advances in Neural Information Processing Systems, G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, volume 7, pages 183-190. MIT Press (Cambridge), 1995. (PostScript). (PDF).
[71]: W. Maass. On the complexity of learning on neural nets. In Computational Learning Theory: EuroColt'93, J. Shawe-Taylor and M. Anthony, editors, pages 1-17. Oxford University Press (Oxford), 1994. (PostScript). (PDF).
[70]: W. Maass. Efficient agnostic PAC-learning with simple hypotheses. In Proc. of the 7th Annual ACM Conference on Computational Learning Theory, pages 67-75, 1994. (PostScript). (PDF).
[69]: W. Maass. Computing on analog neural nets with arbitrary real weights. In Theoretical Andvances in Neural Computation and Learning, V. P. Roychowdhury, K. Y. Siu, and A. Orlitsky, editors, pages 153-172. Kluwer Academics Publisher (Boston), 1994. (PostScript). (PDF).
[68]: W. Maass. Vapnik-Chervonenkis dimension of neural nets. In The Handbook of Brain Theory and Neural Networks, M. A. Arbib, editor, pages 1000-1003. MIT Press (Cambridge), 1995. (PostScript). (PDF).
[67]: W. Maass. Perspectives of current research about the complexity of learning on neural nets. In Theoretical Advances in Neural Computation and Learning, V. P. Roychowdhury, K. Y. Siu, and A. Orlitsky, editors, pages 295-336. Kluwer Academic Publishers (Boston), 1994. (PostScript). (PDF).
[66a]: W. Maass. Neural nets with superlinear VC-dimension. Neural Computation, 6:877-884, 1994. (PostScript). (PDF).
[66b]: W. Maass. Neural nets with superlinear VC-dimension. In Proceedings of the International Conference on Artificial Neural Networks 1994 (ICANN'94), pages 581-584. Springer (Berlin), 1994. (PDF).
[65a]: W. Maass. Agnostic PAC-learning of functions on analog neural nets. Neural Computation, 7:1054-1078, 1995. (PostScript). (PDF).
[65b]: W. Maass. Agnostic PAC-learning of functions on analog neural nets. In Advances in Neural Information Processing Systems, volume 7, pages 311-318, 1995. (PostScript). (PDF).
[64a]: P. Auer, P. M. Long, W. Maass, and G. J. Woeginger. On the complexity of function learning. Machine Learning, 18:187-230, 1995. Invited paper in a special issue of Machine Learning. (PDF).
[64b]: P. Auer, P. M. Long, W. Maass, and G. J. Woeginger. On the complexity of function learning. In Proceedings of the 5th Annual ACM Conference on Computational Learning Theory, pages 392-401, 1993.
[63]: Z. Chen and W. Maass. On-line learning of rectangles and unions of rectangles. Machine Learning, 17:201-223, 1994. Invited paper for a special issue of Machine Learning. (PostScript). (PDF).
[62a]: W. Maass. Bounds for the computational power and learning complexity of analog neural nets. SIAM J. on Computing, 26(3):708-732, 1997. (PostScript). (PDF).
[62b]: W. Maass. Bounds for the computational power and learning complexity of analog neural nets. In Proceedings of the 25th Annual ACM Symposium on Theory Computing, pages 335-344, 1993. (PostScript). (PDF).
[61]: W. Maass, G. Schnitger, E. Szemeredi, and G. Turan. Two tapes versus one for off-line Turing machines. Computational Complexity, 3:392-401, 1993. (PDF).
[60]: Z. Chen and W. Maass. A solution of the credit assignment problem in the case of learning rectangles. In Proceedings of the 3rd Int. Workshop on Analogical and Inductive Inference, volume 642 of Lecture Notes in Artificial Intelligence, pages 26-34. Springer, 1992.
[59]: Z. Chen and W. Maass. On-line learning of rectangles. In Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pages 16-28, 1992. (PDF).
[58a]: W. Maass, G. Schnitger, and E. Sontag. A comparison of the computational power of sigmoid and boolean threshold circuits. In Theoretical Advances in Neural Computation and Learning, V. P. Roychowdhury, K. Y. Siu, and A. Orlitsky, editors, pages 127-151. Kluwer Academic Publishers (Boston), 1994. (PDF).
