Thirza Dado

This paper introduces PAM, a predictive attention mechanism for neural decoding of visual perception, achieving state-of-the-art reconstructions and providing interpretative insights through attention weights and values.

This paper shows that feature-disentangled GAN latents better predict neural responses to visual stimuli than other representations, and can reconstruct visual perception with high fidelity.

This paper reconstructs faces from fMRI data using GAN latent spaces, achieving accurate perception reconstructions by aligning neural responses with generator features.