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Sensory processing produces hierarchical representations, which according to the semantic compression hypothesis, extract increasingly behaviorally relevant quantities from raw stimuli. Predictions of neural activity in hierarchical systems are most often made in supervised deterministic models, while probabilistic generative models provide a more complete unifying view of sensory perception. Whether unsupervised generative models trained on naturalistic stimuli give rise to representational layers of semantically interpretable quantities is yet unresolved, as is whether such representations can predict properties of neural responses in early vision. We use hierarchical variational autoencoders to learn a representation with graded compression levels from natural images, which exhibits variance according to perceptually relevant texture categories. We predict measures of neural response statistics by assessing the posterior distribution of latent variables in response to texture stimuli. Experimental results show that linearly decodable information about stimulus identity is lost in the secondary visual cortex while information is gained about texture type, which behavior is reproduced by the representational layers of our model. Deep generative models fitted to natural stimuli open up opportunities to investigate perceptual top-down effects, uncertainty representations along the visual hierarchy, and contributions of recognition and generative components to neural responses.

Hierarchical semantic compression predicts texture selectivity in early vision