João Sacramento
João Sacramento
Institute of Neuroinformatics, ETH Zürich, Switzerland
Verified email at joaosacramento.com - Homepage
Title
Cited by
Cited by
Year
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature Neuroscience 22 (11), 1761-1770, 2019
1842019
Dendritic cortical microcircuits approximate the backpropagation algorithm
J Sacramento, RP Costa, Y Bengio, W Senn
Advances in Neural Information Processing Systems, 8721-8732, 2018
1092018
Continual learning with hypernetworks
J von Oswald, C Henning, J Sacramento, BF Grewe
International Conference on Learning Representations (ICLR 2020), 2019
492019
Dendritic error backpropagation in deep cortical microcircuits
J Sacramento, RP Costa, Y Bengio, W Senn
arXiv preprint arXiv:1801.00062, 2017
342017
Computational roles of plastic probabilistic synapses
M Llera-Montero, J Sacramento, RP Costa
Current Opinion in Neurobiology 54, 90-97, 2019
172019
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
Y Bengio, B Scellier, O Bilaniuk, J Sacramento, W Senn
arXiv preprint arXiv:1606.01651, 2016
132016
Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
J Sacramento, A Wichert, MCW van Rossum
PLoS computational biology 11 (6), e1004265, 2015
102015
Tree-like hierarchical associative memory structures
J Sacramento, A Wichert
Neural Networks 24 (2), 143-147, 2011
102011
A Theoretical Framework for Target Propagation
A Meulemans, FS Carzaniga, JAK Suykens, J Sacramento, BF Grewe
Advances in Neural Information Processing Systems, 2020
92020
Approximating the predictive distribution via adversarially-trained hypernetworks
C Henning, J von Oswald, J Sacramento, SC Surace, JP Pfister, ...
Bayesian Deep Learning Workshop, NeurIPS (Spotlight) 2018, 2018
92018
Sensory representation of an auditory cued tactile stimulus in the posterior parietal cortex of the mouse
H Mohan, Y Gallero-Salas, S Carta, J Sacramento, B Laurenczy, ...
Scientific reports 8 (1), 7739, 2018
72018
Regarding the temporal requirements of a hierarchical Willshaw network
J Sacramento, F Burnay, A Wichert
Neural Networks 25, 84-93, 2012
52012
Taxonomical associative memory
D Rendeiro, J Sacramento, A Wichert
Cognitive Computation 6 (1), 45-65, 2014
42014
Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory
J Sacramento, A Wichert
Biological Cybernetics 106 (2), 123-133, 2012
42012
Lagrangian dynamics of dendritic microcircuits enables real-time backpropagation of errors
D Dold, AF Kungl, J Sacramento, MA Petrovici, K Schindler, J Binas, ...
target 100 (1), 2, 2019
32019
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
T Mesnard, G Vignoud, J Sacramento, W Senn, Y Bengio
arXiv preprint arXiv:1911.08585, 2019
22019
A contrastive rule for meta-learning
N Zucchet, S Schug, J von Oswald, D Zhao, J Sacramento
arXiv preprint arXiv:2104.01677, 2021
12021
Meta-learning via hypernetworks
D Zhao, J von Oswald, S Kobayashi, J Sacramento, BF Grewe
NeurIPS Workshop on Meta-learning 2020, 2020
12020
Conductance-based dendrites perform reliability-weighted opinion pooling
J Jordan, MA Petrovici, W Senn, J Sacramento
1*
Learning Bayes-optimal dendritic opinion pooling
J Jordan, J Sacramento, WAM Wybo, MA Petrovici, W Senn
arXiv preprint arXiv:2104.13238, 2021
2021
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