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Statistical Challenges in Neuroscience 3-5 September, 2014

Neuroscience is a very diverse field, as its central object - the brain - can be approached from a bewildering variety of angles, studied at any scale from molecular to behavioural, and measured, probed and prodded in myriad different ways. Each of these methods, from 2-photon imaging to reinforcement learning experiments, comes with its own statistical challenges. Some of these challenges will be highly familiar to statisticians, because they include some of the most active research areas in statistics and machine learning: for example, studies of anatomy yield partially observed network data, reverse correlation experiments lead to a large-p small-n regression problem, fMRI comes with big data issues, etc. Others will be less familiar, for example the issues arising from non-stationarity in behavioural experiments, that of inter-individual differences, or that of measuring probabilistic dependence between spiking neurons. These problems are central to the development of methodology in neuroscience and should appeal to many statisticians.

The workshop aims to bring together neuroscientists (interested in methodological issues) and statisticians (interested in new applications) with the goal to (a) introduce the neuroscience community to recent developments within statistics and (b) raise awareness within the statistics community of some of the challenges that arise in the analysis of neuroscientific data.


Travel and Visiting Info

Workshop Information Booklet (participants will receive a hardcopy from the registration desk)

Talk Abstracts and Slides

Poster Abstracts and Downloads

Timetable (pdf)

Keynote Speakers

The scientific committee is:


The meeting organisers are:

Florence Forbes (INRIA, Grenoble Rhône-Alpes)
Tom Nichols (University of Warwick),
Christophe Pouzat (CNRS, Paris)
Gäel Varoquaux (INRIA, Saclay Île-de-France)
Felix Wichmann (MPI for Intelligent Systems, Tübingen)
  Simon Barthelmé (
Nicolas Chopin (
Adam Johansen (