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ORCIDJohanni Brea
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Journal Articles
- 2022
[j8]Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder:
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language. J. Stat. Softw. 102(1) (2022)- [j7]Vasiliki Liakoni, Marco P. Lehmann, Alireza Modirshanechi, Johanni Brea, Antoine Lutti
, Wulfram Gerstner, Kerstin Preuschoff:
Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making. NeuroImage 246: 118780 (2022) - 2021
- [j6]Vasiliki Liakoni, Alireza Modirshanechi, Wulfram Gerstner, Johanni Brea:
Learning in Volatile Environments With the Bayes Factor Surprise. Neural Comput. 33(2): 269-340 (2021) - 2020
- [j5]Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea:
On the choice of metric in gradient-based theories of brain function. PLoS Comput. Biol. 16(4) (2020) - 2019
- [j4]Bernd Illing, Wulfram Gerstner, Johanni Brea:
Biologically plausible deep learning - But how far can we go with shallow networks? Neural Networks 118: 90-101 (2019) - 2017
- [j3]Samuel P. Muscinelli, Wulfram Gerstner, Johanni Brea:
Exponentially Long Orbits in Hopfield Neural Networks. Neural Comput. 29(2): 458-484 (2017) - 2016
- [j2]Johanni Brea, Alexisz Tamás Gaál, Robert Urbanczik, Walter Senn:
Prospective Coding by Spiking Neurons. PLoS Comput. Biol. 12(6) (2016) - 2014
- [j1]Johanni Brea, Robert Urbanczik, Walter Senn:
A Normative Theory of Forgetting: Lessons from the Fruit Fly. PLoS Comput. Biol. 10(6) (2014)
Conference and Workshop Papers
- 2024
- [c7]Flavio Martinelli, Berfin Simsek, Wulfram Gerstner, Johanni Brea:
Expand-and-Cluster: Parameter Recovery of Neural Networks. ICML 2024 - 2023
- [c6]Berfin Simsek, Amire Bendjeddou, Wulfram Gerstner, Johanni Brea:
Should Under-parameterized Student Networks Copy or Average Teacher Weights? NeurIPS 2023 - 2022
- [c5]Georgios Iatropoulos, Johanni Brea, Wulfram Gerstner:
Kernel Memory Networks: A Unifying Framework for Memory Modeling. NeurIPS 2022 - 2021
- [c4]Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clément Hongler, Wulfram Gerstner, Johanni Brea:
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. ICML 2021: 9722-9732 - [c3]Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner:
Fitting summary statistics of neural data with a differentiable spiking network simulator. NeurIPS 2021: 18552-18563 - 2018
- [c2]Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. ICML 2018: 1057-1066 - 2011
- [c1]Johanni Brea, Walter Senn, Jean-Pascal Pfister:
Sequence learning with hidden units in spiking neural networks. NIPS 2011: 1422-1430
Informal and Other Publications
- 2023
- [i15]Johanni Brea, Flavio Martinelli, Berfin Simsek, Wulfram Gerstner:
MLPGradientFlow: going with the flow of multilayer perceptrons (and finding minima fast and accurately). CoRR abs/2301.10638 (2023) - [i14]Flavio Martinelli, Berfin Simsek, Johanni Brea, Wulfram Gerstner:
Expand-and-Cluster: Exact Parameter Recovery of Neural Networks. CoRR abs/2304.12794 (2023) - [i13]Berfin Simsek, Amire Bendjeddou, Wulfram Gerstner, Johanni Brea:
Should Under-parameterized Student Networks Copy or Average Teacher Weights? CoRR abs/2311.01644 (2023) - 2022
- [i12]Luca Viano, Johanni Brea:
Neural NID Rules. CoRR abs/2202.06036 (2022) - [i11]Georgios Iatropoulos, Johanni Brea, Wulfram Gerstner:
Kernel Memory Networks: A Unifying Framework for Memory Modeling. CoRR abs/2208.09416 (2022) - 2021
- [i10]Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clément Hongler, Wulfram Gerstner, Johanni Brea:
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. CoRR abs/2105.12221 (2021) - [i9]Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner:
Fitting summary statistics of neural data with a differentiable spiking network simulator. CoRR abs/2106.10064 (2021) - 2019
- [i8]Bernd Illing, Wulfram Gerstner, Johanni Brea:
Biologically plausible deep learning - but how far can we go with shallow networks? CoRR abs/1905.04101 (2019) - [i7]Johanni Brea, Berfin Simsek, Bernd Illing, Wulfram Gerstner:
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape. CoRR abs/1907.02911 (2019) - [i6]Vasiliki Liakoni, Alireza Modirshanechi, Wulfram Gerstner, Johanni Brea:
An Approximate Bayesian Approach to Surprise-Based Learning. CoRR abs/1907.02936 (2019) - 2018
- [i5]Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation. CoRR abs/1802.04325 (2018) - [i4]Florian Colombo, Johanni Brea, Wulfram Gerstner:
Learning to Generate Music with BachProp. CoRR abs/1812.06669 (2018) - 2017
- [i3]Johanni Brea:
Is prioritized sweeping the better episodic control? CoRR abs/1711.06677 (2017) - 2016
- [i2]Florian Colombo, Samuel P. Muscinelli, Alexander Seeholzer, Johanni Brea, Wulfram Gerstner:
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks. CoRR abs/1606.07251 (2016) - [i1]Thomas Mesnard, Wulfram Gerstner, Johanni Brea:
Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity. CoRR abs/1612.03214 (2016)
Coauthor Index
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