[58b]: W. Maass, G. Schnitger, and E. Sontag. On the computational power of sigmoid versus boolean threshold circuits. In Proc. of the 32nd Annual IEEE Symposium on Foundations of Computer Science 1991, pages 767-776, 1991. (PDF).
[57]: W. Maass. On-line learning with an oblivious environment and the power of randomization. In Proceedings of the 4th Annual ACM Workshop on Computational Learning Theory, pages 167-175. Morgan Kaufmann (San Mateo), 1991. (PDF).
[56]: W. Maass and G. Turan. Algorithms and lower bounds for on-line learning of geometrical concepts. Machine Learning, 14:251-269, 1994. (PDF).
[55a]: W. J. Bultman and W. Maass. Fast identification of geometric objects with membership queries. Information and Computation, 118:48-64, 1995. (PDF).
[55b]: W. J. Bultman and W. Maass. Fast identification of geometric objects with membership queries. In Proceedings of the 4th Annual ACM Workshop on Computational Learning Theory,, pages 337-353, 1991.
[54]: W. Maass and G. Turan. Lower bound methods and separation results for on-line learning models. Machine Learning, 9:107-145, 1992. Invited paper for a special issue of Machine Learning. (PDF).
[53]: A. Gupta and W. Maass. A method for the efficient design of Boltzmann machines for classification problems. In Advances in Neural Information Processing Systems, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, volume 3, pages 825-831. Morgan Kaufmann, (San Mateo), 1991. (PDF).
[52]: W. Maass and T. A. Slaman. Splitting and density for the recursive sets of a fixed time complexity. In Proceedings of a Workshop on Logic from Computer Science, Y. N. Moschovakis, editor, pages 359-372. Springer (Berlin), 1991. (PDF).
[51]: W. Maass and T. A. Slaman. The complexity types of computable sets. Journal of Computer and System Sciences, 44:168-192, 1992. Invited paper for a special issue of the J. Comput. Syst. Sci. (PDF).
[50]: W. Maass and T. A. Slaman. On the relationship between the complexity, the degree, and the extension of a computable set. In Proceedings of the 1989 Recursion Theory Week Oberwolfach, pages 297-322. Springer (Berlin), 1990. (PDF).
[49]: W. Maass and G. Turan. How fast can a threshold gate learn. In Computational Learning Theory and Natural Learning System: Constraints and Prospects, S. J. Hanson, G. A. Drastal, and R. L. Rivest, editors, pages 381-414. MIT Press (Cambridge), 1994. (PDF).
[48]: W. Maass and G. Turan. On the complexity of learning from counterexamples and membership queries. In Proceedings of the 31th Annual IEEE Symposium on Foundations of Computer Science, pages 203-210, 1990. (PDF).
[47]: A. Hajnal, W. Maass, P. Pudlak, M. Szegedy, and G. Turan. Threshold circuits of bounded depth. J. Comput. System Sci., 46:129-154, 1993. (PDF).
[46]: M. Dietzfelbinger and W. Maass. The complexity of matrix transposition on one-tape off-line Turing machines with output tape. Theoretical Computer Science, 108:271-290, 1993. (PDF).
[45]: M. Dietzfelbinger, W. Maass, and G. Schnitger. The complexity of matrix transposition on one-tape off-line Turing machines. Theoretical Computer Science, 82:113-129, 1991. (PDF).
[44]: W. Maass and G. Turán. On the complexity of learning from counterexamples (extended abstract). In Proceedings of the 30th Annual IEEE Symposium on Foundations of Computer Science, pages 262-267, 1989. (PDF).
[43]: W. Maass and T. A. Slaman. Extensional properties of sets of time bounded complexity (extended abstract). In Proceedings of the 7th International Conference on Fundamentals of Computation Theory, volume 380 of Lecture Notes in Computer Science, pages 318-326. Springer (Berlin), 1989. (PDF).
[42]: W. Maass and T. A. Slaman. The complexity types of computable sets (extended abstract). In Proceedings of the 4th Annual Conference on Structure in Complexity Theory, pages 231-239. IEEE Computer Society Press (Washington), 1989. (PDF).
