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 | 321 | 2018 |

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 | 133 | 2013 |

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 | 122 | 2019 |

Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances B Simsek, F Ged, A Jacot, F Spadaro, C Hongler, W Gerstner, J Brea International Conference on Machine Learning, 9722-9732, 2021 | 82 | 2021 |

Efficient model-based deep reinforcement learning with variational state tabulation D Corneil, W Gerstner, J Brea International Conference on Machine Learning, 1049-1058, 2018 | 71 | 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 | 65 | 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 | 52 | 2019 |

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 | 52 | 2011 |

Prospective coding by spiking neurons J Brea, AT Gaál, R Urbanczik, W Senn PLoS computational biology 12 (6), e1005003, 2016 | 48 | 2016 |

Does computational neuroscience need new synaptic learning paradigms? J Brea, W Gerstner Current Opinion in Behavioral Sciences 11, 61-66, 2016 | 43 | 2016 |

A normative theory of forgetting: lessons from the fruit fly J Brea, R Urbanczik, W Senn PLoS Comput Biol 10 (6), e1003640, 2014 | 38 | 2014 |

GaussianProcesses. jl: A Nonparametric Bayes Package for the Julia Language J Fairbrother, C Nemeth, M Rischard, J Brea, T Pinder Journal of Statistical Software 102, 1-36, 2022 | 37* | 2022 |

Learning in Volatile Environments With the Bayes Factor Surprise V Liakoni, A Modirshanechi, W Gerstner, J Brea Neural Computation 33 (2), 269-340, 2021 | 36 | 2021 |

A taxonomy of surprise definitions A Modirshanechi, J Brea, W Gerstner Journal of Mathematical Psychology 110, 102712, 2022 | 35 | 2022 |

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 | 27 | 2016 |

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 | 22 | 2020 |

Decoupling Backpropagation using Constrained Optimization Methods A Gotmare, V Thomas, J Brea, M Jaggi | 20 | 2018 |

Surprise and novelty in the brain A Modirshanechi, S Becker, J Brea, W Gerstner Current Opinion in Neurobiology 82, 102758, 2023 | 13 | 2023 |

Computational models of episodic-like memory in food-caching birds J Brea, NS Clayton, W Gerstner Nature Communications 14 (1), 2979, 2023 | 13 | 2023 |

Learning to Generate Music with BachProp F Colombo, J Brea, W Gerstner arXiv preprint arXiv:1812.06669, 2018 | 12 | 2018 |