We seek to understand how populations of neurons collectively process sensory input, perform computations and control behaviour. To this end, we develop statistical models and machine learning algorithms for large-scale analysis of neural data, and collaborate with experimental laboratories performing measurements of neural activity and behaviour.
We develop statistical models for the analysis of neural and behavioural data.
Read more about our work in our articles and conference papers.
We share code for many of our publications.
A toolbox for modelling and simulating high-dimensional binary and count-data with correlations - 2017-08-21
We put online our new code repository CorBinian for modelling multivariate binary data in Matlab.
It extends and succeeds our previous toolbox, and features new fast MCMC code for regularized fitting of high-dimensional maxEnt models with Rao-Blackwellization, code for minimum probability flow fitting, as well as for computation of specific heat capacity from maxEnt models.