The Python programming language is steadily increasing in popularity as the language of
choice for scientifi c computing. The ability of this scripting environment to access a huge code
base in various languages, combined with its syntactical simplicity, make it the ideal tool for
implementing and sharing ideas among scientists from numerous fi elds and with heterogeneous
methodological backgrounds. The recent rise of reciprocal interest between the machine learning
(ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer
of computational methods that can benefi t from a Python-based framework. For many years, a
large fraction of both research communities have addressed, almost independently, very high
dimensional problems with almost completely non-overlapping methods. However, a number
of recently published studies that applied ML methods to neuroscience research questions
attracted a lot of attention from researchers from both fi elds, as well as the general public, and
showed that this approach can provide novel and fruitful insights into the functioning of the brain.
In this article we show how PyMVPA, a specialized Python framework for machine learning
based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a
single interface to a wide array of machine learning libraries and neural data-processing methods.
We demonstrate the general applicability and power of PyMVPA via analyses of a number of
neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.