seelikat / ganrecon

Code for "Generative adversarial networks for reconstructing natural images from brain activity".

Reconstruct handwritten characters from brains using GANs

Example code for the paper "Generative adversarial networks for reconstructing natural images from brain activity".

Method for reconstructing images from brain activity with GANs. You need a GAN that is trained for reproducing the target distribution (images that look like your stimuli) and a differentiable method for doing perceptual feature matching (here: layer activations of a convolutional neural network).

The method uses linear regression implemented as a neural network to predict the latent space z. Losses are calculated in image space and backpropagated through the loss terms and the GAN over z to the weights of the linear regression layer.

Usage notes

... for the handwritten characters example:

1. Run train_linear_model.py, preferably on a GPU. This will produce ./recon/finalZ.mat which contains z predictions on your validation set.

2. Run reconstruct_from_z.py to generate a PNG with reconstructions of the validation data in ./recon/recons.png.

... for your own data:

1. Train a GAN for your stimulus domain (e.g. natural grayscale images of size [64 64]). During training z should be drawn from a uniform distribution in [-1 1] and normalized (see sample_z() in model_dcgan_G.py).

2. Train a differentiable network for feature matching. The training code for the AlexNet used for handwritten digits can be found in ./featurematching/train_featurematching_handwritten.py.

3. Adapt some parameters in args.py and train_linear_model.py (and hopefully little of the rest). Fine-tune the weights for the loss terms on an isolated data set.

4. You should be able to just run train_linear_model.py then.

Requirements

• Anaconda Python 2.7 version

• chainer version 1.24 (install via: pip install chainer==1.24 --no-cache-dir -vvvv)

• A GPU for training the feature matching network

Usage conditions

If you publish using this code or use it in any other way, please cite:

Seeliger, K., Güçlü, U., Ambrogioni, L., Güçlütürk, Y., & van Gerven, M. A. J. (2018). Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage.

Please notify the corresponding author in addition.