Lars Buesing

I am currently a postdoc at the Gatsby Computational Neuroscience Unit, UCL.
My CV is here.

Office Address

Gatsby Computational Neuroscience Unit
University College London
17 Queen Square
London WC1N 3AR, UK
Phone: +44 (0)20 7679 1191
Email: lars [at] gatsby.ucl.ac.uk

Research Interests

Modelling & Analysis of Recordings from Neural Populations

In collaboration with Maneesh Sahani and Jakob Macke, I have been working on probabilistic methods for analyzing simultaneous recordings from multiple neurons such as multi-electrode recordings. I am especially interested in latent variable models as they allow for principled ways of dimensionality reduction and smoothing of recorded data, which is often high-dimensional and apparently noisy.

L. Buesing, J. Macke, and M. Sahani.
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.
In Advances in Neural Information Processing Systems (NIPS) 25, 2012. Selected for oral presentation.
paper supplementary material

L. Buesing, J. Macke, and M. Sahani.
Learning stable, regularised latent models of neural population dynamics.
Network: Computation in Neural Systems, 23(1,2):24-47, 2012.
preprint

J. Macke, L. Buesing, J. P. Cunningham, B. M. Yu, K. V. Shenoy, and M. Sahani.
Empirical models of spiking in neural populations.
In Advances in Neural Information Processing Systems (NIPS) 24, 2011. Selected for oral presentation.
paper

Functional Models of Neural Mircocircuits

I am also interested in functional, "top-down" models of single neurons and neural microcircuits. This research is driven by the widely acknowledge fact that neural system must "reason", e.g. detect causes of sensory percepts, based on ambiguous and noisy observations of the world. Together with collaborators form TU Graz, I have been exploring the idea that neural microcircuits perform inference computations by sampling from hypotheses that are consistent with the observed data.

L. Buesing, J. Bill, B. Nessler, and W. Maass.
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons.
PLoS Comput Biol, 7(11):e1002211, 11 2011.
link paper

D. Pecevski, L. Buesing, and W. Maass.
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons.
PLoS Comput Biol, 7(12):e1002294, 12 2011.
link paper

B. Nessler, Michael Pfeiffer, L. Buesing, and W. Maass.
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
PLoS Comput Biol, 9(4): e1003037, 04 2013.
link paper

L. Buesing, B. Schrauwen, and R. Legenstein.
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons.
Neural Computation, 22(5):272-1311, 2010.
link paper

B. Schrauwen, L. Buesing, and R. Legenstein.
On computational power and the order-chaos phase transition in reservoir computing.
In Advances in Neural Information Processing Systems (NIPS) 21, 2009.
Studen Paper Award honorable mentions.
paper

L. Buesing and W. Maass.
A spiking neuron as information bottleneck.
Neural Computation, 22(8):1961-1992, 2010.
link paper

L. Buesing and W. Maass.
Simplified rules and theoretical analysis for information bottleneck optimization and PCA with spiking neurons.
In Advances in Neural Information Processing Systems (NIPS) 20, 2008.
paper

E. Muller, L. Buesing, J. Schemmel, and K. Meier.
Spike-frequency adapting neural ensembles: Beyond mean adaptation and renewal theories.
Neural Computation, 19(11):2958-3010, 2007.
link paper

Models of Synaptic Plasticity

Learning and adapting presumably relies to great a extend on the plasticity of synaptic connections between neurons. Together with colleagues from the LCN at the EPFL in Lausanne I have been working on phenomenological models of synaptic plasticity.

C. Clopath, L. Buesing, E. Vasilaki, and W. Gerstner.
Connectivity reflects coding: a model of voltage-based STDP with homeostasis.
Nature Neuroscience, 13(3):344-352, 2010.
link paper

C. Clopath, L. Ziegler, E. Vasilaki, L. Buesing, and W. Gerstner.
Tag-trigger-consolidation: A model of early and late long-term-potentiation and depression.
PLoS Computational Biology, 4(12):e1000248,2008.
link paper