Lars Buesing

Currently, I am a research fellow at the Grossman Center for the Statistics of Mind and the Department of Statistics at Columbia University.
My CV is here.

Office Address

Department of Statistics
Columbia University
Office 930
1255 Amsterdam Avenue
New York, NY 10027
Email: lars [at] stat.columbia.edu

Code package for analyzing high-dimensional recordings of neural activity

I've written, together with Jakob Macke, a Matlab toolbox for analyzing high-dimensional recordings of neural activity.
It contains implementations of the following methods:

Linear Dynamical Systems with Gaussian or Poisson distributed observations (GLDS/PLDS)
- State estimation with variational and Laplace inference
- Parameter estimation with Expectation Maximization
- Parameter estimation with spectral methods
Exponential Family PCA
Exponential Family PCA with nuclear norm penalization (Pfau, D., Pnevmatikakis, E. & Paninski, L. Robust learning of low-dimensional dynamics from large neural ensembles. NIPS 2013)

Here are a couple of example applications for the toolbox:

Dimensionality reduction of spike rasters from many simultaneously recorded neurons
Estimation of smoothed and denoised firing rates for neurons
Modelling of noise correlations with a low-rank, parametric dynamical systems

The git repository can be found here.

Research Interests and Publications

Structured latent variable models for inferring circuit structure from multi-cell activity recordings

L. Buesing, T. Machado, J.P. Cunningham, and L. Paninski.
Clustered factor analysis of multineuronal spike data.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014.
paper supplementary material

S. Turaga, L. Buesing, A. M. Packer, H. Dalgleish, N. Pettit, M. Hausser, and J. Macke.
Inferring neural population dynamics from multiple partial recordings of the same neural circuit.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.
paper

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 is believed to rely to great 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