Sequence learning with hidden units in spiking neural networks. - Johanni Brea; Walter Senn; Jean-Pascal Pfister (NIPS, 2011)

A Normative Theory of Forgetting: Lessons from the Fruit Fly. - Johanni Brea; Robert Urbanczik; Walter Senn (PLoS Comput. Biol., 2014)

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks. - Florian Colombo; Samuel P. Muscinelli; Alexander Seeholzer; Johanni Brea; Wulfram Gerstner (CoRR, 2016)

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity. - Thomas Mesnard; Wulfram Gerstner; Johanni Brea (CoRR, 2016)

Prospective Coding by Spiking Neurons. - Johanni Brea; Alexisz Tamás Gaál; Robert Urbanczik; Walter Senn (PLoS Comput. Biol., 2016)

Is prioritized sweeping the better episodic control? - Johanni Brea (CoRR, 2017)

Exponentially Long Orbits in Hopfield Neural Networks. - Samuel P. Muscinelli; Wulfram Gerstner; Johanni Brea (Neural Comput., 2017)

Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. - Dane S. Corneil; Wulfram Gerstner; Johanni Brea (ICML, 2018)

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation. - Dane S. Corneil; Wulfram Gerstner; Johanni Brea (CoRR, 2018)

Learning to Generate Music with BachProp. - Florian Colombo; Johanni Brea; Wulfram Gerstner (CoRR, 2018)

Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape. - Johanni Brea; Berfin Simsek; Bernd Illing; Wulfram Gerstner (CoRR, 2019)

An Approximate Bayesian Approach to Surprise-Based Learning. - Vasiliki Liakoni; Alireza Modirshanechi; Wulfram Gerstner; Johanni Brea (CoRR, 2019)

Biologically plausible deep learning - But how far can we go with shallow networks? - Bernd Illing; Wulfram Gerstner; Johanni Brea (Neural Networks, 2019)

On the choice of metric in gradient-based theories of brain function. - Simone Carlo Surace; Jean-Pascal Pfister; Wulfram Gerstner; Johanni Brea (PLoS Comput. Biol., 2020)

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. - Berfin Simsek; François Ged; Arthur Jacot; Francesco Spadaro; Clément Hongler; Wulfram Gerstner; Johanni Brea (ICML, 2021)

Fitting summary statistics of neural data with a differentiable spiking network simulator. - Guillaume Bellec; Shuqi Wang; Alireza Modirshanechi; Johanni Brea; Wulfram Gerstner (NeurIPS, 2021)

Learning in Volatile Environments With the Bayes Factor Surprise. - Vasiliki Liakoni; Alireza Modirshanechi; Wulfram Gerstner; Johanni Brea (Neural Comput., 2021)

Kernel Memory Networks: A Unifying Framework for Memory Modeling. - Georgios Iatropoulos; Johanni Brea; Wulfram Gerstner (NeurIPS, 2022)

Neural NID Rules. - Luca Viano; Johanni Brea (CoRR, 2022)

GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language. - Jamie Fairbrother; Christopher Nemeth; Maxime Rischard; Johanni Brea; Thomas Pinder (J. Stat. Softw., 2022)

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making. - Vasiliki Liakoni; Marco P. Lehmann; Alireza Modirshanechi; Johanni Brea; Antoine Lutti; Wulfram Gerstner; Kerstin Preuschoff (NeuroImage, 2022)

Should Under-parameterized Student Networks Copy or Average Teacher Weights? - Berfin Simsek; Amire Bendjeddou; Wulfram Gerstner; Johanni Brea (NeurIPS, 2023)

MLPGradientFlow: going with the flow of multilayer perceptrons (and finding minima fast and accurately). - Johanni Brea; Flavio Martinelli; Berfin Simsek; Wulfram Gerstner (CoRR, 2023)

Expand-and-Cluster: Exact Parameter Recovery of Neural Networks. - Flavio Martinelli; Berfin Simsek; Johanni Brea; Wulfram Gerstner (CoRR, 2023)

Expand-and-Cluster: Parameter Recovery of Neural Networks. - Flavio Martinelli; Berfin Simsek; Wulfram Gerstner; Johanni Brea (ICML, 2024)

Flat Channels to Infinity in Neural Loss Landscapes. - Flavio Martinelli; Alexander van Meegen; Berfin Simsek; Wulfram Gerstner; Johanni Brea (CoRR, 2025)