| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 1761-1770, 2019 | 122 | 2019 |
| Dendritic cortical microcircuits approximate the backpropagation algorithm J Sacramento, RP Costa, Y Bengio, W Senn Advances in Neural Information Processing Systems, 8721-8732, 2018 | 91 | 2018 |
| Dendritic error backpropagation in deep cortical microcircuits J Sacramento, RP Costa, Y Bengio, W Senn arXiv preprint arXiv:1801.00062, 2017 | 32 | 2017 |
| Continual learning with hypernetworks J von Oswald, C Henning, J Sacramento, BF Grewe International Conference on Learning Representations (ICLR 2020), 2019 | 29 | 2019 |
| Computational roles of plastic probabilistic synapses M Llera-Montero, J Sacramento, RP Costa Current Opinion in Neurobiology 54, 90-97, 2019 | 13 | 2019 |
| 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 | 12 | 2016 |
| Energy efficient sparse connectivity from imbalanced synaptic plasticity rules J Sacramento, A Wichert, MCW van Rossum PLoS computational biology 11 (6), e1004265, 2015 | 10 | 2015 |
| Tree-like hierarchical associative memory structures J Sacramento, A Wichert Neural Networks 24 (2), 143-147, 2011 | 10 | 2011 |
| 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 | 7 | 2018 |
| A Theoretical Framework for Target Propagation A Meulemans, FS Carzaniga, JAK Suykens, J Sacramento, BF Grewe Advances in Neural Information Processing Systems, 2020 | 6 | 2020 |
| 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 | 6 | 2018 |
| Regarding the temporal requirements of a hierarchical Willshaw network J Sacramento, F Burnay, A Wichert Neural Networks 25, 84-93, 2012 | 5 | 2012 |
| Taxonomical associative memory D Rendeiro, J Sacramento, A Wichert Cognitive Computation 6 (1), 45-65, 2014 | 4 | 2014 |
| Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory J Sacramento, A Wichert Biological Cybernetics 106 (2), 123-133, 2012 | 4 | 2012 |
| 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 | 2 | 2019 |
| 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 | 2 | 2019 |
| Conductance-based dendrites perform reliability-weighted opinion pooling J Jordan, MA Petrovici, W Senn, J Sacramento | 1* | |
| Neural networks with late-phase weights J von Oswald, S Kobayashi, J Sacramento, A Meulemans, C Henning, ... arXiv preprint arXiv:2007.12927, 2020 | | 2020 |
| Bayesian multisensory integration by dendrites J Sacramento, W Senn Cosyne Abstracts 2016, Salt Lake City USA, 2016 | | 2016 |
| Backward reasoning the formation rules W Senn, J Sacramento Nature Neuroscience 18 (12), 1705-1706, 2015 | | 2015 |
Walter SennInstitut für Physiologie, Universität Bern, SwitzerlandVerified email at pyl.unibe.ch
