A set of tools that make it easier to use “deep” neural networks for data analysis in research
Jacobs M. Williams1,2, Karl M. Kuntzelman3, Matthew R. Johnson3,4
Stable release, MIT license
The intent is to provide a set of tools that make it easier to use “deep” neural networks for data analysis in research — although it also has support for PyMVPA, in order to facilitate more conventional multivariate pattern analyses (MVPA) and make it easier to compare “deep learning” approaches to conventional MVPA. The primary intended use case is for analysis of neuroimaging datasets (e.g., fMRI, EEG, MEG), although there’s nothing to stop people from using it for all kinds of other classification tasks and data types. This is a Python toolbox.
Kuntzelman, K. M., Williams, J. M., Lim, P. C., Samal, A., Rao, P. K., & Johnson, M. R. (2021). Deep-learning-based multivariate pattern analysis (dMVPA): A tutorial and a toolbox. Frontiers in human neuroscience, 15, 89.
Cole, Z. J., Kuntzelman, K. M., Dodd, M. D., & Johnson, M. R. (2019). I see what you did there: Deep learning algorithms can classify cognitive tasks from images of eye tracking data. Journal of Vision, 19(10), 306b-306b.
1Office of Technology Development and Coordination, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States. 2Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States. 3Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States. 4Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States