A workshop on the topic of machine learning approaches in neuroscience and neuroimaging. We believe that both machine learning and neuroimaging can learn from each other as the two communities overlap and enter an intense exchange of ideas and research questions. Methodological developments in machine learning spurn novel paradigms in neuroimaging, neuroscience motivates methodological advances in computational analysis. In this context many controversies and open questions exist. The goal of the workshop is to pinpoint these issues, sketch future directions, and tackle open questions in the light of novel methodology.

The first workshop of this series at NIPS 2011 built upon earlier events in 2006 and 2008. Last year's workshop included many invited speakers, and was centered around two panel discussions, during which 2 questions were discussed: the interpretability of machine learning findings, and the shift of paradigms in the neuroscience community. The discussion was inspiring, and made clear, that there is a tremendous amount the two communities can learn from each other benefiting from communication across the disciplines.

The aim of the workshop is to offer a forum for the overlap of these communities. Besides interpretation, and the shift of paradigms, many open questions remain. Among them:


- How suitable are MVPA and inference methods for brain mapping?
- How can we assess the specificity and sensitivity?
- What is the role of decoding vs. embedded or separate feature selection?
- How can we use these approaches for a flexible and useful representation of neuroimaging data?
- What can we accomplish with generative vs. discriminative modelling?
- Can and should the Machine Learning community provide a standard repertoire of methods for the Neuroimaging community to use (e.g. in choosing a classifier)?