## Frontmatter | | | | --- | --- | | Authors | [[Thirza Dado]], [[Paolo Papale]], [[Antonio Lozano]], [[Lynn Le]], [[Feng Wang]], [[Marcel van Gerven]], [[Pieter Roelfsema]], [[Yağmur Güçlütürk]], [[Umut Güçlü]] | | Date | 2024/05 | | Source | [[PLOS Computational Biology]] | | URL | https://doi.org/10.1371/journal.pcbi.1012058 | | Citation | Dado, T., Papale, P., Lozano, A., Le, L., Wang, F., van Gerven, M., Roelfsema, P., Güçlütürk, Y., & Güçlü, U. (2024). [[Brain2GAN - Feature-disentangled neural encoding and decoding of visual perception in the primate brain]]. _PLOS Computational Biology_. [[URL](https://doi.org/10.1371/journal.pcbi.1012058)]. #Journal | ## Abstract A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. 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. ## PDF ![[Brain2GAN - Feature-disentangled neural encoding and decoding of visual perception in the primate brain.pdf]]