Matching recall and storage in sequence learning with spiking neural networks J Brea, W Senn, JP Pfister Journal of neuroscience 33 (23), 9565-9575, 2013 | 103 | 2013 |

Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules W Gerstner, M Lehmann, V Liakoni, D Corneil, J Brea Frontiers in neural circuits 12, 53, 2018 | 97 | 2018 |

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks F Colombo, SP Muscinelli, A Seeholzer, J Brea, W Gerstner arXiv preprint arXiv:1606.07251, 2016 | 46 | 2016 |

Sequence learning with hidden units in spiking neural networks J Brea, W Senn, JP Pfister Advances in neural information processing systems 24, 1422-1430, 2011 | 46 | 2011 |

Biologically plausible deep learning—But how far can we go with shallow networks? B Illing, W Gerstner, J Brea Neural Networks 118, 90-101, 2019 | 42 | 2019 |

Efficient model-based deep reinforcement learning with variational state tabulation D Corneil, W Gerstner, J Brea International Conference on Machine Learning, 1049-1058, 2018 | 35 | 2018 |

Prospective coding by spiking neurons J Brea, AT Gaál, R Urbanczik, W Senn PLoS computational biology 12 (6), e1005003, 2016 | 32 | 2016 |

A normative theory of forgetting: lessons from the fruit fly J Brea, R Urbanczik, W Senn PLoS Comput Biol 10 (6), e1003640, 2014 | 24 | 2014 |

Does computational neuroscience need new synaptic learning paradigms? J Brea, W Gerstner Current Opinion in Behavioral Sciences 11, 61-66, 2016 | 21 | 2016 |

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity T Mesnard, W Gerstner, J Brea arXiv preprint arXiv:1612.03214, 2016 | 14 | 2016 |

Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape J Brea, B Simsek, B Illing, W Gerstner arXiv preprint arXiv:1907.02911, 2019 | 9 | 2019 |

GaussianProcesses. jl: A Nonparametric Bayes package for the Julia Language J Fairbrother, C Nemeth, M Rischard, J Brea, T Pinder arXiv preprint arXiv:1812.09064, 2018 | 9 | 2018 |

Decoupling Backpropagation using Constrained Optimization Methods A Gotmare, V Thomas, J Brea, M Jaggi | 8 | 2018 |

Is prioritized sweeping the better episodic control? J Brea arXiv preprint arXiv:1711.06677, 2017 | 8 | 2017 |

Learning in Volatile Environments With the Bayes Factor Surprise V Liakoni, A Modirshanechi, W Gerstner, J Brea Neural Computation 33 (2), 269-340, 2021 | 5* | 2021 |

On the choice of metric in gradient-based theories of brain function SC Surace, JP Pfister, W Gerstner, J Brea PLOS Computational Biology 16 (4), e1007640, 2020 | 5 | 2020 |

Learning to Generate Music with BachProp F Colombo, J Brea, W Gerstner arXiv preprint arXiv:1812.06669, 2018 | 5 | 2018 |

Exponentially long orbits in Hopfield neural networks SP Muscinelli, W Gerstner, J Brea Neural computation 29 (2), 458-484, 2017 | 2 | 2017 |

Fitting summary statistics of neural data with a differentiable spiking network simulator G Bellec, S Wang, A Modirshanechi, J Brea, W Gerstner arXiv preprint arXiv:2106.10064, 2021 | | 2021 |

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances B Şimşek, F Ged, A Jacot, F Spadaro, C Hongler, W Gerstner, J Brea arXiv preprint arXiv:2105.12221, 2021 | | 2021 |