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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.
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.
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