2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-2B.44
Session: Poster Session 2B
Location: H Fläche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Hierarchical semantic compression predicts texture selectivity in early vision
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1092-0
Authors: Mihály Bányai, Dávid G. Nagy, Gergő Orbán, MTA Wigner RCP, Hungary
Abstract: 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.