Deep retina is a project to test to what degree artificial neural networks can predict retinal ganglion cell responses to natural stimuli.

Please see our NIPS paper for more details.

Note that deepretina requires python 3.5 or higher.

To install the dependencies, run pip install -r requirements.txt. If you run the runme.py script, it will print out a brief overview of the different modules in deepretina (assuming it is able to import everything correctly).

The following is a high level description of the different modules:

• core.py: contains a function for training a deepretina model
• models.py: contains functions for building different kinds of deepretina models (convnets, RNNs, etc.)
• experiments.py: class structure for loading experimental data
• io.py: contains tools for saving model training progress and parameters to disk

A more comprehensive tutorial is in the works.

Lane McIntosh (lmcintosh@stanford.edu) and Niru Maheswaranathan (nirum@stanford.edu)