PyMVPA: a unifying approach to the analysis of neuroscientific data Export

@article{pymvpa2009, abstract = {The Python programming language is steadily increasing in popularity as the language of choice for scientific 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 fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit 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 machine learning methods to neuroscience research questions attracted a lot of attention from researchers from both fields, 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}, author = {Hanke, Michael and Halchenko, Yaroslav O. and Sederberg, Per B. and Olivetti, Emanuele and Fr\"{u}nd, Ingo and Rieger, Jochem and Herrmann, Christoph S. and Haxby, James V. and Hanson, Stephen J. and Pollmann, Stefan}, citeulike-article-id = {3923957}, citeulike-linkout-0 = {http://frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/003.2009/}, citeulike-linkout-1 = {http://dx.doi.org/10.3389/neuro.11.003.2009}, doi = {10.3389/neuro.11.003.2009}, editor = {K\"{o}tter, Rolf and Bednar, James A. and Davison, Andrew and Diesmann, Markus and Gewaltig, Marc-Oliver and Hines, Michael and Muller, Eilif}, journal = {Fontiers in Neuroinformatics}, keywords = {mvpa, pbcc2009icdm}, number = {3}, posted-at = {2009-01-22 13:46:00}, priority = {2}, title = {PyMVPA: a unifying approach to the analysis of neuroscientific data}, url = {http://dx.doi.org/10.3389/neuro.11.003.2009}, volume = {3}, year = {2009} }

TY - JOUR ID - pymvpa2009 L3 - citeulike-article-id:3923957 N2 - The Python programming language is steadily increasing in popularity as the language of choice for scientific 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 fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit 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 machine learning methods to neuroscience research questions attracted a lot of attention from researchers from both fields, 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 IS - 3 JF - Fontiers in Neuroinformatics TI - PyMVPA: a unifying approach to the analysis of neuroscientific data VL - 3 KW - mvpa KW - pbcc2009icdm AU - Hanke, Michael AU - Halchenko, Yaroslav AU - Sederberg, Per AU - Olivetti, Emanuele AU - Fründ, Ingo AU - Rieger, Jochem AU - Herrmann, Christoph AU - Haxby, James AU - Hanson, Stephen AU - Pollmann, Stefan A2 - Kötter, Rolf A2 - Bednar, James A2 - Davison, Andrew A2 - Diesmann, Markus A2 - Gewaltig, Marc-Oliver A2 - Hines, Michael A2 - Muller, Eilif PY - 2009/// UR - http://dx.doi.org/10.3389/neuro.11.003.2009 ER -