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.
|Titolo:||Statistical learning analysis in neuroscience: aiming for transparency|
|Autori:||M., Hanke; Y. O., Halchenko; Haxby, James Van Loan; S., Pollmann|
|Titolo del periodico:||FRONTIERS IN NEUROSCIENCE|
|Anno di pubblicazione:||2010|
|Numero e parte del fascicolo:||4|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3389/neuro.01.007.2010|
|Appare nelle tipologie:||03.1 Articolo su rivista (Journal article)|