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Related Experiment Video

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The Gateway to the Brain: Dissecting the Primate Eye

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

Thirza Dado¹, Paolo Papale², Antonio Lozano²

¹Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.

Plos Computational Biology

|May 6, 2024

Summary

Feature-disentangled representations from generative adversarial networks (GANs) better explain neural activity in the visual cortex than other models. This finding advances understanding of neural coding for visual perception.

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Last Updated: Jun 27, 2025

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Area of Science:

Neuroscience
Computer Vision
Artificial Intelligence

Background:

Understanding neural representations of visual perception is a key challenge in neuroscience.
Deep generative models offer novel ways to explore these representations.

Purpose of the Study:

To compare different latent representations from deep generative models for explaining neural activity in the macaque visual cortex.
To investigate the role of feature-disentanglement in neural coding.

Main Methods:

Recorded multi-unit activity (MUA) from macaque visual cortex during passive viewing of faces and natural images.
Analyzed MUA against latent representations from StyleGAN (z and w-latents) and Stable Diffusion (CLIP-latents).
Utilized mass univariate neural encoding and multivariate neural decoding analyses.

Main Results:

Feature-disentangled w-latents from StyleGAN significantly outperformed z-latents and CLIP-latents in explaining neural responses.
w-latent features occupy a higher complexity gradient, indicating relevance to high-level neural activity.
Multivariate decoding of w-latents achieved state-of-the-art spatiotemporal reconstructions of visual perception.

Conclusions:

Feature-disentanglement plays a crucial role in high-level neural representations of visual perception.
This study provides a benchmark for future research in neural coding and deep generative models.