All right, there were somethings that I misunderstood about GANs when I wrote this post. First of all, GANs are for unsupervised learning (that is learning on unlabeled data). That means GANs are applicable when we aren't really sure what our latent space is. So, typically, the input for the generative network would be a noise vector. Second of all, my discriminative network would have no way of knowing what stimuli the fMRI data (whether real or counterfeit) corresponds to. So it could only find out if the generative network delivers plausible fMRI data in general and not for a specific stimuli. Another problem is that even if we have a trained generative network for generating fMRI data, it is unclear how this helps us with decoding. As far as I know, there's no way to just "reverse" a network like that. I need to look into this more, but for right now, I don't see how GANs are useful for our problem. Please let me know your thoughts. I'm debating with myself whether to take this post down...