[41]: W. Maass and T. A. Slaman. Some problems and results in the theory of actually computable functions. In Proceedings of the Logic Colloquium '88, Padova, Italy, Ferro, Bonotto, Valentini, and Zanardo, editors, pages 79-89. Elsevier Science Publishers (North-Holland), 1989. (PDF).
[40]: W. Maass and K. Sutner. Motion planning among time dependent obstacles. Acta Informatica, 26:93-122, 1988. (PDF).
[39]: M. Dietzfelbinger and W. Maass. The complexity of matrix transposition on one-tape off-line Turing machines with output tape. In Proceedings of the 15th International Colloquium on Automata, Languages and Programming, volume 317 of Lecture Notes in Computer Science, pages 188-200. Springer (Berlin), 1988. (PDF).
[38]: A. Hajnal, W. Maass, and G. Turan. On the communication complexity of graph properties. In Proceedings of the 20th Annual ACM Symposium on Theory of Computing, pages 186-191, 1988. (PDF).
[37]: N. Alon and W. Maass. Meanders and their applications in lower bound arguments. J. Comput. System Sci., 37:118-129, 1988. Invited paper for a special issue of J. Comput. System Sci. (PDF).
[36]: M. Dietzfelbinger and W. Maass. Lower bound arguments with ``inaccesible'' numbers. Journal of Computer and System Sciences, 36:313-335, 1988. (PDF).
[35]: W. Maass. On the use of inaccessible numbers and order indiscernibles in lower bound arguments for random access machines. J. Symbolic Logic, 53:1098-1109, 1988. (PDF).
[34]: A. Hajnal, W. Maass, P. Pudlak, M. Szegedy, and G. Turan. Threshold circuits of bounded depth. Journal of Computer and System Sciences, 46:129-154, 1993. (PDF).
[33]: W. Maass, G. Schnitger, and E. Szemeredi. Two tapes are better than one for off-line turing machines. In Proceedings of the 19th Annual ACM Symposium on Theory of Computing, pages 94-100, 1987. (PDF).
[32]: D. Hochbaum and W. Maass. Fast approximation algorithms for a nonconvex covering problem. J. Algorithms, 8:305-323, 1987. (PDF).
[31]: W. Maass and A. Schorr. Speed-up of Turing machines with one work tape and a two-way input tape. SIAM J. Comput., 16:195-202, 1987. (PDF).
[30]: N. Alon and W. Maass. Meanders, ramsey's theorem and lower bounds for branching programs. Proceedings of the 27th Annual IEEE Symposium on Foundations of Computer Science, pages 410-417, 1986. (PDF).
[29]: M. Dietzfelbinger and W. Maass. Two lower bound arguments with ``inaccessible'' numbers. In Proceedings of the Structure in Complexity Theory Conference, Berkeley 1986, volume 223 of Lecture Notes in Computer Science, pages 163-183. Springer (Berlin), 1986. (PDF).
[28]: W. Maass and G. Schnitger. An optimal lower bound for Turing machines with one work tape and two-way input tape. In Proceedings of the Structure in Complexity Theory Conference, Berkeley 1986, volume 223 of Lecture Notes in Computer Science, pages 249-264. Springer (Berlin), 1986. (PDF).
[27]: W. Maass. On the complexity of nonconvex covering. SIAM J. Computing, 15:453-467, 1986. (PDF).
[26]: W. Maass. Are recursion theoretic arguments useful in complexity theory. In Proceedings of the International Conference on Logic, Methodology and Philosphy of Science, Salzburg 1983, pages 141-158. North-Holland (Amsterdam), 1986. (PDF).
[25]: W. Maass. Combinatorial lower bound arguments for deterministic and nondeterministic Turing machines. Transactions of the American Mathematical Society, 292(2):675-693, 1985. hard copy. (PDF).
[24]: M. Dietzfelbinger and W. Maass. Strong reducibilities in alpha- and beta-recursion theory. In Proceedings of the 1984 Recursion Theory Week Oberwolfach, Germany, volume 1141 of Lecture Notes in Mathematics, pages 89-120. Springer (Berlin), 1985. (PDF).
[23]: W. Maass. Major subsets and automorphisms of recursively enumerable sets. Proceedings of Symposia in Pure Mathematics, 42:21-32, 1985. (PDF).
