Welcome, new Internet friend. We're glad that you have found your way to the homepage of our little ol' toolbox. We call it the "Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education" package, or DeLINEATE for short. The intent is to provide a set of tools that make it easier to use "deep" neural networks for data analysis in research -- although we also have 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. And 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. We should probably say something more than that. The deep learning stuff is based on a Keras backend, for one thing.
So, full disclosure. We're pushing up against a deadline to get this toolbox into public release. While the toolbox itself is in pretty decent shape (though there are lots of tasks yet to do in order to make it super-great and not merely functional), it's likely going to take a bit longer than we have to get the rest of this website and documentation written. Thus, right now this website is kind of a promise of things-to-be-soon. Some of the content *might* be more placeholder than actual useful information, including some of the sections further down on this very page.
If you happen to stumble upon this and want to try it out before we update all of this text with more useful content, just head to the Download and/or Contact/Contribute pages linked at the top, where you can find a release version to download and a link to the code repository on Bitbucket. Actually using the toolbox might take a little help from the dev team until the documentation is more fleshed out, so please feel free to get in touch and we can give you a hand. Your questions will also help inform which parts of the docs and website need fleshing out most.
We have one. It is the best. I showed it to my Mom and she said it was pretty and hung it on the fridge.
Good luck, sucker.
We don't need... roads.