Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain

28 April 2023

Thirza Dado, Paolo Papale, Antonio Lozano, Lynn Le, Feng Wang, Marcel van Gerven, Pieter Roelfsema, Yağmur Güçlütürk & Umut Güçlü


A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, we analyzed the relationship between multi-unit activity of macaque visual cortex and latent representations of state-of-the-art deep generative models, including feature-disentangled representations of generative adversarial networks (ie, w-latents of StyleGAN) and languagecontrastive representations of latent diffusion networks (ie, CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled representations explain increasingly more variance than the alternative representations over the ventral stream. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

Preprint / GitHub repository / Poster

b2g-xl_image Test set stimulus (top) reconstruction (bottom) from brain activity.