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 | 207 | 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 | 126 | 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 | 83 | 2019 |

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 | 61 | 2016 |

Efficient model-based deep reinforcement learning with variational state tabulation D Corneil, W Gerstner, J Brea International Conference on Machine Learning, 1049-1058, 2018 | 60 | 2018 |

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 | 40 | 2016 |

A normative theory of forgetting: lessons from the fruit fly J Brea, R Urbanczik, W Senn PLoS Comput Biol 10 (6), e1003640, 2014 | 33 | 2014 |

Does computational neuroscience need new synaptic learning paradigms? J Brea, W Gerstner Current Opinion in Behavioral Sciences 11, 61-66, 2016 | 32 | 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 | 29 | 2019 |

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 | 25 | 2016 |

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 | 24* | 2022 |

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 | 20 | 2021 |

Learning in Volatile Environments With the Bayes Factor Surprise V Liakoni, A Modirshanechi, W Gerstner, J Brea Neural Computation 33 (2), 269-340, 2021 | 19* | 2021 |

Decoupling Backpropagation using Constrained Optimization Methods A Gotmare, V Thomas, J Brea, M Jaggi | 15 | 2018 |

Learning to Generate Music with BachProp F Colombo, J Brea, W Gerstner arXiv preprint arXiv:1812.06669, 2018 | 11 | 2018 |

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

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 | 7 | 2020 |

Testing two competing hypotheses for Eurasian jays’ caching for the future P Amodio, J Brea, BG Farrar, L Ostojić, NS Clayton Scientific Reports 11 (1), 1-15, 2021 | 5 | 2021 |

Surprise: a unified theory and experimental predictions A Modirshanechi, J Brea, W Gerstner bioRxiv, 2021 | 5 | 2021 |