| Paper: | PS-1B.6 | ||
| Session: | Poster Session 1B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | A deep generative model explaining tuning properties of monkey face processing patches | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
||
| DOI: | https://doi.org/10.32470/CCN.2019.1026-0 | ||
| Authors: | Haruo Hosoya, ATR International, Japan | ||
| Abstract: | Recent monkey studies have revealed a face processing network in the IT cortex that consists of multiple face-selective patches and forms a putative functional hierarchy. Although a number of computational models accounting for this have been proposed, they have been mostly feedforward, ignoring the reciprocal nature of the visual system. Here, we present a two-layer deep generative model based on variational autoencoder (VAE), which provides a Bayesian probabilistic framework with explicit feedforward and feedback processing. While the lower layer of our model uses a standard VAE, the upper layer uses our recently developed algorithm called group-based VAE, which is capable of learning invariant representations from inputs with grouping information. After training with multi-view face images, the upper layer encoded view-invariant facial identities while the lower layer showed facial feature tuning, both in a way quantitatively similar to the observations in patches AM and ML, respectively, as shown in Freiwald and Tsao (2010) and Freiwald et al. (2009). Taken together, we have found a novel deep generative model that might have some computational relevance with the monkey face processing system. | ||