Lars Buesing
Lars Buesing
Google DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
5332010
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
3662011
Neural scene representation and rendering
SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ...
Science 360 (6394), 1204-1210, 2018
2862018
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
2402013
Imagination-Augmented Agents for Deep Reinforcement Learning.
S Racanière, T Weber, DP Reichert, L Buesing, A Guez, DJ Rezende, ...
NIPS, 5690-5701, 2017
2042017
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
1902011
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
1742017
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
1342010
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
1262008
Black box variational inference for state space models
E Archer, IM Park, L Buesing, J Cunningham, L Paninski
arXiv preprint arXiv:1511.07367, 2015
1102015
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
1002011
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
802017
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
702007
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
662013
Temporal difference variational auto-encoder
K Gregor, G Papamakarios, F Besse, L Buesing, T Weber
arXiv preprint arXiv:1806.03107, 2018
602018
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
592018
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
572009
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
492018
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
492012
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
402015
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Articles 1–20