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Software for neuroscience computing

The CCN supports the development of software systems for neuroscience computing under a research program directed by Yaroslav Halchenko. Dr. Halchenko (yaroslav.o.halchenko@dartmouth.edu) also provides consultation on the use of these systems and their application to cognitive neuroscience research.


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NeuroDebian is a sustainable and transparent software platform (free and open source) for neuroscience computing.  It is integrated with the Debian operating system and therefore compatible with a large variety of computing platforms natively or through virtualization.  It includes software packages for brain image analysis (e.g. AFNI, FSL), neural decoding (PyMVPA), computer-controlled cognitive experiments (e.g. PsychoPy, Psychtoolbox-3), electrophysiology (e.g. Neo, Stimfit), high-throughput (e.g. Condor) and generic (e.g. Pandas, statsmodels) computing and many more.  It affords automatic updating of software and efficient version tracking, among other features. NeuroDebian is currently used at over 500 universities and neuroscience research centers on six continents with over 10,000 unique visitors of its website each month.  It is also a backbone of many continuous integration (e.g. on travis-ci.org) and cloud (e.g. NITRC-CE) deployments.

 neurodebian world map of users

World map of Neurodebian users
link to neuro.debian.org


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PyMVPA is a Python-based software platform for neural decoding using multivariate pattern analysis.  It affords both volume- and surface-based analyses using a wide variety of supervised and unsupervised machine learning methods, representational similarity analyses, searchlight analyses, hyperalignment of representational spaces, and model-based decoding and encoding.  The software also can be used for neural data other than fMRI, including analysis of MEG and EEG data through spatio-temporo-frequency band searchlights and cross-modal EEG to fMRI trans-fusion.  It also has been used for analyses on data unrelated to neuroscience, demonstrating its general utility.  PyMVPA also serves as a repository for sample data sets (e.g.  Haxby et al. 2001; Haxby et al.  2011; Connolly et al. 2012) that can be used for education, development of new algorithms, or new analyses and independent research reports.

link to www.pymvpa.org


Software team

Yaroslav and MichaelYaroslav Halchenko, Center for Cognitive Neuroscience, Dartmouth

Michael Hanke, University of Magdeburg, Germany