Disentangled deep generative models reveal coding principles of the human face processing network
The paper introduces disentangled deep generative models to interpret human face processing brain activity and finds interpretable latent dimensions that align with fMRI data and a gradient of face feature representation.
PLoS Computational Biology · Feb 26, 2024Peer-reviewedNeuroscience