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Johanni Brea
Johanni Brea
Scientist & Lecturer, Laboratory of Computational Neuroscience, EPFL
Bestätigte E-Mail-Adresse bei epfl.ch
Titel
Zitiert von
Zitiert von
Jahr
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
2052018
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
1262013
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
822019
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
612016
Efficient model-based deep reinforcement learning with variational state tabulation
D Corneil, W Gerstner, J Brea
International Conference on Machine Learning, 1049-1058, 2018
602018
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
522011
Prospective coding by spiking neurons
J Brea, AT Gaál, R Urbanczik, W Senn
PLoS computational biology 12 (6), e1005003, 2016
402016
A normative theory of forgetting: lessons from the fruit fly
J Brea, R Urbanczik, W Senn
PLoS Comput Biol 10 (6), e1003640, 2014
332014
Does computational neuroscience need new synaptic learning paradigms?
J Brea, W Gerstner
Current Opinion in Behavioral Sciences 11, 61-66, 2016
322016
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
282019
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
252016
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
23*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
192021
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
152018
Learning to Generate Music with BachProp
F Colombo, J Brea, W Gerstner
arXiv preprint arXiv:1812.06669, 2018
112018
Is prioritized sweeping the better episodic control?
J Brea
arXiv preprint arXiv:1711.06677, 2017
82017
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
72020
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
52021
Surprise: a unified theory and experimental predictions
A Modirshanechi, J Brea, W Gerstner
bioRxiv, 2021
52021
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