Statistical learning analysis in neuroscience: aiming for transparency

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires ``neuroscience-aware'' technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here we review its features and applicability to various neural data modalities.

Abstract licence: CC BY

30 more references, whose wording the paper’s licence keeps private.

• A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie · 2014

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.