Stimulus domain transfer in recurrent models for large scale cortical population prediction on video (Code)
Code to reproduce results of the NIPS 2018 paper: "Stimulus domain transfer in recurrent models for large scale cortical population prediction on video".
nvidia-docker(version 1), and
nvidia-docker-compose. You can easily run it with
nvidia-dockerversion 2. In that case have a look at the
nvidia-docker-compose.yml.jinjaand extract the options for the
notebookservice to start the container.
- GIN along with
git-annexto download the data.
The data shared with this code is licensed under a This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This license requires that you contact us before you use the data in your own research. In particular, this means that you have to ask for permission if you intend to publish a new analysis performed with this data (no derivative works-clause).
Go to a folder of you choice and type the following commands in a shell of your choice:
git clone https://github.com/sinzlab/Sinz2018_NIPS.git cd Sinz2018_NIPS # get the data gin get cajal/Sinz2018_NIPS_data # might take a while; fast internet recommended # create a file with DB credentials echo "DJ_HOST=archive.datajoint.io" >> .env echo "DJ_USER=nips" >> .env echo "DJ_PASS=nips-submission" >> .env # create docker container (possibly you need sudo) nvidia-docker-compose -t docker-compose.yml.jinja build notebook0
Then you can start the container via
nvidia-docker-compose -t docker-compose.yml.jinja up notebook0
Now you should be able to access the jupyter notebooks via
YOURCOMPUTER:2018 in the browser.
Custom Database Server
You can also run the notebook with your own database server. In that case you need to insert the content of
Sinz2018_NIPS_data/dbdump/nips2018.sql into your own database and change the
DJ_HOST, DJ_USER, DJ_PASS parameters accordingly.