Lars Buesing
Lars Buesing
Google DeepMind
Verified email at google.com
TitleCited byYear
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
4562010
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons
L Buesing, J Bill, B Nessler, W Maass
PLoS computational biology 7 (11), e1002211, 2011
3102011
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
B Nessler, M Pfeiffer, L Buesing, W Maass
PLoS computational biology 9 (4), e1003037, 2013
2022013
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
1462011
Neural scene representation and rendering
SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ...
Science 360 (6394), 1204-1210, 2018
1382018
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
1282017
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
1202010
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 computational biology 4 (12), e1000248, 2008
1192008
Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
D Pecevski, L Buesing, W Maass
PLoS computational biology 7 (12), e1002294, 2011
842011
Imagination-augmented agents for deep reinforcement learning
S Racanière, T Weber, D Reichert, L Buesing, A Guez, DJ Rezende, ...
Advances in neural information processing systems, 5690-5701, 2017
752017
Black box variational inference for state space models
E Archer, IM Park, L Buesing, J Cunningham, L Paninski
arXiv preprint arXiv:1511.07367, 2015
752015
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
652007
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, 1682-1690, 2012
492012
On computational power and the order-chaos phase transition in reservoir computing
B Schrauwen, L Büsing, RA Legenstein
Advances in Neural Information Processing Systems, 1425-1432, 2009
482009
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
442017
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
412012
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
382018
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
302015
High-dimensional neural spike train analysis with generalized count linear dynamical systems
Y Gao, L Busing, KV Shenoy, JP Cunningham
Advances in neural information processing systems, 2044-2052, 2015
272015
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
S Turaga, L Buesing, AM Packer, H Dalgleish, N Pettit, M Hausser, ...
Advances in Neural Information Processing Systems, 539-547, 2013
262013
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Articles 1–20