Real-time computing without stable states: A new framework for neural computation based on perturbations W Maass, T Natschläger, H Markram Neural computation 14 (11), 2531-2560, 2002 | 2667 | 2002 |

Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997 | 1572 | 1997 |

Pulsed neural networks W Maass, CM Bishop MIT press, 2001 | 1171 | 2001 |

Approximation schemes for covering and packing problems in image processing and VLSI DS Hochbaum, W Maass Journal of the ACM (JACM) 32 (1), 130-136, 1985 | 802 | 1985 |

State-dependent computations: spatiotemporal processing in cortical networks DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125, 2009 | 699 | 2009 |

Introduction: Bilingual conversation revisited P Auer Code-switching in conversation, 9-32, 2013 | 529 | 2013 |

Threshold circuits of bounded depth A Hajnal, W Maass, P Pudlák, M Szegedy, G Turán Journal of Computer and System Sciences 46 (2), 129-154, 1993 | 423 | 1993 |

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons L Buesing, J Bill, B Nessler, W Maass PLoS computational biology 7 (11), 2011 | 319 | 2011 |

Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007 | 317 | 2007 |

Universal multi-task kernels A Caponnetto, CA Micchelli, M Pontil, Y Ying Journal of Machine Learning Research 9 (Jul), 1615-1646, 2008 | 293* | 2008 |

On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000 | 289 | 2000 |

Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996 | 281 | 1996 |

Fast sigmoidal networks via spiking neurons W Maass Neural Computation 9 (2), 279-304, 1997 | 245 | 1997 |

What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005 | 232 | 2005 |

Computational models for generic cortical microcircuits W Maass, T Natschläger, H Markram Computational neuroscience: A comprehensive approach 18, 575-605, 2004 | 219 | 2004 |

On the computational power of noisy spiking neurons W Maass Advances in neural information processing systems, 211-217, 1996 | 219 | 1996 |

Computational neuroscience: a comprehensive approach J Feng Chapman and Hall/CRC, 2003 | 216 | 2003 |

The" liquid computer": A novel strategy for real-time computing on time series T Natschläger, W Maass, H Markram Special issue on Foundations of Information Processing of TELEMATIK 8 …, 2002 | 216 | 2002 |

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback R Legenstein, D Pecevski, W Maass PLoS computational biology 4 (10), 2008 | 213 | 2008 |

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity B Nessler, M Pfeiffer, L Buesing, W Maass PLoS computational biology 9 (4), 2013 | 210 | 2013 |