[22]: D. Hochbaum and W. Maass. Approximation schemes for covering and packing problems in image processing and VLSI. J. Assoc. Comp. Mach., 32:130-136, 1985. (PDF).
[21]: W. Maass. Variations on promptly simple sets. J. Symbolic Logic, 50:138-148, 1985. (PDF).
[20]: W. Maass. Quadratic lower bounds for deterministic and nondeterministic one-tape Turing machines. In Proceedings of 16th Annual ACM Symp. on Theory of Computing, pages 401-408, 1984. (PDF).
[19]: D. Hochbaum and W. Maass. Approximation schemes for covering and packing problems in robotics and VLSI (extended abstract). In Proceedings of Symp. on Theoretical Aspects of Computer Science (Paris 1984), volume 166 of Lecture Notes in Computer Science, pages 55-62. Springer (Berlin), 1984. (PDF).
[18]: W. Maass. On the orbits of hyperhypersimple sets. J. Symbolic Logic, 49:51-62, 1984. (PDF).
[17]: S. Homer and W. Maass. Oracle dependent properties of the lattice of NP-sets. Theoretical Computer Science, 24:279-289, 1983. (PDF).
[16]: W. Maass and M. Stob. The intervals of the lattice of recursively enumerable sets determined by major subsets. Ann. of Pure and Applied Logic, 24:189-212, 1983. (PDF).
[15]: W. Maass. Characterization of recursively enumerable sets with supersets effectively isomorphic to all recursively enumerable sets. Trans. Amer. Math. Soc., 279:311-336, 1983. (PDF).
[14]: W. Maass. Recursively enumerable generic sets. The Journal of Symbolic Logic, 47:809-823, 1983. (PDF).
[13]: W. Maass. Recursively invariant beta-recursion theory. Ann. of Math. Logic, 21:27-73, 1981. (PDF).
[12]: W. Maass. A countable basis for sigma-one-two sets and recursion theory on aleph-one. Proceedings Amer. Math. Soc., 82:267-270, 1981. (PDF).
[11]: W. Maass, A. Shore, and M. Stob. Splitting properties and jump classes. Israel J. Math., 39:210-224, 1981. (PDF).
[10]: W. Maass. Recursively invariant beta-recursion theory -- a preliminary survey. In Proceedings of the Conf. on Recursion Theory and Computational Complexity, G. Lolli, editor, pages 229-236. Liguori editore (Napoli), 1981. (PDF).
[9]: W. Maass. On alpha- and beta-recursively enumerable degrees. Ann. of Math. Logic, 16:205-231, 1979. (PDF).
[8]: W. Maass. High alpha-recursively enumerable degrees. In Generalized Recursion Theory II, E. Fenstad, R. O. Gandy, and G. E. Sacks, editors, pages 239-269. North-Holland (Amsterdam), 1978. (PDF).
[7]: W. Maass. Contributions to alpha- and beta-recursion theory. Habilitationsschrift, Ludwig-Maximilians-Universitaet Muenchen, 1978. Minerva Publikation (Muenchen). (PDF).
[6]: W. Maass. Fine structure theory of the constructible universe in alpha- and beta-recursion theory. In Higher Set Theory, G. H. Mueller and D. Scott, editors, volume 669 of Lecture Notes in Mathematics, pages 339-359. Springer (Berlin), 1978. (PDF).
[5]: W. Maass. The uniform regular set theorem in alpha-recursion theory. J. Symbolic Logic, 43:270-279, 1978. (PDF).
[4]: W. Maass. Inadmissibility, tame r.e. sets and the admissible collapse. Annals of Mathematical Logics, 13:149-170, 1978. (PDF).
[3]: W. Maass. On minimal pairs and minimal degrees in higher recursion theory. Archive Math. Logik Grundlagen, 18:169-186, 1977. (PDF).
[2]: W. Maass. Eine Funktionalinterpretation der praedikativen Analysis. Archive Math. Logik Grundlagen, 18:27-46, 1976. (PDF).
[1]: W. Maass. Church rosser theorem fuer lambda-kalkuele mit unendlich langen termen. In Proof Theory Symposium Kiel 1974, J. Diller and G. H. Mueller, editors, volume 500 of Lecture Notes in Mathematics, pages 257-263. Springer (Berlin), 1975. (PDF).