| Connectivity reflects coding: a model of voltage-based STDP with homeostasis C Clopath, L Büsing, E Vasilaki, W Gerstner Nature neuroscience 13 (3), 344, 2010 | 533 | 2010 |
| Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons L Buesing, J Bill, B Nessler, W Maass PLoS Comput Biol 7 (11), e1002211, 2011 | 366 | 2011 |
| Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210, 2018 | 285 | 2018 |
| Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity B Nessler, M Pfeiffer, L Buesing, W Maass PLoS Comput Biol 9 (4), e1003037, 2013 | 240 | 2013 |
| Imagination-Augmented Agents for Deep Reinforcement Learning. S Racanière, T Weber, DP Reichert, L Buesing, A Guez, DJ Rezende, ... NIPS, 5690-5701, 2017 | 204 | 2017 |
| Empirical models of spiking in neural populations JH Macke, JP Cunningham, MY Byron, KV Shenoy, M Sahani Advances in neural information processing systems 24, 2011 | 189 | 2011 |
| Imagination-augmented agents for deep reinforcement learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv preprint arXiv:1707.06203, 2017 | 174 | 2017 |
| Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons L Büsing, B Schrauwen, R Legenstein Neural computation 22 (5), 1272-1311, 2010 | 134 | 2010 |
| Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression C Clopath, L Ziegler, E Vasilaki, L Büsing, W Gerstner PLoS Comput Biol 4 (12), e1000248, 2008 | 126 | 2008 |
| Black box variational inference for state space models E Archer, IM Park, L Buesing, J Cunningham, L Paninski arXiv preprint arXiv:1511.07367, 2015 | 110 | 2015 |
| Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons D Pecevski, L Buesing, W Maass PLoS Comput Biol 7 (12), e1002294, 2011 | 99 | 2011 |
| Learning model-based planning from scratch R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ... arXiv preprint arXiv:1707.06170, 2017 | 80 | 2017 |
| Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories E Muller, L Buesing, J Schemmel, K Meier Neural computation 19 (11), 2958-3010, 2007 | 70 | 2007 |
| Spectral learning of linear dynamics from generalised-linear observations with application to neural population data L Buesing, JH Macke, M Sahani Advances in Neural Information Processing Systems 25: 26th Conference on …, 2013 | 65 | 2013 |
| Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018 | 59 | 2018 |
| Learning and querying fast generative models for reinforcement learning L Buesing, T Weber, S Racaniere, SM Eslami, D Rezende, DP Reichert, ... arXiv preprint arXiv:1802.03006, 2018 | 59 | 2018 |
| On computational power and the order-chaos phase transition in reservoir computing B Schrauwen, L Buesing, R Legenstein 22nd Annual conference on Neural Information Processing Systems (NIPS 2008 …, 2009 | 57 | 2009 |
| Woulda, coulda, shoulda: Counterfactually-guided policy search L Buesing, T Weber, Y Zwols, S Racaniere, A Guez, JB Lespiau, N Heess arXiv preprint arXiv:1811.06272, 2018 | 49 | 2018 |
| Learning stable, regularised latent models of neural population dynamics L Buesing, JH Macke, M Sahani Network: Computation in Neural Systems 23 (1-2), 24-47, 2012 | 49 | 2012 |
| Estimating state and parameters in state space models of spike trains JH Macke, L Buesing, M Sahani, Z Chen Advanced state space methods for neural and clinical data 137, 2015 | 40 | 2015 |
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyAdresse e-mail validée de igi.tugraz.at
