Deep convolutional models improve predictions of macaque V1 responses to natural images (Code)

Code for Cadena, S. A, et al. (2019). Deep convolutional models improve predictions of macaque V1 responses to natural images. Plos Computational Biology. Link to paper

Data License

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).

Setup

To run this code you need the following:

• Python3
• Tensorflow 1.5
• The data is available in a GIN repository. Either download and unzip the contents found here and sore them in the folder Cadena_PlosCB19_data/ or clone the contents of the GIN repository in the same folder.
• Download the checkpoint weights of the normalized VGG-19 network here (80MB) and store them in the vgg_weights/ folder

Citation

If you find our code useful please cite us in your work:

@article{cadena2019deep,
  title={Deep convolutional models improve predictions of macaque V1 responses to natural images},
  author={Cadena, Santiago A and Denfield, George H and Walker, Edgar Y and Gatys, Leon A and Tolias, Andreas S and Bethge, Matthias and Ecker, Alexander S},
  journal={Plos Computational Biology},
  year={2019}
}