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.

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Read more about our work in our articles and conference papers.

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We share code for many of our publications.

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Latest News

Three papers accepted at NIPS

... one of which as a spotlight! - 2017-09-06

We had three papers accepted at NIPS this year– Jan-Matthis’ and Pedro’s paper Flexible statistical inference for mechanistic models of neural dynamics and Marcel’s paper Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations were accepted for poster presentations. Artur’s paper Fast amortized inference of neural activity from calcium imaging data with variational autoencoders was selected for a spotlight. There were 3240 submissions to NIPS this year, 678 of which were accepted at the conference, and 112 of which for spotlights.

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