CNN Model for System Identification of Neural Types
This is a code repository to the paper (cite as):
Klindt, D., Ecker, A., Euler, T. & Bethge, M. (2017). Neural system identification for large populations separating “what” and “where”. In Advances in Neural Information Processing Systems.
- Tensorflow (with GPU support, see https://www.tensorflow.org/install/install_linux)
To reproduce the figures from the paper (see above) open the corresponding notebooks:
For Figure 3 and 4 go to
and execute the cells with further instructions provided in the comments.
For Figure 5b-d execute
as well as
where 'Batty' is the CNN model with fixed location mask and 'McInt' the CNN model with fully connected readout.
For Figure 5e execute
For Table 1 see
The results of the grid search are stored in a database using the data management toolkit DataJoint. If you intend to actually run the code yourself there will be additional work needed setting up a MySQL server and installing DataJoint. We're happy to help with that.
If your goal is to just use the code to fit a model to your own data, consult
standalone.py for a working example.
If you want to check the code we used:
convnet.py defines the neural networks and does
the heavy lifting;
database.py contains the database classes and exact parameter settings
that we used (
Fit._make_tuples() is a good starting point).