Lars Buesing
Lars Buesing
Google DeepMind
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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
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
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
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
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
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
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
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
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
Black box variational inference for state space models
E Archer, IM Park, L Buesing, J Cunningham, L Paninski
arXiv preprint arXiv:1511.07367, 2015
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
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
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
Neural scene representation and rendering
SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ...
Science 360 (6394), 1204-1210, 2018
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
Estimating state and parameters in state space models of spike trains
JH Macke, L Buesing, M Sahani
Advanced State Space Methods for Neural and Clinical Data 137, 2015
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
Clustered factor analysis of multineuronal spike data
L Buesing, TA Machado, JP Cunningham, L Paninski
Advances in Neural Information Processing Systems, 3500-3508, 2014
A spiking neuron as information bottleneck
L Buesing, W Maass
Neural computation 22 (8), 1961-1992, 2010
Simplified rules and theoretical analysis for information bottleneck optimization and PCA with spiking neurons
L Buesing, W Maass
Advances in Neural Information Processing Systems, 193-200, 2008
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